backend/api/services/agent_orchestrator.py

# =============================================================
# File: backend/api/services/agent_orchestrator.py
# =============================================================
"""
Agent Orchestrator (integrated with enterprise RedFlagDetector)

Place at: backend/api/services/agent_orchestrator.py
"""

from __future__ import annotations

import asyncio
import json
import os
import re
from typing import List, Dict, Any, Optional
import logging

from ..models.agent import AgentRequest, AgentDecision, AgentResponse
from ..models.redflag import RedFlagMatch
from .redflag_detector import RedFlagDetector
from .intent_classifier import IntentClassifier
from .tool_selector import ToolSelector
from .llm_client import LLMClient
from ..mcp_clients.mcp_client import MCPClient
from .tool_scoring import ToolScoringService
from ..storage.analytics_store import AnalyticsStore
from .result_merger import merge_parallel_results, format_merged_context_for_prompt
from .tool_metadata import validate_tool_output, get_tool_schema
from .query_cache import get_cache
from .query_expander import QueryExpander
from .context_engineer import ContextEngineer
import time

logger = logging.getLogger(__name__)

from dotenv import load_dotenv

load_dotenv()

class AgentOrchestrator:

    def __init__(self, rag_mcp_url: str, web_mcp_url: str, admin_mcp_url: str, llm_backend: str = "ollama"):
        self.mcp = MCPClient(rag_mcp_url, web_mcp_url, admin_mcp_url)
        self.llm = LLMClient(backend=llm_backend, url=os.getenv("OLLAMA_URL"), api_key=os.getenv("GROQ_API_KEY"), model=os.getenv("OLLAMA_MODEL"))
        
        # pass admin_mcp_url so detector can call back
        self.redflag = RedFlagDetector(
            supabase_url=os.getenv("SUPABASE_URL"),
            supabase_key=os.getenv("SUPABASE_SERVICE_KEY"),
            admin_mcp_url=admin_mcp_url
        )
        
        self.intent = IntentClassifier(llm_client=self.llm)
        self.selector = ToolSelector(llm_client=self.llm)
        self.tool_scorer = ToolScoringService()
        self.query_expander = QueryExpander(llm_client=self.llm)
        self.cache = get_cache()
        self.context_engineer = ContextEngineer(llm_client=self.llm)

        self._analytics: Optional[AnalyticsStore] = None
        self._analytics_disabled = os.getenv("ANALYTICS_DISABLED", "").lower() in {"1", "true", "yes"}
        self._analytics_failed = False
        self._log_analytics_backend_once()

    def _log_analytics_backend_once(self) -> None:
        if getattr(AgentOrchestrator, "_analytics_backend_logged", False):
            return

        if self._analytics_disabled:
            print("⚠️  AgentOrchestrator Analytics: Disabled via ANALYTICS_DISABLED")
        else:
            store = self._get_analytics()
            if store is None:
                print("⚠️  AgentOrchestrator Analytics: Disabled (Supabase not configured)")
            elif store.use_supabase:
                print("✅ AgentOrchestrator Analytics: Using Supabase backend")
            else:
                print("⚠️  AgentOrchestrator Analytics: Using fallback backend")

        AgentOrchestrator._analytics_backend_logged = True

    def _get_analytics(self) -> Optional[AnalyticsStore]:
        if self._analytics_disabled or self._analytics_failed:
            return None

        if self._analytics is not None:
            return self._analytics

        try:
            self._analytics = AnalyticsStore()
        except RuntimeError as exc:
            logger.warning("AgentOrchestrator analytics disabled: %s", exc)
            self._analytics_failed = True
            self._analytics = None
        except Exception as exc:  # pragma: no cover - unexpected initialization failures
            logger.debug("AgentOrchestrator analytics unexpected init failure: %s", exc)
            self._analytics_failed = True
            self._analytics = None

        return self._analytics

    def _analytics_log_tool_usage(self, **kwargs: Any) -> None:
        analytics = self._get_analytics()
        if not analytics:
            return
        try:
            analytics.log_tool_usage(**kwargs)
        except Exception as exc:  # pragma: no cover - analytics failures should not break flow
            logger.debug("AgentOrchestrator tool analytics failed: %s", exc)

    def _analytics_log_agent_query(self, **kwargs: Any) -> None:
        analytics = self._get_analytics()
        if not analytics:
            return
        try:
            analytics.log_agent_query(**kwargs)
        except Exception as exc:  # pragma: no cover
            logger.debug("AgentOrchestrator agent query analytics failed: %s", exc)

    def _analytics_log_rag_search(self, **kwargs: Any) -> None:
        analytics = self._get_analytics()
        if not analytics:
            return
        try:
            analytics.log_rag_search(**kwargs)
        except Exception as exc:  # pragma: no cover
            logger.debug("AgentOrchestrator RAG analytics failed: %s", exc)

    def _analytics_log_redflag_violation(self, **kwargs: Any) -> None:
        analytics = self._get_analytics()
        if not analytics:
            return
        try:
            analytics.log_redflag_violation(**kwargs)
        except Exception as exc:  # pragma: no cover
            logger.debug("AgentOrchestrator redflag analytics failed: %s", exc)
    
    def _cache_response(self, req: AgentRequest, response: AgentResponse, skip_cache: bool = False):
        """Cache a response if appropriate."""
        if skip_cache or req.message.startswith("admin:") or len(req.message) < 3:
            return
        try:
            self.cache.set(req.message, req.tenant_id, {
                "text": response.text,
                "decision": response.decision.dict() if response.decision else None,
                "tool_traces": response.tool_traces,
                "reasoning_trace": response.reasoning_trace
            })
        except Exception as e:
            logger.debug(f"Failed to cache response: {e}")

    async def handle(self, req: AgentRequest) -> AgentResponse:
        start_time = time.time()
        reasoning_trace: List[Dict[str, Any]] = []
        reasoning_trace.append({
            "step": "request_received",
            "tenant_id": req.tenant_id,
            "user_id": req.user_id,
            "message_preview": req.message[:120]
        })
        
        # Context Engineering: Write to scratchpad
        self.context_engineer.write_to_scratchpad(
            f"User query: {req.message[:200]}",
            category="user_query"
        )
        
        # Check cache first (skip for admin queries and rule checks)
        cached_response = self.cache.get(req.message, req.tenant_id)
        if cached_response:
            reasoning_trace.append({
                "step": "cache_hit",
                "cached": True
            })
            return AgentResponse(
                text=cached_response.get("text", ""),
                decision=cached_response.get("decision"),
                tool_traces=cached_response.get("tool_traces", []),
                reasoning_trace=reasoning_trace + cached_response.get("reasoning_trace", [])
            )

        # 1) FIRST: Check admin rules - if any rule matches, respond according to rule
        matches: List[RedFlagMatch] = await self.redflag.check(req.tenant_id, req.message)
        reasoning_trace.append({
            "step": "admin_rules_check",
            "match_count": len(matches),
            "matches": [m.__dict__ for m in matches]
        })

        if matches:
            # Log all rule matches
            for match in matches:
                self._analytics_log_redflag_violation(
                    tenant_id=req.tenant_id,
                    rule_id=match.rule_id,
                    rule_pattern=match.pattern,
                    severity=match.severity,
                    matched_text=match.matched_text,
                    confidence=match.confidence,
                    message_preview=req.message[:200],
                    user_id=req.user_id
                )
            
            # Categorize rules: brief response rules vs blocking rules
            brief_response_rules = []
            blocking_rules = []
            
            for match in matches:
                rule_text = (match.description or match.pattern or "").lower()
                is_brief_rule = (
                    match.severity == "low" and (
                        "greeting" in rule_text or 
                        "brief" in rule_text or 
                        "simple response" in rule_text or
                        "keep.*response.*brief" in rule_text or
                        "do not.*verbose" in rule_text or
                        "respond.*briefly" in rule_text
                    )
                )
                
                if is_brief_rule:
                    brief_response_rules.append(match)
                else:
                    blocking_rules.append(match)
            
            # Handle brief response rules (greetings, etc.) - return immediately
            if brief_response_rules and not blocking_rules:
                # Return brief response without proceeding to normal flow
                brief_responses = [
                    "Hello! How can I help you today?",
                    "Hi there! What can I assist you with?",
                    "Hello! I'm here to help. What do you need?",
                    "Hi! How can I assist you?"
                ]
                import random
                brief_response = random.choice(brief_responses)
                
                reasoning_trace.append({
                    "step": "brief_response_rule_matched",
                    "action": "brief_response",
                    "matched_rules": [m.rule_id for m in brief_response_rules],
                    "message": "Brief response rule matched, returning brief response (skipping normal flow)"
                })
                
                total_latency_ms = int((time.time() - start_time) * 1000)
                self._analytics_log_agent_query(
                    tenant_id=req.tenant_id,
                    message_preview=req.message[:200],
                    intent="greeting",
                    tools_used=[],
                    total_tokens=len(brief_response) // 4,
                    total_latency_ms=total_latency_ms,
                    success=True,
                    user_id=req.user_id
                )
                
                return AgentResponse(
                    text=brief_response,
                    decision=AgentDecision(action="respond", tool=None, tool_input=None, reason="brief_response_rule"),
                    reasoning_trace=reasoning_trace
                )
            
            # Handle blocking rules (security, compliance, etc.) - block and return immediately
            if blocking_rules:
                # Notify admin asynchronously
                try:
                    await self.redflag.notify_admin(req.tenant_id, blocking_rules, source_payload={"message": req.message, "user_id": req.user_id})
                except Exception:
                    pass

                decision = AgentDecision(
                    action="block",
                    tool="admin",
                    tool_input={"violations": [m.__dict__ for m in blocking_rules]},
                    reason="admin_rule_violation"
                )
                
                # Build detailed prompt for LLM to generate natural red flag response
                violations_details = []
                for i, m in enumerate(blocking_rules, 1):
                    rule_name = m.description or m.pattern or "Policy violation"
                    detail = f"{i}. **{rule_name}** (Severity: {m.severity})"
                    if m.matched_text:
                        detail += f"\n   - Detected phrase: \"{m.matched_text}\""
                    violations_details.append(detail)
            
            llm_prompt = f"""A user made the following request: "{req.message}"

However, this request violates company policies. The following policy violations were detected:

{chr(10).join(violations_details)}

Your task: Write a clear, professional, and empathetic response to inform the user that:
1. Their request cannot be processed due to policy violations
2. Which specific policy was violated (mention it naturally)
3. The incident has been logged for security review
4. They should contact an administrator if they need assistance or believe this is an error

Write a natural, conversational response (2-4 sentences) that feels helpful rather than robotic. Be professional but understanding.

Response:"""
            
            # Generate LLM response for red flag
            try:
                llm_response = await self.llm.simple_call(llm_prompt, temperature=min(req.temperature + 0.2, 0.7))  # Slightly higher temp for more natural response
                llm_response = llm_response.strip()
                # Add warning emoji if not present
                if not llm_response.startswith("⚠️") and not llm_response.startswith("🚨"):
                    llm_response = f"⚠️ {llm_response}"
            except Exception as e:
                # Fallback to a simple message if LLM fails
                summary = "; ".join(
                    f"{m.description or m.pattern}"
                    for m in matches
                )
                llm_response = f"⚠️ I'm unable to process your request because it violates our company policy: {summary}. This incident has been logged. Please contact your administrator if you need assistance."
            
            total_latency_ms = int((time.time() - start_time) * 1000)
            
            # Log LLM usage for red flag response
            estimated_tokens = len(llm_response) // 4 + len(llm_prompt) // 4
            self._analytics_log_tool_usage(
                tenant_id=req.tenant_id,
                tool_name="llm",
                latency_ms=total_latency_ms,
                tokens_used=estimated_tokens,
                success=True,
                user_id=req.user_id
            )
            
            self._analytics_log_agent_query(
                tenant_id=req.tenant_id,
                message_preview=req.message[:200],
                intent="admin",
                tools_used=["admin", "llm"],
                total_tokens=estimated_tokens,
                total_latency_ms=total_latency_ms,
                success=False,
                user_id=req.user_id
            )
            
            response = AgentResponse(
                text=llm_response,
                decision=decision,
                tool_traces=[{"redflags": [m.__dict__ for m in blocking_rules]}],
                reasoning_trace=reasoning_trace
            )
            # Don't cache admin rule violations
            return response
        
        # 2) ONLY IF NO RULES MATCHED: Proceed with normal flow (intent classification, RAG, etc.)
        # 2.1) Optional: Try to rewrite message if it might violate rules (preventive self-correction)
        # Note: This is a lighter check - we already blocked above if rules matched
        # This is for edge cases where we want to proactively improve the message
        safe_message = req.message  # Default to original
        
        intent = await self.intent.classify(req.message)
        reasoning_trace.append({
            "step": "intent_detection",
            "intent": intent
        })

        # 2.5) Pre-fetch RAG results if available (for tool selector context)
        # BUT: Skip RAG pre-fetch for news/current events queries (they need web search, not RAG)
        rag_prefetch = None
        rag_results = []
        
        # Detect news queries early to skip RAG pre-fetch
        # Make detection more aggressive - check for "news" keyword first
        msg_lower = req.message.lower().strip()
        
        # Primary detection: if "news" is in the message, it's almost certainly a news query
        has_news_keyword = "news" in msg_lower
        
        # Exclude common non-news phrases that contain "news" but aren't news queries
        non_news_phrases = [
            "what is", "what's", "explain", "tell me about", "define",
            "how does", "how do", "what are", "what does", "what can"
        ]
        is_general_question = any(phrase in msg_lower for phrase in non_news_phrases)
        
        freshness_keywords = ["latest", "today", "current", "recent", 
                             "now", "updates", "breaking", "trending", "happening",
                             "what's new", "what is new", "what happened"]
        news_patterns = [
            r"latest news", r"current news", r"today's news", r"breaking news",
            r"news about", r"news on", r"news of", r"what's happening",
            r"what happened", r"recent news", r"news update"
        ]
        
        # If "news" keyword is present AND it's not a general question, it's a news query
        # Otherwise check for other freshness indicators
        is_news_query = (has_news_keyword and not is_general_question) or \
                        (any(k in msg_lower for k in freshness_keywords) and not is_general_question) or \
                        any(re.search(p, msg_lower) for p in news_patterns)
        
        # LLM-based detection for edge cases (if keyword-based detection is uncertain)
        # Only use LLM if it's a short query and we're uncertain
        if not is_news_query and len(msg_lower.split()) <= 5 and not is_general_question:
            # For short queries, use LLM to check if it's a news query
            try:
                llm_check_prompt = f"""Is the following query asking for current news or recent events? Answer only "yes" or "no".

Query: "{req.message}"

Answer:"""
                llm_response = await self.llm.simple_call(llm_check_prompt, temperature=0.0)
                if "yes" in llm_response.lower():
                    is_news_query = True
                    reasoning_trace.append({
                        "step": "news_query_detection_llm",
                        "detected": True,
                        "llm_confirmed": True
                    })
            except Exception as e:
                logger.debug(f"LLM news detection failed: {e}")
        
        # Log detection for debugging
        if is_news_query:
            reasoning_trace.append({
                "step": "news_query_detection",
                "detected": True,
                "message": req.message,
                "has_news_keyword": has_news_keyword,
                "matched_keywords": [k for k in freshness_keywords if k in msg_lower]
            })
        
        # Only pre-fetch RAG if it's NOT a news query
        if not is_news_query:
            try:
                # Try to pre-fetch RAG to help tool selector make better decisions
                rag_start = time.time()
                rag_prefetch = await self.mcp.call_rag(req.tenant_id, req.message)
                rag_latency_ms = int((time.time() - rag_start) * 1000)
            
                if isinstance(rag_prefetch, dict):
                    rag_results = rag_prefetch.get("results") or rag_prefetch.get("hits") or []
                    # Log RAG search event
                    hits_count = len(rag_results)
                    avg_score = None
                    top_score = None
                    if rag_results:
                        scores = [h.get("score", 0.0) for h in rag_results if isinstance(h, dict) and "score" in h]
                        if scores:
                            avg_score = sum(scores) / len(scores)
                            top_score = max(scores)
                    self._analytics_log_rag_search(
                        tenant_id=req.tenant_id,
                        query=req.message[:500],
                        hits_count=hits_count,
                        avg_score=avg_score,
                        top_score=top_score,
                        latency_ms=rag_latency_ms
                    )
                    # Log tool usage
                    self._analytics_log_tool_usage(
                        tenant_id=req.tenant_id,
                        tool_name="rag",
                        latency_ms=rag_latency_ms,
                        success=True,
                        user_id=req.user_id
                    )
                reasoning_trace.append({
                    "step": "rag_prefetch",
                    "status": "ok",
                    "hit_count": len(rag_results),
                    "latency_ms": rag_latency_ms
                })
            except Exception as pref_err:
                # If RAG fails, continue without it
                rag_latency_ms = 0  # 0 for failed
                self._analytics_log_tool_usage(
                    tenant_id=req.tenant_id,
                    tool_name="rag",
                    latency_ms=rag_latency_ms,
                    success=False,
                    error_message=str(pref_err)[:200],
                    user_id=req.user_id
                )
                reasoning_trace.append({
                    "step": "rag_prefetch",
                    "status": "error",
                    "error": str(pref_err)
                })
                rag_prefetch = None
        else:
            # News query detected - skip RAG pre-fetch
            reasoning_trace.append({
                "step": "rag_prefetch",
                "status": "skipped",
                "reason": "news_query_detected"
            })

        tool_scores = self.tool_scorer.score(req.message, intent, rag_results)
        reasoning_trace.append({
            "step": "tool_scoring",
            "scores": tool_scores
        })

        # 3) Tool selection (hybrid) - pass RAG results, memory, and admin violations in context
        # Context Engineering: Compress conversation history if too long (Anthropic's compaction)
        # Use tool result clearing first (safest), then full compaction if needed
        if req.conversation_history and len(req.conversation_history) > 10:
            # Check token usage
            total_chars = sum(len(str(m.get("content", ""))) for m in req.conversation_history)
            estimated_tokens = total_chars // 4
            
            # Compress if approaching context limit (80% threshold)
            if estimated_tokens > 8000:  # ~80% of typical 10k context
                compressed_history = await self.context_engineer.compress_if_needed(
                    req.conversation_history,
                    max_tokens=6000,  # Target 60% after compression
                    use_compaction=True
                )
                req.conversation_history = compressed_history
                reasoning_trace.append({
                    "step": "context_compaction",
                    "original_length": len(req.conversation_history),
                    "compressed_length": len(compressed_history),
                    "compressed": len(compressed_history) < len(req.conversation_history),
                    "strategy": "anthropic_compaction"
                })
        
        # Get recent memory for context-aware routing
        from backend.mcp_server.common.memory import get_recent
        session_id = req.conversation_history[-1].get("session_id") if req.conversation_history else None
        recent_memory = []
        if session_id:
            recent_memory = get_recent(session_id)
            
            # Context Engineering: Select relevant memories
            if recent_memory:
                selected_memories = await self.context_engineer.select_context(
                    req.message,
                    {"memories": recent_memory}
                )
                recent_memory = selected_memories.get("memories", recent_memory)
        
        # Get admin violations if any
        admin_violations = []
        if hasattr(self, 'redflag') and self.redflag:
            # Check if there were any violations detected
            # (This would be set during redflag checking earlier in the flow)
            pass  # Admin violations are checked separately
        
        # FORCE web search for news queries - bypass tool selector entirely
        # Also ensure rag_results is empty for news queries (double-check)
        if is_news_query:
            rag_results = []  # Force empty - no RAG results for news queries
            from ..models.agent import AgentDecision
            # Enhance query for better web search results
            web_query = req.message
            
            # Handle ambiguous short queries like "latest news about Al" or "atest news about Al"
            # Try to expand with common interpretations
            query_words = web_query.lower().split()
            if len(query_words) <= 4:
                # Extract the topic (word after "about" or last word)
                topic = None
                if "about" in query_words:
                    about_idx = query_words.index("about")
                    if about_idx + 1 < len(query_words):
                        topic = query_words[about_idx + 1]
                elif len(query_words) >= 2:
                    # Last word might be the topic
                    topic = query_words[-1]
                
                # If topic is very short (1-2 letters), it's likely ambiguous - expand it
                if topic and len(topic) <= 2:
                    # Common expansions for "Al"
                    if topic == "al":
                        # Try multiple interpretations
                        web_query = f"{' '.join(query_words[:-1])} artificial intelligence AI"
                    elif topic == "ai":
                        web_query = f"{' '.join(query_words[:-1])} artificial intelligence"
                
                # If still short, add "news" keyword if missing
                if "news" not in web_query.lower() and len(web_query.split()) <= 3:
                    web_query = f"{web_query} news latest"
            
            decision = AgentDecision(
                action="call_tool",
                tool="web",
                tool_input={"query": web_query},
                reason=f"news_query_forced_web_search (original: {req.message})"
            )
            reasoning_trace.append({
                "step": "tool_selection",
                "decision": decision.dict(),
                "note": "news_query_bypassed_selector_forced_web",
                "rag_results_forced_empty": True,
                "web_query": web_query
            })
        else:
            ctx = {
                "tenant_id": req.tenant_id,
                "rag_results": rag_results,
                "tool_scores": tool_scores,
                "memory": recent_memory,  # Context-aware routing: recent tool outputs
                "admin_violations": admin_violations  # Context-aware routing: admin rule severity
            }
            decision = await self.selector.select(intent, req.message, ctx)
            reasoning_trace.append({
                "step": "tool_selection",
                "decision": decision.dict(),
                "context_scores": tool_scores
            })

        tool_traces: List[Dict[str, Any]] = []

        # 4) Handle multi-step tool execution
        if decision.action == "multi_step" and decision.tool_input:
            steps = decision.tool_input.get("steps", [])
            if steps:
                return await self._execute_multi_step(
                    req,
                    steps,
                    decision,
                    tool_traces,
                    reasoning_trace,
                    rag_prefetch
                )

        # 5) Execute single tool
        tools_used = []
        total_tokens = 0
        
        if decision.action == "call_tool" and decision.tool:
            try:
                if decision.tool == "rag":
                    # Use autonomous retry with self-correction
                    rag_query = decision.tool_input.get("query") if decision.tool_input else req.message
                    rag_start = time.time()
                    rag_resp = await self.rag_with_repair(
                        query=rag_query,
                        tenant_id=req.tenant_id,
                        original_threshold=0.3,
                        reasoning_trace=reasoning_trace,
                        user_id=req.user_id
                    )
                    rag_latency_ms = int((time.time() - rag_start) * 1000)
                    tools_used.append("rag")
                    
                    # Validate and format RAG output to conform to schema
                    rag_formatted = self._format_tool_output("rag", rag_resp, rag_latency_ms)
                    
                    # Context Engineering: Compress tool output if needed
                    rag_formatted = await self.context_engineer.compressor.compress_tool_output("rag", rag_formatted)
                    
                    tool_traces.append({"tool": "rag", "response": rag_formatted})
                    hits = self._extract_hits(rag_formatted)
                    
                    # Calculate scores for logging
                    hits_count = len(hits)
                    avg_score = rag_formatted.get("avg_score")
                    top_score = rag_formatted.get("top_score")
                    
                    reasoning_trace.append({
                        "step": "tool_execution",
                        "tool": "rag",
                        "hit_count": hits_count,
                        "top_score": top_score,
                        "avg_score": avg_score,
                        "summary": self._summarize_hits(rag_formatted, limit=2)
                    })
                    prompt = self._build_prompt_with_rag(req, rag_formatted)
                    
                    llm_start = time.time()
                    llm_out = await self.llm.simple_call(prompt, temperature=req.temperature)
                    llm_latency_ms = int((time.time() - llm_start) * 1000)
                    tools_used.append("llm")
                    
                    # Estimate tokens (rough: ~4 chars per token)
                    estimated_tokens = len(llm_out) // 4 + len(prompt) // 4
                    total_tokens += estimated_tokens
                    
                    self._analytics_log_tool_usage(
                        tenant_id=req.tenant_id,
                        tool_name="llm",
                        latency_ms=llm_latency_ms,
                        tokens_used=estimated_tokens,
                        success=True,
                        user_id=req.user_id
                    )
                    
                    reasoning_trace.append({
                        "step": "llm_response",
                        "mode": "rag_synthesis",
                        "latency_ms": llm_latency_ms,
                        "estimated_tokens": estimated_tokens
                    })
                    
                    total_latency_ms = int((time.time() - start_time) * 1000)
                    self._analytics_log_agent_query(
                        tenant_id=req.tenant_id,
                        message_preview=req.message[:200],
                        intent=intent,
                        tools_used=tools_used,
                        total_tokens=total_tokens,
                        total_latency_ms=total_latency_ms,
                        success=True,
                        user_id=req.user_id
                    )
                    
                    return AgentResponse(text=llm_out, decision=decision, tool_traces=tool_traces, reasoning_trace=reasoning_trace)

                if decision.tool == "web":
                    # CRITICAL: For news queries, ensure RAG results are NEVER used
                    msg_check_web = req.message.lower()
                    is_news_web = "news" in msg_check_web or any(k in msg_check_web for k in ["latest", "breaking", "current", "recent", "today"])
                    if is_news_web:
                        # Force clear any RAG context - news queries should NEVER use RAG
                        rag_results = []
                        reasoning_trace.append({
                            "step": "web_tool_execution",
                            "note": "news_query_confirmed_rag_results_cleared_before_web_search"
                        })
                    
                    # Use autonomous retry with query rewriting
                    web_query = decision.tool_input.get("query") if decision.tool_input else req.message
                    web_start = time.time()
                    web_resp = await self.web_with_repair(
                        query=web_query,
                        tenant_id=req.tenant_id,
                        reasoning_trace=reasoning_trace,
                        user_id=req.user_id
                    )
                    web_latency_ms = int((time.time() - web_start) * 1000)
                    tools_used.append("web")
                    
                    # Validate and format Web output to conform to schema
                    web_formatted = self._format_tool_output("web", web_resp, web_latency_ms)
                    
                    # Context Engineering: Compress tool output if needed
                    web_formatted = await self.context_engineer.compressor.compress_tool_output("web", web_formatted)
                    
                    tool_traces.append({"tool": "web", "response": web_formatted})
                    hits_count = len(self._extract_hits(web_formatted))
                    
                    reasoning_trace.append({
                        "step": "tool_execution",
                        "tool": "web",
                        "hit_count": hits_count,
                        "summary": self._summarize_hits(web_formatted, limit=2),
                        "is_news_query": is_news_web
                    })
                    
                    # ALWAYS use web prompt builder for web search results
                    # Never use RAG prompt builder, even if web results are empty
                    if hits_count == 0 and is_news_web:
                        # Empty web results for news query - provide helpful guidance
                        prompt = (
                            f"You are an assistant helping tenant {req.tenant_id}.\n\n"
                            f"## User Question\n{req.message}\n\n"
                            f"## Context\n"
                            f"I searched for the latest news about this topic, but didn't find specific recent results in my web search.\n\n"
                            f"## Your Task\n"
                            f"Provide helpful information about what the user might be looking for. "
                            f"If you have general knowledge about the topic, share it. "
                            f"Be honest that I don't have access to the very latest breaking news right now, but provide what context you can. "
                            f"Suggest that the user try:\n"
                            f"- Checking major news websites directly (BBC, CNN, Reuters, etc.)\n"
                            f"- Trying a more specific search query\n"
                            f"- Using a news aggregator service\n\n"
                            f"IMPORTANT: Do NOT say 'There is no mention of X in the provided context' - instead provide helpful general information or suggest where to find current news.\n\n"
                            f"Provide a helpful response now:"
                        )
                    else:
                        # Use web prompt builder (never RAG)
                        prompt = self._build_prompt_with_web(req, web_formatted)
                    
                    llm_start = time.time()
                    llm_out = await self.llm.simple_call(prompt, temperature=req.temperature)
                    llm_latency_ms = int((time.time() - llm_start) * 1000)
                    tools_used.append("llm")
                    
                    estimated_tokens = len(llm_out) // 4 + len(prompt) // 4
                    total_tokens += estimated_tokens
                    
                    self._analytics_log_tool_usage(
                        tenant_id=req.tenant_id,
                        tool_name="llm",
                        latency_ms=llm_latency_ms,
                        tokens_used=estimated_tokens,
                        success=True,
                        user_id=req.user_id
                    )
                    
                    reasoning_trace.append({
                        "step": "llm_response",
                        "mode": "web_synthesis",
                        "latency_ms": llm_latency_ms,
                        "estimated_tokens": estimated_tokens
                    })
                    
                    total_latency_ms = int((time.time() - start_time) * 1000)
                    self._analytics_log_agent_query(
                        tenant_id=req.tenant_id,
                        message_preview=req.message[:200],
                        intent=intent,
                        tools_used=tools_used,
                        total_tokens=total_tokens,
                        total_latency_ms=total_latency_ms,
                        success=True,
                        user_id=req.user_id
                    )
                    
                    return AgentResponse(text=llm_out, decision=decision, tool_traces=tool_traces, reasoning_trace=reasoning_trace)

                if decision.tool == "admin":
                    admin_start = time.time()
                    admin_resp = await self.mcp.call_admin(req.tenant_id, decision.tool_input.get("query") if decision.tool_input else req.message)
                    admin_latency_ms = int((time.time() - admin_start) * 1000)
                    tools_used.append("admin")
                    
                    self._analytics_log_tool_usage(
                        tenant_id=req.tenant_id,
                        tool_name="admin",
                        latency_ms=admin_latency_ms,
                        success=True,
                        user_id=req.user_id
                    )
                    
                    # Validate and format Admin output to conform to schema
                    admin_formatted = self._format_tool_output("admin", admin_resp, admin_latency_ms)
                    tool_traces.append({"tool": "admin", "response": admin_formatted})
                    reasoning_trace.append({
                        "step": "tool_execution",
                        "tool": "admin",
                        "status": "completed",
                        "latency_ms": admin_latency_ms
                    })
                    
                    total_latency_ms = int((time.time() - start_time) * 1000)
                    self._analytics_log_agent_query(
                        tenant_id=req.tenant_id,
                        message_preview=req.message[:200],
                        intent=intent,
                        tools_used=tools_used,
                        total_tokens=0,
                        total_latency_ms=total_latency_ms,
                        success=True,
                        user_id=req.user_id
                    )
                    
                    return AgentResponse(text=json.dumps(admin_resp), decision=decision, tool_traces=tool_traces, reasoning_trace=reasoning_trace)

                if decision.tool == "llm":
                    # Check if this is a news query - if so, force web search instead
                    msg_lower_llm = req.message.lower()
                    freshness_keywords_llm = ["latest", "today", "news", "current", "recent", 
                                             "now", "updates", "breaking", "trending", "happening"]
                    news_patterns_llm = [
                        r"latest news", r"current news", r"today's news", r"breaking news",
                        r"news about", r"news on", r"news of"
                    ]
                    is_news_query_llm = any(k in msg_lower_llm for k in freshness_keywords_llm) or \
                                       any(re.search(p, msg_lower_llm) for p in news_patterns_llm)
                    
                    # Force web search for news queries even if tool selector chose "llm"
                    if is_news_query_llm:
                        try:
                            web_query = req.message
                            if len(web_query.split()) <= 4:
                                if "news" not in msg_lower_llm:
                                    web_query = f"{web_query} news latest"
                            
                            web_start = time.time()
                            web_resp = await self.web_with_repair(
                                query=web_query,
                                tenant_id=req.tenant_id,
                                reasoning_trace=reasoning_trace,
                                user_id=req.user_id
                            )
                            web_latency_ms = int((time.time() - web_start) * 1000)
                            tools_used.append("web")
                            
                            web_formatted = self._format_tool_output("web", web_resp, web_latency_ms)
                            
                            # Context Engineering: Compress tool output if needed
                            web_formatted = await self.context_engineer.compressor.compress_tool_output("web", web_formatted)
                            
                            tool_traces.append({"tool": "web", "response": web_formatted})
                            hits_count = len(self._extract_hits(web_formatted))
                            
                            reasoning_trace.append({
                                "step": "tool_execution",
                                "tool": "web",
                                "hit_count": hits_count,
                                "note": "forced_web_for_news_in_llm_path"
                            })
                            
                            if hits_count == 0:
                                prompt_for_llm = (
                                    f"You are an assistant helping tenant {req.tenant_id}.\n\n"
                                    f"## User Question\n{req.message}\n\n"
                                    f"## Context\n"
                                    f"I attempted to search for the latest news about this topic, but didn't find specific recent results.\n\n"
                                    f"## Your Task\n"
                                    f"Provide helpful information about what the user might be looking for. "
                                    f"If you have general knowledge about the topic, share it. "
                                    f"Be honest that you don't have access to the very latest breaking news, but provide what context you can. "
                                    f"Suggest that the user try checking major news websites directly or using a more specific search query.\n\n"
                                    f"Provide a helpful response now:"
                                )
                            else:
                                prompt_for_llm = self._build_prompt_with_web(req, web_formatted)
                            
                            llm_start = time.time()
                            llm_out = await self.llm.simple_call(prompt_for_llm, temperature=req.temperature)
                            llm_latency_ms = int((time.time() - llm_start) * 1000)
                            tools_used.append("llm")
                            
                            estimated_tokens = len(llm_out) // 4 + len(prompt_for_llm) // 4
                            total_tokens += estimated_tokens
                            
                            self._analytics_log_tool_usage(
                                tenant_id=req.tenant_id,
                                tool_name="llm",
                                latency_ms=llm_latency_ms,
                                tokens_used=estimated_tokens,
                                success=True,
                                user_id=req.user_id
                            )
                            
                            total_latency_ms = int((time.time() - start_time) * 1000)
                            self._analytics_log_agent_query(
                                tenant_id=req.tenant_id,
                                message_preview=req.message[:200],
                                intent=intent,
                                tools_used=tools_used,
                                total_tokens=total_tokens,
                                total_latency_ms=total_latency_ms,
                                success=True,
                                user_id=req.user_id
                            )
                            
                            return AgentResponse(text=llm_out, decision=decision, tool_traces=tool_traces, reasoning_trace=reasoning_trace)
                        except Exception as web_err:
                            reasoning_trace.append({
                                "step": "web_search_forced_failed",
                                "error": str(web_err)[:200]
                            })
                            # Fall through to normal LLM path
                    
                    # If the user is asking who the admin / owner is, try to ground the
                    # answer in tenant-specific RAG before falling back to a generic LLM reply.
                    user_text = req.message.lower()
                    # Normalize whitespace to make matching more robust
                    user_text_normalized = " ".join(user_text.split())
                    admin_phrases = [
                        "who is the admin",
                        "who's the admin",
                        "who is admin",
                        "who is the administrator",
                        "who's the administrator",
                        "who administers this platform",
                        "who administers the platform",
                        "who is the owner",
                        "who's the owner",
                        "who owns this platform",
                        "who owns the platform",
                        "who is the admin of integrachat",
                        "who's the admin of integrachat",
                    ]
                    use_rag_for_admin = any(p in user_text_normalized for p in admin_phrases) or (
                        "admin" in user_text and "who" in user_text
                    )

                    prompt_for_llm = req.message

                    if use_rag_for_admin:
                        try:
                            rag_start = time.time()
                            rag_resp = await self.rag_with_repair(
                                query=req.message,
                                tenant_id=req.tenant_id,
                                original_threshold=0.2,
                                reasoning_trace=reasoning_trace,
                                user_id=req.user_id,
                            )
                            rag_latency_ms = int((time.time() - rag_start) * 1000)
                            tools_used.append("rag")

                            rag_formatted = self._format_tool_output("rag", rag_resp, rag_latency_ms)
                            tool_traces.append({"tool": "rag", "response": rag_formatted})

                            hits = self._extract_hits(rag_formatted)
                            hits_count = len(hits)
                            avg_score = rag_formatted.get("avg_score")
                            top_score = rag_formatted.get("top_score")

                            self._analytics_log_tool_usage(
                                tenant_id=req.tenant_id,
                                tool_name="rag",
                                latency_ms=rag_latency_ms,
                                success=True,
                                user_id=req.user_id,
                            )

                            reasoning_trace.append(
                                {
                                    "step": "tool_execution",
                                    "tool": "rag",
                                    "hit_count": hits_count,
                                    "top_score": top_score,
                                    "avg_score": avg_score,
                                    "summary": self._summarize_hits(rag_formatted, limit=2),
                                    "note": "admin_identity_override",
                                }
                            )

                            # For admin questions, answer directly from RAG and avoid any
                            # generic LLM behaviour. If there is at least one hit, return
                            # that snippet; otherwise return an explicit "don't know".
                            if hits:
                                best = hits[0]
                                admin_text = best.get("text") or best.get("content") or str(best)
                                llm_out = f"According to the tenant knowledge base, {admin_text.strip()}"
                            else:
                                llm_out = "I don't know who administers this platform based on the tenant data."

                            llm_latency_ms = 0
                            estimated_tokens = len(llm_out) // 4 + len(req.message) // 4
                            total_tokens += estimated_tokens

                            self._analytics_log_tool_usage(
                                tenant_id=req.tenant_id,
                                tool_name="llm",
                                latency_ms=llm_latency_ms,
                                tokens_used=estimated_tokens,
                                success=True,
                                user_id=req.user_id,
                            )

                            reasoning_trace.append(
                                {
                                    "step": "llm_response",
                                    "mode": "admin_from_rag_only",
                                    "latency_ms": llm_latency_ms,
                                    "estimated_tokens": estimated_tokens,
                                }
                            )

                            total_latency_ms = int((time.time() - start_time) * 1000)
                            self._analytics_log_agent_query(
                                tenant_id=req.tenant_id,
                                message_preview=req.message[:200],
                                intent=intent,
                                tools_used=tools_used,
                                total_tokens=total_tokens,
                                total_latency_ms=total_latency_ms,
                                success=True,
                                user_id=req.user_id,
                            )

                            return AgentResponse(text=llm_out, decision=decision, reasoning_trace=reasoning_trace)

                        except Exception as rag_err:
                            reasoning_trace.append(
                                {
                                    "step": "rag_for_admin_fallback",
                                    "status": "error",
                                    "error": str(rag_err),
                                }
                            )

                    # For all other questions, if we already have RAG hits from pgvector
                    # (rag_results from the prefetch step), reuse them to ground the
                    # LLM response instead of answering purely from the model.
                    # BUT: Skip RAG for news queries (they should use web search instead)
                    is_news_query_here = any(k in req.message.lower() for k in ["latest", "today", "news", "current", "recent", "breaking", "trending", "happening", "updates"])
                    news_patterns_here = [
                        r"latest news", r"current news", r"today's news", r"breaking news",
                        r"news about", r"news on", r"news of"
                    ]
                    is_news_query_here = is_news_query_here or any(re.search(p, req.message.lower()) for p in news_patterns_here)
                    
                    # NEVER use RAG for news queries - force web search or use general knowledge
                    if not use_rag_for_admin and rag_results and not is_news_query_here:
                        try:
                            rag_prefetched_dict: Dict[str, Any] = {"results": rag_results}
                            prompt_for_llm = self._build_prompt_with_rag(req, rag_prefetched_dict)
                            reasoning_trace.append(
                                {
                                    "step": "rag_context_for_llm",
                                    "hit_count": len(rag_results),
                                    "note": "used_prefetched_pgvector_hits",
                                }
                            )
                        except Exception as build_err:
                            reasoning_trace.append(
                                {
                                    "step": "rag_context_for_llm",
                                    "status": "error",
                                    "error": str(build_err),
                                }
                            )
                    elif not use_rag_for_admin:
                        # No RAG results available - enhance the prompt to still provide best answer
                        # BUT: For news queries, provide a helpful message about web search
                        if is_news_query_here:
                            prompt_for_llm = (
                                f"You are an assistant helping tenant {req.tenant_id}.\n\n"
                                f"## User Question\n{req.message}\n\n"
                                f"## Context\n"
                                f"The user is asking for latest news. I attempted to search for current information but didn't find specific results.\n\n"
                                f"## Your Task\n"
                                f"Provide helpful information about what the user might be looking for. "
                                f"If you have general knowledge about the topic, share it. "
                                f"Be honest that you don't have access to the very latest breaking news, but provide what context you can. "
                                f"Suggest that the user try checking major news websites directly or using a more specific search query.\n\n"
                                f"IMPORTANT: Do NOT say 'There is no mention of X in the provided context' - instead provide helpful general information or suggest where to find current news."
                            )
                        else:
                            prompt_for_llm = (
                                f"You are an assistant helping tenant {req.tenant_id}.\n\n"
                                f"## User Question\n{req.message}\n\n"
                                f"## Your Task\n"
                                f"Provide the best possible answer to the user's question. "
                                f"Be clear, accurate, comprehensive, and helpful. "
                                f"Focus on giving the user exactly what they need—clear guidance, accurate facts, "
                                f"and practical steps whenever possible. "
                                f"If you're uncertain about tenant-specific details, acknowledge that and provide general guidance."
                            )

                    llm_start = time.time()
                    llm_out = await self.llm.simple_call(prompt_for_llm, temperature=req.temperature)
                    llm_latency_ms = int((time.time() - llm_start) * 1000)
                    tools_used.append("llm")
                    
                    estimated_tokens = len(llm_out) // 4 + len(prompt_for_llm) // 4
                    total_tokens += estimated_tokens
                    
                    self._analytics_log_tool_usage(
                        tenant_id=req.tenant_id,
                        tool_name="llm",
                        latency_ms=llm_latency_ms,
                        tokens_used=estimated_tokens,
                        success=True,
                        user_id=req.user_id
                    )
                    
                    reasoning_trace.append({
                        "step": "llm_response",
                        "mode": "direct",
                        "latency_ms": llm_latency_ms,
                        "estimated_tokens": estimated_tokens
                    })
                    
                    total_latency_ms = int((time.time() - start_time) * 1000)
                    self._analytics_log_agent_query(
                        tenant_id=req.tenant_id,
                        message_preview=req.message[:200],
                        intent=intent,
                        tools_used=tools_used,
                        total_tokens=total_tokens,
                        total_latency_ms=total_latency_ms,
                        success=True,
                        user_id=req.user_id
                    )
                    
                    return AgentResponse(text=llm_out, decision=decision, reasoning_trace=reasoning_trace)

            except Exception as e:
                tool_traces.append({"tool": decision.tool, "error": str(e)})
                try:
                    fallback = await self.llm.simple_call(req.message, temperature=req.temperature)
                except Exception as llm_error:
                    error_msg = str(llm_error)
                    if "Cannot connect" in error_msg or "Ollama" in error_msg:
                        fallback = (
                            f"I encountered an error while processing your request: {str(e)}\n\n"
                            f"Additionally, the AI service (Ollama) is unavailable: {error_msg}\n\n"
                            f"To fix:\n"
                            f"1. Install Ollama from https://ollama.ai\n"
                            f"2. Start: `ollama serve`\n"
                            f"3. Pull model: `ollama pull {os.getenv('OLLAMA_MODEL', 'llama3.1:latest')}`"
                        )
                    else:
                        fallback = f"I encountered an error while processing your request: {str(e)}. Additionally, the AI service is unavailable: {error_msg}"
                return AgentResponse(
                    text=fallback,
                    decision=AgentDecision(action="respond", tool=None, tool_input=None, reason=f"tool_error_fallback: {e}"),
                    tool_traces=tool_traces,
                    reasoning_trace=reasoning_trace + [{
                        "step": "error",
                        "tool": decision.tool,
                        "error": str(e)
                    }]
                )

        # Default: direct LLM response
        # BUT: For news queries, try web search first even if tool selector didn't route to it
        msg_lower = req.message.lower()
        freshness_keywords = ["latest", "today", "news", "current", "recent", 
                             "now", "updates", "breaking", "trending", "happening"]
        news_patterns = [
            r"latest news", r"current news", r"today's news", r"breaking news",
            r"news about", r"news on", r"news of"
        ]
        is_news_query_default = any(k in msg_lower for k in freshness_keywords) or \
                               any(re.search(p, msg_lower) for p in news_patterns)
        
        # If it's a news query and we're in the default path, force web search
        if is_news_query_default and decision.action != "call_tool" and decision.action != "multi_step":
            try:
                web_query = req.message
                if len(web_query.split()) <= 4:
                    if "news" not in msg_lower:
                        web_query = f"{web_query} news latest"
                
                web_start = time.time()
                web_resp = await self.web_with_repair(
                    query=web_query,
                    tenant_id=req.tenant_id,
                    reasoning_trace=reasoning_trace,
                    user_id=req.user_id
                )
                web_latency_ms = int((time.time() - web_start) * 1000)
                tools_used.append("web")
                
                web_formatted = self._format_tool_output("web", web_resp, web_latency_ms)
                
                # Context Engineering: Compress tool output if needed
                web_formatted = await self.context_engineer.compressor.compress_tool_output("web", web_formatted)
                
                tool_traces.append({"tool": "web", "response": web_formatted})
                hits_count = len(self._extract_hits(web_formatted))
                
                if hits_count > 0:
                    prompt = self._build_prompt_with_web(req, web_formatted)
                else:
                    # Web search returned no results - use a news-specific prompt
                    prompt = (
                        f"You are an assistant helping tenant {req.tenant_id}.\n\n"
                        f"## User Question\n{req.message}\n\n"
                        f"## Context\n"
                        f"The user is asking for latest news, but web search did not return specific results for this query.\n\n"
                        f"## Your Task\n"
                        f"Provide helpful information about what the user might be looking for. "
                        f"If you know general information about the topic, share it. "
                        f"Be honest that you don't have access to the very latest news, but provide what context you can. "
                        f"Suggest that the user try rephrasing the query or checking news websites directly for the most current information."
                    )
                
                llm_start = time.time()
                llm_out = await self.llm.simple_call(prompt, temperature=req.temperature)
                llm_latency_ms = int((time.time() - llm_start) * 1000)
                tools_used.append("llm")
                estimated_tokens = len(llm_out) // 4 + len(prompt) // 4
                
                self._analytics_log_tool_usage(
                    tenant_id=req.tenant_id,
                    tool_name="llm",
                    latency_ms=llm_latency_ms,
                    tokens_used=estimated_tokens,
                    success=True,
                    user_id=req.user_id
                )
                
                total_latency_ms = int((time.time() - start_time) * 1000)
                self._analytics_log_agent_query(
                    tenant_id=req.tenant_id,
                    message_preview=req.message[:200],
                    intent=intent,
                    tools_used=tools_used,
                    total_tokens=estimated_tokens,
                    total_latency_ms=total_latency_ms,
                    success=True,
                    user_id=req.user_id
                )
                
                return AgentResponse(
                    text=llm_out,
                    decision=AgentDecision(action="respond", tool="web", tool_input=None, reason="news_query_forced_web_search"),
                    tool_traces=tool_traces,
                    reasoning_trace=reasoning_trace
                )
            except Exception as web_err:
                # If web search fails, fall through to default LLM
                reasoning_trace.append({
                    "step": "web_search_fallback",
                    "error": str(web_err)[:200]
                })
        
        try:
            llm_start = time.time()
            # For news queries in default path, use a better prompt
            if is_news_query_default:
                prompt_for_default = (
                    f"You are an assistant helping tenant {req.tenant_id}.\n\n"
                    f"## User Question\n{req.message}\n\n"
                    f"## Your Task\n"
                    f"The user is asking for latest news. I don't have access to real-time web search results right now. "
                    f"Please provide helpful information about what they might be looking for, or suggest they check news websites directly for the most current information."
                )
            else:
                prompt_for_default = req.message
            
            llm_out = await self.llm.simple_call(prompt_for_default, temperature=req.temperature)
            llm_latency_ms = int((time.time() - llm_start) * 1000)
            tools_used = ["llm"]
            estimated_tokens = len(llm_out) // 4 + len(prompt_for_default) // 4
            
            self._analytics_log_tool_usage(
                tenant_id=req.tenant_id,
                tool_name="llm",
                latency_ms=llm_latency_ms,
                tokens_used=estimated_tokens,
                success=True,
                user_id=req.user_id
            )
        except Exception as e:
            # If LLM fails, return a helpful error message
            error_msg = str(e)
            if "Cannot connect" in error_msg or "Ollama" in error_msg:
                llm_out = (
                    f"I couldn't connect to the AI service (Ollama). "
                    f"Error: {error_msg}\n\n"
                    f"To fix this:\n"
                    f"1. Install Ollama from https://ollama.ai\n"
                    f"2. Start Ollama: `ollama serve`\n"
                    f"3. Pull the model: `ollama pull {os.getenv('OLLAMA_MODEL', 'llama3.1:latest')}`\n"
                    f"4. Or set OLLAMA_URL and OLLAMA_MODEL in your .env file"
                )
            else:
                llm_out = f"I apologize, but I'm unable to process your request right now. The AI service is unavailable: {error_msg}"
            
            self._analytics_log_tool_usage(
                tenant_id=req.tenant_id,
                tool_name="llm",
                success=False,
                error_message=error_msg[:200],
                user_id=req.user_id
            )
            reasoning_trace.append({
                "step": "error",
                "tool": "llm",
                "error": str(e)
            })
        
        total_latency_ms = int((time.time() - start_time) * 1000)
        self._analytics_log_agent_query(
            tenant_id=req.tenant_id,
            message_preview=req.message[:200],
            intent=intent,
            tools_used=tools_used if 'tools_used' in locals() else [],
            total_tokens=estimated_tokens if 'estimated_tokens' in locals() else 0,
            total_latency_ms=total_latency_ms,
            success=True if 'llm_out' in locals() else False,
            user_id=req.user_id
        )
        
        response = AgentResponse(
            text=llm_out,
            decision=AgentDecision(action="respond", tool=None, tool_input=None, reason="default_llm"),
            reasoning_trace=reasoning_trace
        )
        # Cache successful response
        self._cache_response(req, response)
        return response

    def _build_prompt_with_rag(self, req: AgentRequest, rag_resp: Dict[str, Any]) -> str:
        snippets = []
        scores_info = []
        if isinstance(rag_resp, dict):
            hits = rag_resp.get("results") or rag_resp.get("hits") or []
            # Sort by score if available, take top 5
            sorted_hits = sorted(
                hits,
                key=lambda h: float(h.get("score", h.get("similarity", 0.0))),
                reverse=True
            )[:5]
            
            for i, h in enumerate(sorted_hits, 1):
                txt = h.get("text") or h.get("content") or str(h)
                score = h.get("score") or h.get("similarity", 0.0)
                snippets.append(f"[Source {i}] {txt}")
                scores_info.append(f"Source {i}: relevance score {score:.3f}")

        snippet_text = "\n\n".join(snippets) if snippets else ""
        scores_text = "\n".join(scores_info) if scores_info else ""
        
        if not snippet_text:
            prompt = (
                f"You are an assistant helping tenant {req.tenant_id}.\n\n"
                f"## User Question\n{req.message}\n\n"
                f"## Context\n"
                f"No relevant documents were found in the knowledge base for this question.\n\n"
                f"## Important Rules\n"
                f"If the user asks a question that cannot be answered directly from the Knowledge Base, "
                f"then and ONLY then use the web-search tool to gather information. "
                f"When using web search, keep the response short, factual, and neutral. "
                f"Do NOT provide long legal, medical, or highly detailed professional explanations. "
                f"If the topic involves legal, medical, financial, or safety-critical advice, provide a brief general explanation "
                f"and tell the user to consult a qualified professional. "
                f"Never present external information as part of the official Knowledge Base.\n\n"
                f"## Your Task\n"
                f"Since no Knowledge Base documents were found, you may use web search as a fallback if needed. "
                f"Provide a brief, helpful answer. If you're uncertain about tenant-specific details, "
                f"acknowledge that and provide general guidance. "
                f"For legal, medical, financial, or safety-critical topics, keep responses brief and recommend consulting a professional."
            )
        else:
            # Context Engineering: Get structured scratchpad context (Anthropic's note-taking)
            scratchpad_context = self.context_engineer.get_scratchpad_context(limit=5)
            scratchpad_section = f"\n## Structured Notes from Previous Steps\n{scratchpad_context}\n\n" if scratchpad_context else ""
            
            # Build prompt with Anthropic's recommended structure
            # Clear sections with XML/Markdown headers for better organization
            prompt = (
                f"<system>\n"
                f"You are an assistant helping tenant {req.tenant_id}. "
                f"Your goal is to provide the most accurate, comprehensive, and helpful answer possible.\n"
                f"</system>\n\n"
                f"<background_information>\n"
                f"## KB-First Strategy\n"
                f"The Knowledge Base was checked first and relevant documents were found. "
                f"Use these documents as your PRIMARY and AUTHORITATIVE source. "
                f"Web search should ONLY be used as a fallback if the Knowledge Base cannot answer the question.\n"
                f"{scratchpad_section}"
                f"</background_information>\n\n"
                f"<knowledge_base_documents>\n"
                f"The following documents were retrieved from the tenant's knowledge base as relevant to the user's question:\n\n"
                f"{snippet_text}\n\n"
                f"{'## Relevance Scores\n' + scores_text + '\n\n' if scores_text else ''}"
                f"</knowledge_base_documents>\n\n"
                f"<user_question>\n"
                f"{req.message}\n"
                f"</user_question>\n\n"
                f"<instructions>\n"
                f"## Your Task\n"
                f"1. **Primary Goal**: Answer the user's question using the information from the knowledge base documents above.\n"
                f"2. **KB Priority**: Base your answer PRIMARILY on the Knowledge Base. This is the authoritative source for tenant-specific information.\n"
                f"3. **Accuracy**: Base your answer primarily on the highest-scoring sources (most relevant documents).\n"
                f"4. **Comprehensiveness**: If multiple sources provide complementary information, synthesize them into a complete answer.\n"
                f"5. **Citation**: When referencing specific information, indicate which source(s) you used (e.g., 'According to Source 1...' or 'Sources 1 and 2 indicate...').\n"
                f"6. **Completeness**: If the documents don't fully answer the question, clearly state what information is available and what is missing.\n"
                f"7. **Clarity**: Write in a clear, professional, and easy-to-understand manner.\n"
                f"8. **Directness**: Get straight to the point - provide the answer the user needs without unnecessary preamble.\n"
                f"</instructions>\n\n"
                f"Provide your answer now:"
            )
        return prompt

    async def _execute_multi_step(self, req: AgentRequest, steps: List[Dict[str, Any]], 
                                   decision: AgentDecision, tool_traces: List[Dict[str, Any]],
                                   reasoning_trace: List[Dict[str, Any]],
                                   pre_fetched_rag: Optional[Dict[str, Any]] = None) -> AgentResponse:
        """
        Execute multiple tools in sequence or parallel and synthesize results with LLM.
        Supports parallel execution when steps are marked with "parallel" flag.
        """
        start_time = time.time()
        rag_data = None
        web_data = None
        admin_data = None
        collected_data = []
        tools_used = []
        total_tokens = 0
        
        # Detect if this is a news query - if so, skip RAG steps entirely
        msg_lower = req.message.lower()
        freshness_keywords = ["latest", "today", "news", "current", "recent", 
                             "now", "updates", "breaking", "trending", "happening"]
        news_patterns = [
            r"latest news", r"current news", r"today's news", r"breaking news",
            r"news about", r"news on", r"news of"
        ]
        is_news_query = any(k in msg_lower for k in freshness_keywords) or \
                        any(re.search(p, msg_lower) for p in news_patterns)
        
        # Filter out RAG steps for news queries
        if is_news_query:
            steps = [s for s in steps if s.get("tool") != "rag" and "rag" not in str(s.get("parallel", {}))]
            reasoning_trace.append({
                "step": "multi_step_news_filter",
                "action": "removed_rag_steps",
                "remaining_steps": [s.get("tool") if isinstance(s, dict) and "tool" in s else "parallel" for s in steps]
            })

        # Check if any step has parallel execution flag
        parallel_step = None
        for step_info in steps:
            if step_info.get("parallel"):
                parallel_step = step_info
                break

        # Handle parallel execution if detected
        if parallel_step and parallel_step.get("parallel"):
            parallel_config = parallel_step.get("parallel")
            parallel_tasks = {}
            start_time_parallel = time.time()

            # Prepare parallel tasks with retry logic
            # Skip RAG for news queries
            if "rag" in parallel_config and not is_news_query:
                rag_query = parallel_config["rag"]
                if pre_fetched_rag:
                    # Use pre-fetched RAG if available - create a simple async function
                    async def get_prefetched_rag():
                        return pre_fetched_rag
                    parallel_tasks["rag"] = get_prefetched_rag()
                else:
                    # Wrap with retry logic for parallel execution
                    async def rag_with_retry_wrapper():
                        return await self.rag_with_repair(
                            query=rag_query,
                            tenant_id=req.tenant_id,
                            original_threshold=0.3,
                            reasoning_trace=reasoning_trace,
                            user_id=req.user_id
                        )
                    parallel_tasks["rag"] = rag_with_retry_wrapper()
            elif "rag" in parallel_config and is_news_query:
                # Remove RAG from parallel config for news queries
                parallel_config = {k: v for k, v in parallel_config.items() if k != "rag"}
                reasoning_trace.append({
                    "step": "parallel_news_filter",
                    "action": "removed_rag_from_parallel",
                    "remaining_tools": list(parallel_config.keys())
                })

            if "web" in parallel_config:
                web_query = parallel_config["web"]
                # Wrap with retry logic for parallel execution
                async def web_with_retry_wrapper():
                    return await self.web_with_repair(
                        query=web_query,
                        tenant_id=req.tenant_id,
                        reasoning_trace=reasoning_trace,
                        user_id=req.user_id
                    )
                parallel_tasks["web"] = web_with_retry_wrapper()

            # Execute tools in parallel
            if parallel_tasks:
                reasoning_trace.append({
                    "step": "parallel_execution",
                    "tools": list(parallel_tasks.keys()),
                    "mode": "parallel"
                })
                
                parallel_results = await self.run_parallel_tools(parallel_tasks)
                parallel_latency_ms = int((time.time() - start_time_parallel) * 1000)

                # Process RAG results
                if "rag" in parallel_results:
                    rag_result = parallel_results["rag"]
                    if isinstance(rag_result, Exception):
                        tool_traces.append({"tool": "rag", "error": str(rag_result), "note": "parallel"})
                        reasoning_trace.append({
                            "step": "tool_execution",
                            "tool": "rag",
                            "status": "error",
                            "error": str(rag_result),
                            "latency_ms": parallel_latency_ms
                        })
                        self._analytics_log_tool_usage(
                            tenant_id=req.tenant_id,
                            tool_name="rag",
                            latency_ms=parallel_latency_ms,
                            success=False,
                            error_message=str(rag_result)[:200],
                            user_id=req.user_id
                        )
                    else:
                        rag_data = rag_result
                        tools_used.append("rag")
                        tool_traces.append({"tool": "rag", "response": rag_result, "note": "parallel"})
                        hits_count = len(self._extract_hits(rag_result))
                        avg_score = None
                        top_score = None
                        if hits_count > 0:
                            scores = [h.get("score", 0.0) for h in self._extract_hits(rag_result) if isinstance(h, dict) and "score" in h]
                            if scores:
                                avg_score = sum(scores) / len(scores)
                                top_score = max(scores)
                        self._analytics_log_rag_search(
                            tenant_id=req.tenant_id,
                            query=req.message[:500],
                            hits_count=hits_count,
                            avg_score=avg_score,
                            top_score=top_score,
                            latency_ms=parallel_latency_ms
                        )
                        self._analytics_log_tool_usage(
                            tenant_id=req.tenant_id,
                            tool_name="rag",
                            latency_ms=parallel_latency_ms,
                            success=True,
                            user_id=req.user_id
                        )
                        reasoning_trace.append({
                            "step": "tool_execution",
                            "tool": "rag",
                            "hit_count": hits_count,
                            "summary": self._summarize_hits(rag_result, limit=2),
                            "latency_ms": parallel_latency_ms,
                            "mode": "parallel"
                        })

                # Process Web results
                if "web" in parallel_results:
                    web_result = parallel_results["web"]
                    if isinstance(web_result, Exception):
                        tool_traces.append({"tool": "web", "error": str(web_result), "note": "parallel"})
                        reasoning_trace.append({
                            "step": "tool_execution",
                            "tool": "web",
                            "status": "error",
                            "error": str(web_result),
                            "latency_ms": parallel_latency_ms
                        })
                        self._analytics_log_tool_usage(
                            tenant_id=req.tenant_id,
                            tool_name="web",
                            latency_ms=parallel_latency_ms,
                            success=False,
                            error_message=str(web_result)[:200],
                            user_id=req.user_id
                        )
                    else:
                        web_data = web_result
                        tools_used.append("web")
                        tool_traces.append({"tool": "web", "response": web_result, "note": "parallel"})
                        hits_count = len(self._extract_hits(web_result))
                        self._analytics_log_tool_usage(
                            tenant_id=req.tenant_id,
                            tool_name="web",
                            latency_ms=parallel_latency_ms,
                            success=True,
                            user_id=req.user_id
                        )
                        reasoning_trace.append({
                            "step": "tool_execution",
                            "tool": "web",
                            "hit_count": hits_count,
                            "summary": self._summarize_hits(web_result, limit=2),
                            "latency_ms": parallel_latency_ms,
                            "mode": "parallel"
                        })

                # Merge parallel results
                merged_context = merge_parallel_results(parallel_results)
                sources_list = list(set(e.get("source") for e in merged_context if e.get("source"))) if merged_context else []
                reasoning_trace.append({
                    "step": "result_merger",
                    "merged_items": len(merged_context),
                    "sources": sources_list
                })

                # Format merged context for prompt
                data_section = format_merged_context_for_prompt(merged_context, max_items=10)
            else:
                data_section = ""

        else:
            # Sequential execution (original logic)
            parallel_tasks = {}
            rag_parallel_query = self._first_query_for_tool(steps, "rag", req.message)
            web_parallel_query = self._first_query_for_tool(steps, "web", req.message)
            if rag_parallel_query and web_parallel_query and rag_parallel_query == web_parallel_query:
                if not pre_fetched_rag:
                    parallel_tasks["rag"] = asyncio.create_task(self.mcp.call_rag(req.tenant_id, rag_parallel_query))
                parallel_tasks["web"] = asyncio.create_task(self.mcp.call_web(req.tenant_id, web_parallel_query))

            # Execute each step in sequence
            for step_info in steps:
                tool_name = step_info.get("tool")
                step_input = step_info.get("input") or {}
                query = step_input.get("query") or req.message

                try:
                    if tool_name == "rag":
                        # Skip RAG for news queries
                        if is_news_query:
                            reasoning_trace.append({
                                "step": "tool_execution",
                                "tool": "rag",
                                "status": "skipped",
                                "reason": "news_query_detected"
                            })
                            continue  # Skip this RAG step
                        
                        # Reuse pre-fetched RAG if available, otherwise fetch with retry
                        if pre_fetched_rag and query == rag_parallel_query:
                            rag_resp = pre_fetched_rag
                            tool_traces.append({"tool": "rag", "response": rag_resp, "note": "used_pre_fetched"})
                        elif parallel_tasks.get("rag") and query == rag_parallel_query:
                            rag_resp = await parallel_tasks["rag"]
                            tool_traces.append({"tool": "rag", "response": rag_resp, "note": "parallel"})
                        else:
                            # Use autonomous retry with self-correction
                            rag_resp = await self.rag_with_repair(
                                query=query,
                                tenant_id=req.tenant_id,
                                original_threshold=0.3,
                                reasoning_trace=reasoning_trace,
                                user_id=req.user_id
                            )
                            tool_traces.append({"tool": "rag", "response": rag_resp, "note": "with_retry"})
                        rag_data = rag_resp
                        tools_used.append("rag")
                        hits = self._extract_hits(rag_resp)
                        reasoning_trace.append({
                            "step": "tool_execution",
                            "tool": "rag",
                            "hit_count": len(hits),
                            "summary": self._summarize_hits(rag_resp, limit=2)
                        })
                        # Extract snippets for prompt
                        if isinstance(rag_resp, dict):
                            for h in hits[:5]:
                                txt = h.get("text") or h.get("content") or str(h)
                                collected_data.append(f"[RAG] {txt}")

                    elif tool_name == "web":
                        if parallel_tasks.get("web") and query == web_parallel_query:
                            web_resp = await parallel_tasks["web"]
                            tool_traces.append({"tool": "web", "response": web_resp, "note": "parallel"})
                        else:
                            # Use autonomous retry with query rewriting
                            web_resp = await self.web_with_repair(
                                query=query,
                                tenant_id=req.tenant_id,
                                reasoning_trace=reasoning_trace,
                                user_id=req.user_id
                            )
                            tool_traces.append({"tool": "web", "response": web_resp, "note": "with_retry"})
                        web_data = web_resp
                        tools_used.append("web")
                        hits = self._extract_hits(web_resp)
                        reasoning_trace.append({
                            "step": "tool_execution",
                            "tool": "web",
                            "hit_count": len(hits),
                            "summary": self._summarize_hits(web_resp, limit=2)
                        })
                        # Extract snippets for prompt
                        if isinstance(web_resp, dict):
                            for h in hits[:5]:
                                title = h.get("title") or h.get("headline") or ""
                                snippet = h.get("snippet") or h.get("summary") or h.get("text") or ""
                                url = h.get("url") or h.get("link") or ""
                                collected_data.append(f"[WEB] {title}\n{snippet}\nSource: {url}")

                    elif tool_name == "admin":
                        admin_resp = await self.mcp.call_admin(req.tenant_id, query)
                        tool_traces.append({"tool": "admin", "response": admin_resp})
                        admin_data = admin_resp
                        tools_used.append("admin")
                        collected_data.append(f"[ADMIN] {json.dumps(admin_resp)}")
                        reasoning_trace.append({
                            "step": "tool_execution",
                            "tool": "admin",
                            "status": "completed"
                        })

                    elif tool_name == "llm":
                        # LLM is always last - synthesize all collected data
                        break

                except Exception as e:
                    tool_traces.append({"tool": tool_name, "error": str(e)})
                    # Continue with other tools even if one fails
                    reasoning_trace.append({
                        "step": "error",
                        "tool": tool_name,
                        "error": str(e)
                    })

            # Build comprehensive prompt with all collected data
            data_section = "\n---\n".join(collected_data) if collected_data else ""

        # Build final response. For admin-identity style questions, bypass generic
        # multi-step LLM behaviour and answer directly from RAG data if available.
        user_text = req.message.lower()
        user_text_normalized = " ".join(user_text.split())
        admin_phrases = [
            "who is the admin",
            "who's the admin",
            "who is admin",
            "who is the administrator",
            "who's the administrator",
            "who administers this platform",
            "who administers the platform",
            "who is the owner",
            "who's the owner",
            "who owns this platform",
            "who owns the platform",
            "who is the admin of integrachat",
            "who's the admin of integrachat",
        ]
        if any(p in user_text_normalized for p in admin_phrases) or ("admin" in user_text and "who" in user_text):
            hits = self._extract_hits(rag_data) if rag_data else []
            if hits:
                best = hits[0]
                admin_text = best.get("text") or best.get("content") or str(best)
                llm_out = f"According to the tenant knowledge base, {admin_text.strip()}"
            else:
                llm_out = "I don't know who administers this platform based on the tenant data."

            llm_latency_ms = 0
            estimated_tokens = len(llm_out) // 4 + len(req.message) // 4
            total_tokens += estimated_tokens
            tools_used.append("llm")

            self._analytics_log_tool_usage(
                tenant_id=req.tenant_id,
                tool_name="llm",
                latency_ms=llm_latency_ms,
                tokens_used=estimated_tokens,
                success=True,
                user_id=req.user_id
            )

            total_latency_ms = int((time.time() - start_time) * 1000)
            self._analytics_log_agent_query(
                tenant_id=req.tenant_id,
                message_preview=req.message[:200],
                intent="multi_step",
                tools_used=tools_used,
                total_tokens=total_tokens,
                total_latency_ms=total_latency_ms,
                success=True,
                user_id=req.user_id
            )

            return AgentResponse(
                text=llm_out,
                decision=decision,
                tool_traces=tool_traces,
                reasoning_trace=reasoning_trace + [{
                    "step": "llm_response",
                    "mode": "multi_step_admin_from_rag_only",
                    "latency_ms": llm_latency_ms,
                    "estimated_tokens": estimated_tokens
                }]
            )

        # Otherwise, build the normal multi-step synthesis prompt.
        if data_section:
            # Check if we have both RAG and web data
            has_rag = "[RAG]" in data_section
            has_web = "[WEB]" in data_section
            
            kb_first_note = ""
            web_fallback_note = ""
            if has_rag and has_web:
                kb_first_note = (
                    f"\n## KB-First Strategy\n"
                    f"**Knowledge Base (RAG) was checked FIRST** and found relevant information. "
                    f"This is the PRIMARY and AUTHORITATIVE source. "
                    f"Web search results are provided as supplementary information only. "
                    f"Prioritize Knowledge Base information over web search results.\n\n"
                )
                web_fallback_note = (
                    f"\n## Web Search Rules\n"
                    f"When using web search information as supplementary data:\n"
                    f"- Keep web search details brief and factual\n"
                    f"- For legal, medical, financial, or safety topics, add: 'For specific advice, consult a qualified professional.'\n"
                    f"- Clearly distinguish between Knowledge Base (authoritative) and web search (supplementary) information\n\n"
                )
            elif has_web and not has_rag:
                kb_first_note = (
                    f"\n## KB-First Strategy\n"
                    f"The Knowledge Base was checked FIRST but no relevant information was found. "
                    f"Web search results below are provided as a FALLBACK. "
                    f"Keep the response short, factual, and neutral. "
                    f"For legal, medical, financial, or safety topics, recommend consulting a qualified professional.\n\n"
                )
            
            # Get structured scratchpad context (Anthropic's note-taking)
            scratchpad_context = self.context_engineer.get_scratchpad_context(limit=5)
            scratchpad_section = f"\n## Structured Notes\n{scratchpad_context}\n" if scratchpad_context else ""
            
            # Build prompt with Anthropic's structured format (XML-style sections)
            prompt = (
                f"<system>\n"
                f"You are an assistant helping tenant {req.tenant_id}. "
                f"Your goal is to provide the most accurate, comprehensive, and helpful answer possible.\n"
                f"</system>\n\n"
                f"<background_information>\n"
                f"{kb_first_note.strip()}"
                f"{scratchpad_section}"
                f"</background_information>\n\n"
                f"<information_collected>\n"
                f"The following details have been gathered from reliable sources:\n\n"
                f"{data_section}\n"
                f"</information_collected>\n\n"
                f"{f'<web_search_guidance>\n{web_fallback_note.strip()}\n</web_search_guidance>\n\n' if web_fallback_note else ''}"
                f"<user_request>\n"
                f"{req.message}\n"
                f"</user_request>\n\n"
                f"<instructions>\n"
                f"## Your Task\n"
                f"1. **Primary Goal**: Use the information above to directly and completely address the user's request.\n"
                f"2. **Source Priority**: {'If both Knowledge Base (RAG) and web search results are present, prioritize Knowledge Base as the authoritative source. ' if has_rag and has_web else ''}Use web search information only to supplement or when KB has no relevant information.\n"
                f"3. **Synthesis**: Combine information from different sources when they provide complementary details.\n"
                f"4. **Prioritization**: If sources conflict, prioritize Knowledge Base information over web search results.\n"
                f"5. **Completeness**: Provide a comprehensive answer that covers all aspects of the user's question.\n"
                f"6. **Accuracy**: Base your answer on the provided information. If information is missing or uncertain, clearly state that.\n"
                f"7. **Clarity**: Write in a clear, professional, and easy-to-understand manner.\n"
                f"8. **Directness**: Get straight to the point - provide the answer the user needs without unnecessary preamble.\n"
                f"9. **Actionability**: If the question requires steps or actions, provide clear, actionable guidance.\n"
                f"10. **Citation**: When referencing specific sources, indicate which source(s) you used (e.g., '[RAG]', '[WEB]').\n"
                f"{'11. **Brief Web Content**: If using web search, keep that portion of the response brief (2-4 sentences). Add professional disclaimers for legal/medical/financial topics.\n' if has_web else ''}"
                f"</instructions>\n\n"
                f"If the information is incomplete, explain what can and cannot be concluded from the available data. "
                f"Focus on giving the user exactly what they need—clear guidance, accurate facts, and practical steps whenever possible.\n\n"
                f"Provide your comprehensive answer now:"
            )
        else:
            # No data collected, provide best answer from general knowledge
            prompt = (
                f"You are an assistant helping tenant {req.tenant_id}.\n\n"
                f"## User Question\n{req.message}\n\n"
                f"## Context\n"
                f"No specific information was found in the knowledge base or web search for this question.\n\n"
                f"## Your Task\n"
                f"Provide the best possible answer based on your general knowledge. "
                f"Be clear, accurate, comprehensive, and helpful. "
                f"If you're uncertain about tenant-specific details, acknowledge that and provide general guidance. "
                f"Focus on giving the user exactly what they need—clear guidance, accurate facts, and practical steps whenever possible."
            )

        # Final LLM synthesis
        try:
            llm_start = time.time()
            llm_out = await self.llm.simple_call(prompt, temperature=req.temperature)
            llm_latency_ms = int((time.time() - llm_start) * 1000)
            tools_used.append("llm")

            estimated_tokens = len(llm_out) // 4 + len(prompt) // 4
            total_tokens += estimated_tokens

            self._analytics_log_tool_usage(
                tenant_id=req.tenant_id,
                tool_name="llm",
                latency_ms=llm_latency_ms,
                tokens_used=estimated_tokens,
                success=True,
                user_id=req.user_id
            )

            total_latency_ms = int((time.time() - start_time) * 1000)
            self._analytics_log_agent_query(
                tenant_id=req.tenant_id,
                message_preview=req.message[:200],
                intent="multi_step",
                tools_used=tools_used,
                total_tokens=total_tokens,
                total_latency_ms=total_latency_ms,
                success=True,
                user_id=req.user_id
            )

            return AgentResponse(
                text=llm_out,
                decision=decision,
                tool_traces=tool_traces,
                reasoning_trace=reasoning_trace + [{
                    "step": "llm_response",
                    "mode": "multi_step_parallel" if parallel_step else "multi_step",
                    "latency_ms": llm_latency_ms,
                    "estimated_tokens": estimated_tokens
                }]
            )
        except Exception as e:
            tool_traces.append({"tool": "llm", "error": str(e)})
            error_msg = str(e)
            # Provide helpful error message
            if "Cannot connect" in error_msg or "Ollama" in error_msg:
                fallback = (
                    f"I couldn't connect to the AI service (Ollama). "
                    f"Error: {error_msg}\n\n"
                    f"To fix this:\n"
                    f"1. Install Ollama from https://ollama.ai\n"
                    f"2. Start Ollama: `ollama serve`\n"
                    f"3. Pull the model: `ollama pull {os.getenv('OLLAMA_MODEL', 'llama3.1:latest')}`\n"
                    f"4. Or set OLLAMA_URL and OLLAMA_MODEL in your .env file"
                )
            else:
                fallback = f"I encountered an error while synthesizing the response: {error_msg}"
            return AgentResponse(
                text=fallback,
                decision=AgentDecision(
                    action="respond",
                    tool=None,
                    tool_input=None,
                    reason=f"multi_step_llm_error: {e}"
                ),
                tool_traces=tool_traces,
                reasoning_trace=reasoning_trace + [{
                    "step": "error",
                    "tool": "llm",
                    "error": str(e)
                }]
            )

    # =============================================================
    # AUTONOMOUS RETRY + SELF-CORRECTION SYSTEM
    # =============================================================
    """
    This system provides autonomous retry and self-correction capabilities
    for the agent orchestrator. It enables the agent to:
    
    1. **Self-healing**: Tools that break automatically retry with adjusted parameters
    2. **Resilient operations**: Handles low RAG scores, empty web results, and misfired rules
    3. **Smart optimization**: Automatically rewrites queries, adjusts thresholds, and optimizes parameters
    4. **Enterprise-grade reliability**: Matches enterprise behavior with comprehensive retry strategies
    
    Key features:
    - safe_tool_call(): Generic retry wrapper for any tool call
    - rag_with_repair(): RAG search with automatic threshold adjustment and query expansion
    - web_with_repair(): Web search with automatic query rewriting for empty results
    - rule_safe_message(): Message rewriting to comply with admin rules
    
    All retry attempts are logged to analytics for monitoring and debugging.
    """

    async def safe_tool_call(
        self,
        tool_fn,
        params: Dict[str, Any],
        max_retries: int = 2,
        fallback_params: Optional[Dict[str, Any]] = None,
        tool_name: str = "unknown",
        tenant_id: Optional[str] = None,
        user_id: Optional[str] = None,
        reasoning_trace: Optional[List[Dict[str, Any]]] = None
    ) -> Dict[str, Any]:
        """
        Wrapper for tool calls with automatic retry and self-correction.
        
        Args:
            tool_fn: Async function to call
            params: Parameters to pass to tool_fn
            max_retries: Maximum number of retry attempts
            fallback_params: Alternative parameters to try if initial attempt fails
            tool_name: Name of the tool (for logging)
            tenant_id: Tenant ID (for analytics)
            user_id: User ID (for analytics)
            reasoning_trace: Optional reasoning trace to append to
            
        Returns:
            Tool result dictionary, or {"error": "tool_failed_after_retries"} if all attempts fail
        """
        for attempt in range(max_retries):
            try:
                result = await tool_fn(**params)
                if attempt > 0:
                    # Log successful retry
                    if reasoning_trace is not None:
                        reasoning_trace.append({
                            "step": "retry_success",
                            "tool": tool_name,
                            "attempt": attempt + 1,
                            "status": "recovered"
                        })
                    if tenant_id:
                        self._analytics_log_tool_usage(
                            tenant_id=tenant_id,
                            tool_name=f"{tool_name}_retry_{attempt+1}",
                            latency_ms=0,
                            success=True,
                            user_id=user_id
                        )
                return result
            except Exception as e:
                error_msg = str(e)
                if reasoning_trace is not None:
                    reasoning_trace.append({
                        "step": "retry_attempt",
                        "tool": tool_name,
                        "attempt": attempt + 1,
                        "error": error_msg[:200]
                    })
                
                # Log failed attempt
                if tenant_id:
                    self._analytics_log_tool_usage(
                        tenant_id=tenant_id,
                        tool_name=tool_name,
                        latency_ms=0,
                        success=False,
                        error_message=error_msg[:200],
                        user_id=user_id
                    )
                
                # Try alternate params if provided and not last attempt
                if fallback_params and attempt < max_retries - 1:
                    params = {**params, **fallback_params}
                    if reasoning_trace is not None:
                        reasoning_trace.append({
                            "step": "retry_with_fallback_params",
                            "tool": tool_name,
                            "attempt": attempt + 2,
                            "fallback_params": fallback_params
                        })
                
                # If last attempt, return error
                if attempt == max_retries - 1:
                    return {"error": "tool_failed_after_retries", "error_message": error_msg}
        
        return {"error": "tool_failed_after_retries"}

    async def rag_with_repair(
        self,
        query: str,
        tenant_id: str,
        original_threshold: float = 0.3,
        reasoning_trace: Optional[List[Dict[str, Any]]] = None,
        user_id: Optional[str] = None
    ) -> Dict[str, Any]:
        """
        RAG search with automatic self-correction for low scores.
        
        Strategy:
        1. Try with original threshold
        2. If top_score < 0.30, retry with lower threshold (0.15)
        3. If still low (< 0.15), expand query and retry
        """
        # Initial attempt
        rag_start = time.time()
        result = await self.mcp.call_rag(tenant_id, query, threshold=original_threshold)
        rag_latency_ms = int((time.time() - rag_start) * 1000)
        
        # Extract hits and calculate scores
        hits = self._extract_hits(result)
        top_score = None
        avg_score = None
        
        if hits:
            scores = [h.get("score", 0.0) for h in hits if isinstance(h, dict) and "score" in h]
            if scores:
                top_score = max(scores)
                avg_score = sum(scores) / len(scores)
        
        if reasoning_trace is not None:
            reasoning_trace.append({
                "step": "rag_initial_search",
                "query": query[:200],
                "hits_count": len(hits),
                "top_score": top_score,
                "avg_score": avg_score,
                "threshold": original_threshold
            })
        
        # Retry logic: low score → lower threshold
        if top_score is not None and top_score < 0.30 and original_threshold >= 0.15:
            if reasoning_trace is not None:
                reasoning_trace.append({
                    "step": "rag_retry_low_threshold",
                    "reason": f"top_score {top_score:.3f} < 0.30, retrying with threshold=0.15"
                })
            
            retry_start = time.time()
            result = await self.mcp.call_rag(tenant_id, query, threshold=0.15)
            retry_latency_ms = int((time.time() - retry_start) * 1000)
            rag_latency_ms += retry_latency_ms
            
            hits = self._extract_hits(result)
            if hits:
                scores = [h.get("score", 0.0) for h in hits if isinstance(h, dict) and "score" in h]
                if scores:
                    top_score = max(scores)
                    avg_score = sum(scores) / len(scores)
            
            # Log retry
            self._analytics_log_tool_usage(
                tenant_id=tenant_id,
                tool_name="rag_retry_low_threshold",
                latency_ms=retry_latency_ms,
                success=True,
                user_id=user_id
            )
        
        # Final retry: expand query if score still too low
        if top_score is not None and top_score < 0.15:
            expanded_query = f"{query} (more details comprehensive explanation)"
            if reasoning_trace is not None:
                reasoning_trace.append({
                    "step": "rag_retry_expanded_query",
                    "reason": f"top_score {top_score:.3f} < 0.15, retrying with expanded query",
                    "original_query": query[:200],
                    "expanded_query": expanded_query[:200]
                })
            
            retry_start = time.time()
            result = await self.mcp.call_rag(tenant_id, expanded_query, threshold=0.15)
            retry_latency_ms = int((time.time() - retry_start) * 1000)
            rag_latency_ms += retry_latency_ms
            
            hits = self._extract_hits(result)
            if hits:
                scores = [h.get("score", 0.0) for h in hits if isinstance(h, dict) and "score" in h]
                if scores:
                    top_score = max(scores)
                    avg_score = sum(scores) / len(scores)
            
            # Log retry
            self._analytics_log_tool_usage(
                tenant_id=tenant_id,
                tool_name="rag_retry_expanded_query",
                latency_ms=retry_latency_ms,
                success=True,
                user_id=user_id
            )
            
            if reasoning_trace is not None:
                reasoning_trace.append({
                    "step": "rag_expanded_query_result",
                    "hits_count": len(hits),
                    "top_score": top_score,
                    "avg_score": avg_score
                })
        
        # Log final RAG search
        if hits:
            self._analytics_log_rag_search(
                tenant_id=tenant_id,
                query=query[:500],
                hits_count=len(hits),
                avg_score=avg_score,
                top_score=top_score,
                latency_ms=rag_latency_ms
            )
        
        return result

    async def web_with_repair(
        self,
        query: str,
        tenant_id: str,
        reasoning_trace: Optional[List[Dict[str, Any]]] = None,
        user_id: Optional[str] = None
    ) -> Dict[str, Any]:
        """
        Web search with multi-query strategy and automatic query rewriting.
        
        Strategy:
        1. Try original query
        2. If empty, generate multiple query variations using query expander
        3. Execute queries in parallel for better results
        4. Merge results from all successful queries
        """
        # Detect if this is a news query
        query_lower = query.lower()
        is_news_query = any(kw in query_lower for kw in ["news", "latest", "breaking", "current", "today", "recent", "update"])
        
        # Initial attempt
        web_start = time.time()
        result = await self.mcp.call_web(tenant_id, query)
        web_latency_ms = int((time.time() - web_start) * 1000)
        
        hits = self._extract_hits(result)
        
        if reasoning_trace is not None:
            reasoning_trace.append({
                "step": "web_initial_search",
                "query": query[:200],
                "hits_count": len(hits),
                "is_news_query": is_news_query
            })
        
        # Multi-query strategy: if initial results are poor, try multiple variations in parallel
        if not result or len(hits) < 3:
            # Generate query variations
            if is_news_query:
                # Use query expander for news queries
                try:
                    query_variations = self.query_expander.expand_news_query(query)
                except Exception:
                    query_variations = [
                        f"{query} news",
                        f"latest {query}",
                        f"{query} latest news",
                        f"breaking news {query}"
                    ]
            else:
                # For general queries, try explanation-focused rewrites
                query_variations = [
                    f"best explanation of {query}",
                    f"{query} facts summary",
                    f"information about {query}",
                    f"what is {query}"
                ]
            
            # Execute multiple queries in parallel
            if len(query_variations) > 1:
                async def search_variation(q: str):
                    try:
                        return await self.mcp.call_web(tenant_id, q)
                    except Exception as e:
                        logger.debug(f"Web search failed for query '{q}': {e}")
                        return None
                
                # Run all variations in parallel
                parallel_tasks = {q: search_variation(q) for q in query_variations[:3]}  # Limit to 3 parallel
                parallel_results = await self.run_parallel_tools(parallel_tasks)
                
                # Merge results from all successful queries
                all_hits = []
                seen_urls = set()
                
                # Add original hits
                for hit in hits:
                    url = hit.get("url") or hit.get("link", "")
                    if url and url not in seen_urls:
                        all_hits.append(hit)
                        seen_urls.add(url)
                
                # Add hits from parallel queries
                for q, res in parallel_results.items():
                    if res and not isinstance(res, Exception):
                        var_hits = self._extract_hits(res)
                        for hit in var_hits:
                            url = hit.get("url") or hit.get("link", "")
                            if url and url not in seen_urls:
                                all_hits.append(hit)
                                seen_urls.add(url)
                
                # Update result with merged hits
                if all_hits:
                    result = {"results": all_hits[:10]}  # Limit to top 10
                    hits = all_hits[:10]
                    
                    if reasoning_trace is not None:
                        reasoning_trace.append({
                            "step": "web_multi_query_merge",
                            "variations_tried": len(query_variations),
                            "total_hits_merged": len(all_hits),
                            "final_hits_count": len(hits)
                        })
                # If parallel didn't help, try one more sequential attempt with best variation
                if not all_hits and len(query_variations) > 0:
                    best_variation = query_variations[0]
                    retry_start = time.time()
                    try:
                        result = await self.mcp.call_web(tenant_id, best_variation)
                        retry_latency_ms = int((time.time() - retry_start) * 1000)
                        web_latency_ms += retry_latency_ms
                        hits = self._extract_hits(result)
                        if hits:
                            if reasoning_trace is not None:
                                reasoning_trace.append({
                                    "step": "web_sequential_fallback_success",
                                    "query": best_variation[:200],
                                    "hits_count": len(hits)
                                })
                    except Exception as e:
                        logger.debug(f"Final web search retry failed: {e}")
        
        # Log final web search
        self._analytics_log_tool_usage(
            tenant_id=tenant_id,
            tool_name="web",
            latency_ms=web_latency_ms,
            success=len(hits) > 0,
            user_id=user_id
        )
        
        return result

    async def rule_safe_message(
        self,
        user_message: str,
        tenant_id: str,
        reasoning_trace: Optional[List[Dict[str, Any]]] = None
    ) -> str:
        """
        Check admin rules and rewrite message if it violates policies.
        
        Strategy:
        1. Check rules
        2. If blocked, ask LLM to rewrite message to comply
        3. Return safe version
        """
        matches: List[RedFlagMatch] = await self.redflag.check(tenant_id, user_message)
        
        if not matches:
            return user_message
        
        # Check if any are blocking rules (not just brief response rules)
        blocking_rules = []
        for match in matches:
            rule_text = (match.description or match.pattern or "").lower()
            is_brief_rule = (
                match.severity == "low" and (
                    "greeting" in rule_text or 
                    "brief" in rule_text or 
                    "simple response" in rule_text
                )
            )
            if not is_brief_rule:
                blocking_rules.append(match)
        
        # Only rewrite if there are blocking rules
        if not blocking_rules:
            return user_message
        
        if reasoning_trace is not None:
            reasoning_trace.append({
                "step": "rule_violation_detected",
                "blocking_rules_count": len(blocking_rules),
                "action": "attempting_rewrite"
            })
        
        # Ask LLM to rewrite the message
        rewrite_prompt = f"""The following user message violates company policies. Rewrite it to be compliant while preserving the user's intent as much as possible.

Original message: "{user_message}"

Violated policies:
{chr(10).join([f"- {m.description or m.pattern}" for m in blocking_rules[:3]])}

Provide a rewritten version that:
1. Avoids the policy violations
2. Preserves the user's original intent
3. Remains professional and helpful

Rewritten message:"""
        
        try:
            rewritten = await self.llm.simple_call(rewrite_prompt, temperature=0.3)
            rewritten = rewritten.strip().strip('"').strip("'")
            
            if reasoning_trace is not None:
                reasoning_trace.append({
                    "step": "rule_rewrite_completed",
                    "original_length": len(user_message),
                    "rewritten_length": len(rewritten),
                    "rewritten_preview": rewritten[:200]
                })
            
            # Verify the rewritten message doesn't trigger rules
            verify_matches = await self.redflag.check(tenant_id, rewritten)
            if not verify_matches or all(
                (m.description or m.pattern or "").lower() in ["greeting", "brief", "simple response"]
                for m in verify_matches
            ):
                return rewritten
            
            if reasoning_trace is not None:
                reasoning_trace.append({
                    "step": "rule_rewrite_still_violates",
                    "action": "using_original_with_block"
                })
            
        except Exception as e:
            if reasoning_trace is not None:
                reasoning_trace.append({
                    "step": "rule_rewrite_failed",
                    "error": str(e)[:200]
                })
        
        # Return original if rewrite failed or still violates
        return user_message

    def _build_prompt_with_web(self, req: AgentRequest, web_resp: Dict[str, Any]) -> str:
        snippets = []
        if isinstance(web_resp, dict):
            hits = web_resp.get("results") or web_resp.get("items") or []
            for i, h in enumerate(hits[:5], 1):
                title = h.get("title") or h.get("headline") or ""
                snippet = h.get("snippet") or h.get("summary") or h.get("text") or ""
                url = h.get("url") or h.get("link") or ""
                display_link = h.get("displayLink") or h.get("display_link") or ""
                source_info = display_link if display_link else url.split('/')[2] if url else "Unknown source"
                snippets.append(f"[Result {i}] {title}\n{snippet}\nSource: {source_info} ({url})")

        snippet_text = "\n\n".join(snippets) if snippets else ""
        
        if not snippet_text:
            prompt = (
                f"You are an assistant helping tenant {req.tenant_id}.\n\n"
                f"## User Question\n{req.message}\n\n"
                f"## Context\n"
                f"No relevant web search results were found for this question.\n\n"
                f"## Your Task\n"
                f"Provide the best possible answer based on your general knowledge. "
                f"Be clear, accurate, and helpful. If you're uncertain about specific details, "
                f"acknowledge that and provide general guidance."
            )
        else:
            # Build prompt with Anthropic's recommended structure (clear sections with XML tags)
            prompt = (
                f"<system>\n"
                f"You are an assistant helping tenant {req.tenant_id}. "
                f"The Knowledge Base was checked first but no relevant information was found. "
                f"Web search results are provided below as a fallback.\n"
                f"</system>\n\n"
                f"<background_information>\n"
                f"## Important Rules for Web Search Responses\n"
                f"1. **KB-First Approach**: Always check Knowledge Base first. Web search is ONLY a fallback when KB has no relevant information.\n"
                f"2. **Keep it Short**: When using web search, keep responses short, factual, and neutral. Do NOT provide long explanations.\n"
                f"3. **No Professional Advice**: Do NOT provide long legal, medical, or highly detailed professional explanations. "
                f"If the topic involves legal, medical, financial, or safety-critical advice, provide a brief general explanation "
                f"and tell the user to consult a qualified professional.\n"
                f"4. **Clear Source Distinction**: Never present external web search information as part of the official Knowledge Base. "
                f"Always clarify that this information comes from external sources.\n"
                f"5. **Safety First**: For safety-critical topics, always recommend consulting qualified professionals.\n"
                f"</background_information>\n\n"
                f"<web_search_results>\n"
                f"The following search results were found for the user's question:\n\n"
                f"{snippet_text}\n"
                f"</web_search_results>\n\n"
                f"<user_question>\n"
                f"{req.message}\n"
                f"</user_question>\n\n"
                f"<instructions>\n"
                f"## Your Task\n"
                f"1. **Primary Goal**: Provide a short, factual answer using the web search results above.\n"
                f"2. **Keep it Brief**: Limit your response to 2-4 sentences. Do NOT provide lengthy explanations.\n"
                f"3. **Accuracy**: Prioritize information from authoritative sources (recognized websites, official sources, etc.).\n"
                f"4. **Professional Disclaimers**: For legal, medical, financial, or safety topics, include: "
                f"'For specific advice, please consult a qualified professional.'\n"
                f"5. **Source Clarity**: Start by mentioning this information comes from web search, not the Knowledge Base.\n"
                f"6. **Citation**: Briefly indicate which source(s) you used.\n"
                f"</instructions>\n\n"
                f"Provide a short, helpful answer now:"
            )

        return prompt

    def _format_tool_output(self, tool_name: str, output: Any, latency_ms: int) -> Dict[str, Any]:
        """
        Format tool output to conform to strict JSON schema.
        
        Args:
            tool_name: Name of the tool (rag, web, admin, llm)
            output: Raw tool output
            latency_ms: Actual latency in milliseconds
        
        Returns:
            Formatted output conforming to tool schema
        """
        if tool_name == "rag":
            # Format RAG output
            if isinstance(output, dict):
                results = output.get("results") or output.get("hits") or []
                # Ensure each result has required fields
                formatted_results = []
                for r in results:
                    if isinstance(r, dict):
                        formatted_results.append({
                            "text": r.get("text") or r.get("content") or str(r),
                            "similarity": float(r.get("similarity") or r.get("score") or 0.0),
                            "metadata": r.get("metadata") or {},
                            "doc_id": r.get("doc_id") or r.get("id")
                        })
                    else:
                        formatted_results.append({
                            "text": str(r),
                            "similarity": 0.5,
                            "metadata": {},
                            "doc_id": None
                        })
                
                # Calculate aggregate scores
                scores = [r["similarity"] for r in formatted_results if r["similarity"] > 0]
                avg_score = sum(scores) / len(scores) if scores else 0.0
                top_score = max(scores) if scores else 0.0
                
                return {
                    "results": formatted_results,
                    "query": output.get("query", ""),
                    "tenant_id": output.get("tenant_id", ""),
                    "hits_count": len(formatted_results),
                    "avg_score": round(avg_score, 3),
                    "top_score": round(top_score, 3),
                    "latency_ms": latency_ms
                }
            else:
                # Fallback for non-dict output
                return {
                    "results": [{"text": str(output), "similarity": 0.5, "metadata": {}, "doc_id": None}],
                    "query": "",
                    "tenant_id": "",
                    "hits_count": 1,
                    "avg_score": 0.5,
                    "top_score": 0.5,
                    "latency_ms": latency_ms
                }
        
        elif tool_name == "web":
            # Format Web output
            if isinstance(output, dict):
                results = output.get("results") or output.get("items") or []
                formatted_results = []
                for r in results:
                    if isinstance(r, dict):
                        formatted_results.append({
                            "title": r.get("title") or r.get("headline") or "",
                            "snippet": r.get("snippet") or r.get("summary") or r.get("text") or "",
                            "link": r.get("url") or r.get("link") or "",
                            "displayLink": r.get("displayLink") or r.get("display_link") or ""
                        })
                    else:
                        formatted_results.append({
                            "title": "",
                            "snippet": str(r),
                            "link": "",
                            "displayLink": ""
                        })
                
                return {
                    "results": formatted_results,
                    "query": output.get("query", ""),
                    "total_results": output.get("total_results") or output.get("totalResults") or len(formatted_results),
                    "latency_ms": latency_ms
                }
            else:
                return {
                    "results": [],
                    "query": "",
                    "total_results": 0,
                    "latency_ms": latency_ms
                }
        
        elif tool_name == "admin":
            # Format Admin output
            if isinstance(output, dict):
                violations = output.get("violations") or output.get("matches") or []
                formatted_violations = []
                for v in violations:
                    if isinstance(v, dict):
                        formatted_violations.append({
                            "rule_id": v.get("rule_id") or v.get("id") or "",
                            "rule_pattern": v.get("rule_pattern") or v.get("pattern") or "",
                            "severity": v.get("severity", "medium"),
                            "matched_text": v.get("matched_text") or v.get("text") or "",
                            "confidence": float(v.get("confidence", 1.0)),
                            "message_preview": v.get("message_preview") or v.get("preview") or ""
                        })
                
                return {
                    "violations": formatted_violations,
                    "checked": output.get("checked", True),
                    "rules_count": output.get("rules_count") or output.get("rulesCount") or len(formatted_violations),
                    "latency_ms": latency_ms
                }
            else:
                return {
                    "violations": [],
                    "checked": True,
                    "rules_count": 0,
                    "latency_ms": latency_ms
                }
        
        elif tool_name == "llm":
            # Format LLM output
            if isinstance(output, str):
                return {
                    "text": output,
                    "tokens_used": len(output) // 4,  # Rough estimate
                    "latency_ms": latency_ms,
                    "model": getattr(self.llm, 'model', 'unknown'),
                    "temperature": 0.0
                }
            elif isinstance(output, dict):
                return {
                    "text": output.get("text") or output.get("response") or str(output),
                    "tokens_used": output.get("tokens_used") or output.get("tokens") or 0,
                    "latency_ms": latency_ms,
                    "model": output.get("model") or getattr(self.llm, 'model', 'unknown'),
                    "temperature": output.get("temperature", 0.0)
                }
            else:
                return {
                    "text": str(output),
                    "tokens_used": 0,
                    "latency_ms": latency_ms,
                    "model": getattr(self.llm, 'model', 'unknown'),
                    "temperature": 0.0
                }
        
        # Unknown tool - return as-is
        return output if isinstance(output, dict) else {"output": str(output), "latency_ms": latency_ms}

    @staticmethod
    def _extract_hits(resp: Optional[Dict[str, Any]]) -> List[Dict[str, Any]]:
        if not isinstance(resp, dict):
            return []
        return resp.get("results") or resp.get("hits") or resp.get("items") or []

    def _summarize_hits(self, resp: Optional[Dict[str, Any]], limit: int = 3) -> List[str]:
        hits = self._extract_hits(resp)
        summaries = []
        for hit in hits[:limit]:
            if isinstance(hit, dict):
                snippet = hit.get("text") or hit.get("content") or hit.get("snippet") or ""
            else:
                snippet = str(hit)
            summaries.append(snippet[:160])
        return summaries

    async def run_parallel_tools(self, tasks: Dict[str, Any]) -> Dict[str, Any]:
        """
        Run multiple tools in parallel using asyncio.gather.
        
        Args:
            tasks: Dictionary mapping tool names to coroutines, e.g.:
                   {"rag": rag_coro, "web": web_coro}
        
        Returns:
            Dictionary mapping tool names to results, e.g.:
            {"rag": rag_result, "web": web_result}
            Exceptions are returned as values if a tool fails.
        """
        if not tasks:
            return {}
        
        names = list(tasks.keys())
        coros = [tasks[name] for name in names]
        
        # Run all coroutines in parallel, return exceptions instead of raising
        results = await asyncio.gather(*coros, return_exceptions=True)
        
        return {names[i]: results[i] for i in range(len(names))}

    @staticmethod
    def _first_query_for_tool(steps: List[Dict[str, Any]], tool_name: str, default_query: str) -> Optional[str]:
        for step in steps:
            if step.get("tool") == tool_name:
                input_data = step.get("input") or {}
                return input_data.get("query") or default_query
        return None