# =============================================================
# 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
from typing import List, Dict, Any, Optional

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
import time


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.analytics = AnalyticsStore()

    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]
        })

        # 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
            )
            
            return AgentResponse(
                text=llm_response,
                decision=decision,
                tool_traces=[{"redflags": [m.__dict__ for m in blocking_rules]}],
                reasoning_trace=reasoning_trace
            )
        
        # 2) ONLY IF NO RULES MATCHED: Proceed with normal flow (intent classification, RAG, etc.)
        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)
        rag_prefetch = None
        rag_results = []
        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

        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 in context
        ctx = {
            "tenant_id": req.tenant_id,
            "rag_results": rag_results,
            "tool_scores": tool_scores
        }
        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":
                    rag_start = time.time()
                    rag_resp = await self.mcp.call_rag(req.tenant_id, decision.tool_input.get("query") if decision.tool_input else req.message)
                    rag_latency_ms = int((time.time() - rag_start) * 1000)
                    tools_used.append("rag")
                    
                    tool_traces.append({"tool": "rag", "response": rag_resp})
                    hits = self._extract_hits(rag_resp)
                    
                    # Log RAG search and tool usage
                    hits_count = len(hits)
                    avg_score = None
                    top_score = None
                    if hits:
                        scores = [h.get("score", 0.0) for h in hits 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
                    )
                    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,
                        "summary": self._summarize_hits(rag_resp, limit=2),
                        "latency_ms": rag_latency_ms
                    })
                    prompt = self._build_prompt_with_rag(req, rag_resp)
                    
                    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":
                    web_start = time.time()
                    web_resp = await self.mcp.call_web(req.tenant_id, decision.tool_input.get("query") if decision.tool_input else req.message)
                    web_latency_ms = int((time.time() - web_start) * 1000)
                    tools_used.append("web")
                    
                    tool_traces.append({"tool": "web", "response": web_resp})
                    hits_count = len(self._extract_hits(web_resp))
                    
                    self.analytics.log_tool_usage(
                        tenant_id=req.tenant_id,
                        tool_name="web",
                        latency_ms=web_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_resp, limit=2),
                        "latency_ms": web_latency_ms
                    })
                    prompt = self._build_prompt_with_web(req, web_resp)
                    
                    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
                    )
                    
                    tool_traces.append({"tool": "admin", "response": admin_resp})
                    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":
                    llm_start = time.time()
                    llm_out = await self.llm.simple_call(req.message, temperature=req.temperature)
                    llm_latency_ms = int((time.time() - llm_start) * 1000)
                    tools_used.append("llm")
                    
                    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": "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
        try:
            llm_start = time.time()
            llm_out = await self.llm.simple_call(req.message, temperature=req.temperature)
            llm_latency_ms = int((time.time() - llm_start) * 1000)
            tools_used = ["llm"]
            estimated_tokens = len(llm_out) // 4 + len(req.message) // 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
        )
        
        return AgentResponse(
            text=llm_out,
            decision=AgentDecision(action="respond", tool=None, tool_input=None, reason="default_llm"),
            reasoning_trace=reasoning_trace
        )

    def _build_prompt_with_rag(self, req: AgentRequest, rag_resp: Dict[str, Any]) -> str:
        snippets = []
        if isinstance(rag_resp, dict):
            hits = rag_resp.get("results") or rag_resp.get("hits") or []
            for h in hits[:5]:
                txt = h.get("text") or h.get("content") or str(h)
                snippets.append(txt)

        snippet_text = "\n---\n".join(snippets) or ""
        prompt = (
            f"You are an assistant helping tenant {req.tenant_id}. Use the following retrieved documents to answer the user's question.\n"
            f"Documents:\n{snippet_text}\n\n"
            f"User question: {req.message}\nProvide a concise, accurate answer and cite the source snippets where appropriate."
        )
        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 and synthesize results with LLM.
        """
        rag_data = None
        web_data = None
        admin_data = None
        collected_data = []

        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":
                    # Reuse pre-fetched RAG if available, otherwise fetch
                    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:
                        rag_resp = await self.mcp.call_rag(req.tenant_id, query)
                        tool_traces.append({"tool": "rag", "response": rag_resp})
                    rag_data = rag_resp
                    reasoning_trace.append({
                        "step": "tool_execution",
                        "tool": "rag",
                        "hit_count": len(self._extract_hits(rag_resp)),
                        "summary": self._summarize_hits(rag_resp, limit=2)
                    })
                    # Extract snippets for prompt
                    if isinstance(rag_resp, dict):
                        hits = rag_resp.get("results") or rag_resp.get("hits") or []
                        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:
                        web_resp = await self.mcp.call_web(req.tenant_id, query)
                        tool_traces.append({"tool": "web", "response": web_resp})
                    web_data = web_resp
                    reasoning_trace.append({
                        "step": "tool_execution",
                        "tool": "web",
                        "hit_count": len(self._extract_hits(web_resp)),
                        "summary": self._summarize_hits(web_resp, limit=2)
                    })
                    # Extract snippets for prompt
                    if isinstance(web_resp, dict):
                        hits = web_resp.get("results") or web_resp.get("items") or []
                        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
                    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 ""
        
        if data_section:
            prompt = (
                f"You are an assistant helping tenant {req.tenant_id}.\n\n"
                f"The following information has been gathered from multiple sources:\n\n"
                f"{data_section}\n\n"
                f"User question: {req.message}\n\n"
                f"Provide a comprehensive, accurate answer using the information above. "
                f"Cite sources where appropriate (RAG for internal docs, WEB for online sources)."
            )
        else:
            # No data collected, just answer the question
            prompt = req.message

        # Final LLM synthesis
        try:
            llm_out = await self.llm.simple_call(prompt, temperature=req.temperature)
            return AgentResponse(
                text=llm_out,
                decision=decision,
                tool_traces=tool_traces,
                reasoning_trace=reasoning_trace + [{
                    "step": "llm_response",
                    "mode": "multi_step"
                }]
            )
        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)
                }]
            )

    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 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 ""
                snippets.append(f"{title}\n{snippet}\nSource: {url}")

        snippet_text = "\n---\n".join(snippets) or ""
        prompt = (
            f"You are an assistant with access to recent web search results. Use the following results to answer.\n{snippet_text}\n\n"
            f"User question: {req.message}\nAnswer succinctly and indicate which results you used."
        )
        return prompt

    @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

    @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