docs: Add Production Readiness Assessment

Add honest gap analysis comparing DeepBoner to enterprise best practices
from Microsoft, AWS, Shopify, IBM, and OpenTelemetry guidance.

Key findings:
- Architecture: 8/10 (solid patterns, hierarchical orchestration)
- State Management: 8/10 (ResearchMemory, ContextVars isolation)
- Error Handling: 7/10 (exception hierarchy, fallbacks)
- Testing: 7/10 (unit tests, CI/CD)
- Observability: 3/10 (GAP - no tracing, no OpenTelemetry)
- Safety/Guardrails: 2/10 (GAP - no prompt injection protection)
- Cost Tracking: 1/10 (GAP - no token counting)

Verdict: Well-architected hackathon project with solid foundations,
lacking production observability and safety features.

Sources cited from industry leaders for credibility.

docs/README.md

@@ -8,6 +8,7 @@ Welcome to the DeepBoner documentation. This directory contains comprehensive do
 |------------|----------|
 | Get started quickly | [Getting Started](getting-started/installation.md) |
 | Understand the architecture | [Architecture Overview](architecture/overview.md) |
+| Assess production readiness | [Production Readiness](architecture/production-readiness.md) |
 | Set up for development | [Development Guide](development/testing.md) |
 | Deploy the application | [Deployment Guide](deployment/docker.md) |
 | Look up configuration | [Reference](reference/configuration.md) |
@@ -28,6 +29,7 @@ docs/
 ├── architecture/                 # System design documentation
 │   ├── overview.md               # High-level architecture
 │   ├── agent-tool-state-contracts.md  # Agent/Tool/State contracts (CRITICAL)
+│   ├── production-readiness.md   # Enterprise gap analysis (HONEST)
 │   ├── system-registry.md        # Service registry (canonical wiring)
 │   ├── workflow-diagrams.md      # Visual workflow diagrams
 │   ├── component-inventory.md    # Complete component catalog

docs/architecture/production-readiness.md

@@ -0,0 +1,359 @@
+# Production Readiness Assessment
+
+> **Last Updated**: 2025-12-06
+> **Purpose**: Honest assessment of DeepBoner against enterprise best practices
+> **Status**: Hackathon Complete → Production Gaps Identified
+
+This document compares DeepBoner's current implementation against industry best practices for multi-agent orchestration systems, based on guidance from Microsoft, AWS, IBM, and production experiences from Shopify and others.
+
+---
+
+## Executive Summary
+
+**Overall Assessment**: DeepBoner has **solid architectural foundations** but lacks **production observability and safety features** expected in enterprise deployments.
+
+| Category | Score | Status |
+|----------|-------|--------|
+| Architecture | 8/10 | Strong |
+| State Management | 8/10 | Strong |
+| Error Handling | 7/10 | Good |
+| Testing | 7/10 | Good |
+| Observability | 3/10 | **Gap** |
+| Safety/Guardrails | 2/10 | **Gap** |
+| Cost Tracking | 1/10 | **Gap** |
+
+---
+
+## What We Have (Implemented)
+
+### 1. Orchestration Patterns ✅
+
+**Industry Standard**: Hierarchical, collaborative, or handoff patterns for agent coordination.
+
+**DeepBoner Implementation**:
+- ✅ Manager → Agent hierarchy (Microsoft Agent Framework)
+- ✅ Blackboard pattern (ResearchMemory as shared cognitive state)
+- ✅ Dynamic agent selection by Manager
+- ✅ Fallback synthesis when agents fail
+
+**Evidence**: `src/orchestrators/advanced.py`, `src/services/research_memory.py`
+
+### 2. Error Surfacing ✅
+
+**Industry Standard**: "Surface errors instead of hiding them so downstream agents and orchestrator logic can respond appropriately." — Microsoft
+
+**DeepBoner Implementation**:
+- ✅ Exception hierarchy (DeepBonerError → SearchError, JudgeError, etc.)
+- ✅ Errors yield AgentEvent(type="error") for UI visibility
+- ✅ Fallback synthesis on timeout/max rounds
+- ✅ Judge returns fallback assessment on LLM failure
+
+**Evidence**: `src/utils/exceptions.py`, `src/orchestrators/advanced.py`
+
+### 3. State Isolation ✅
+
+**Industry Standard**: "Design agents to be as isolated as practical from each other."
+
+**DeepBoner Implementation**:
+- ✅ ContextVars for per-request isolation
+- ✅ MagenticState wrapper prevents cross-request leakage
+- ✅ ResearchMemory scoped to single query
+
+**Evidence**: `src/agents/state.py`
+
+### 4. Break Conditions ✅
+
+**Industry Standard**: Prevent infinite loops, implement timeouts, use circuit breakers.
+
+**DeepBoner Implementation**:
+- ✅ Max rounds (5 default)
+- ✅ Timeout (600s default)
+- ✅ Judge approval as primary break condition
+- ✅ Max stall count (3)
+- ⚠️ No formal circuit breaker pattern
+
+**Evidence**: `src/orchestrators/advanced.py`
+
+### 5. Structured Outputs ✅
+
+**Industry Standard**: Use structured, validated outputs to prevent hallucination.
+
+**DeepBoner Implementation**:
+- ✅ Pydantic models for all data types
+- ✅ Validation on all inputs/outputs
+- ✅ PydanticAI for structured LLM outputs
+- ✅ Citation validation in ReportAgent
+
+**Evidence**: `src/utils/models.py`, `src/agent_factory/judges.py`
+
+### 6. Testing ✅
+
+**Industry Standard**: "Continuous testing pipelines that validate agent reliability."
+
+**DeepBoner Implementation**:
+- ✅ Unit tests with mocking (respx, pytest-mock)
+- ✅ Test markers (unit, integration, slow, e2e)
+- ✅ Coverage tracking
+- ✅ CI/CD pipeline
+- ⚠️ No formal LLM output evaluation framework
+
+**Evidence**: `tests/`, `.github/workflows/ci.yml`
+
+---
+
+## What We're Missing (Gaps)
+
+### 1. Observability/Tracing ❌
+
+**Industry Standard**: "Implement comprehensive tracing that captures every decision point from initial user input through final action execution." — [OpenTelemetry](https://opentelemetry.io/blog/2025/ai-agent-observability/)
+
+**Current State**:
+- ✅ AgentEvents for UI streaming
+- ✅ structlog for logging
+- ❌ No OpenTelemetry integration
+- ❌ No distributed tracing
+- ❌ No trace IDs for debugging
+- ❌ No span hierarchy (orchestrator → agent → tool)
+
+**Impact**: Cannot trace a single request through the entire system. Debugging production issues requires log correlation.
+
+**Recommendation**: Add OpenTelemetry instrumentation or integrate with observability platform (Langfuse, Datadog LLM Observability).
+
+**Effort**: L (Large)
+
+---
+
+### 2. Token/Cost Tracking ❌
+
+**Industry Standard**: "Track token usage—since AI providers charge by token, tracking this metric directly impacts costs." — [LakeFSs](https://lakefs.io/blog/llm-observability-tools/)
+
+**Current State**:
+- ❌ No token counting
+- ❌ No cost estimation per query
+- ❌ No budget limits
+- ❌ No usage dashboards
+
+**Impact**: Cannot estimate or control costs. No visibility into expensive queries.
+
+**Recommendation**: Add token counting to LLM clients, emit as metrics.
+
+**Effort**: M (Medium)
+
+---
+
+### 3. Guardrails/Input Validation ❌
+
+**Industry Standard**: "Guardrails AI enforces safety and compliance by validating every LLM interaction through configurable input and output validators." — [Guardrails AI](https://www.guardrailsai.com/)
+
+**Current State**:
+- ❌ No prompt injection detection
+- ❌ No PII detection/redaction
+- ❌ No toxicity filtering
+- ❌ No jailbreak protection
+- ✅ Basic Pydantic validation (length limits, types)
+
+**Impact**: System trusts user input directly. Vulnerable to prompt injection attacks.
+
+**Recommendation**: Add input guardrails before LLM calls.
+
+**Effort**: M (Medium)
+
+---
+
+### 4. Formal Evaluation Framework ⚠️
+
+**Industry Standard**: "Build multiple LLM judges for different aspects of agent performance, and align judges with human judgment." — [Shopify Engineering](https://shopify.engineering/building-production-ready-agentic-systems)
+
+**Current State**:
+- ✅ JudgeAgent evaluates evidence quality
+- ❌ No meta-evaluation of JudgeAgent accuracy
+- ❌ No comparison to human judgment
+- ❌ No A/B testing framework
+- ❌ No evaluation datasets
+
+**Impact**: Cannot measure if Judge decisions are correct. No ground truth comparison.
+
+**Recommendation**: Create evaluation datasets, implement meta-evaluation.
+
+**Effort**: L (Large)
+
+---
+
+### 5. Circuit Breaker Pattern ⚠️
+
+**Industry Standard**: "Consider circuit breaker patterns for agent dependencies." — Microsoft
+
+**Current State**:
+- ✅ Timeout for entire workflow
+- ✅ Max consecutive failures in HF Judge (3)
+- ⚠️ No formal circuit breaker for external APIs
+- ⚠️ No graceful degradation per tool
+
+**Impact**: If PubMed is down, entire search fails rather than continuing with other sources.
+
+**Recommendation**: Add per-tool circuit breakers, continue with partial results.
+
+**Effort**: M (Medium)
+
+---
+
+### 6. Drift Detection ❌
+
+**Industry Standard**: "Monitoring key metrics of model drift—such as changes in response patterns or variations in output quality." — Industry consensus
+
+**Current State**:
+- ❌ No baseline metrics
+- ❌ No output pattern tracking
+- ❌ No automated drift alerts
+- ❌ No quality regression detection
+
+**Impact**: Cannot detect if model updates degrade quality.
+
+**Recommendation**: Log output patterns, establish baselines, alert on deviation.
+
+**Effort**: L (Large)
+
+---
+
+### 7. Human-in-the-Loop ⚠️
+
+**Industry Standard**: "Maintain a human-in-the-loop with escalations for human review on high-risk decisions." — [McKinsey](https://www.mckinsey.com/capabilities/quantumblack/our-insights/one-year-of-agentic-ai-six-lessons-from-the-people-doing-the-work)
+
+**Current State**:
+- ⚠️ User reviews final report (implicit)
+- ❌ No explicit escalation for uncertain decisions
+- ❌ No "confidence too low" breakout to human
+- ❌ No approval workflow
+
+**Impact**: Low-confidence results shown without warning.
+
+**Recommendation**: Add confidence thresholds for human escalation.
+
+**Effort**: S (Small)
+
+---
+
+## Gap Prioritization
+
+### Critical (Block Production)
+
+None. The system is functional for demo/research use.
+
+### High (Before Enterprise Deployment)
+
+| Gap | Why |
+|-----|-----|
+| Observability/Tracing | Cannot debug production issues |
+| Guardrails | Vulnerable to prompt injection |
+| Token Tracking | Cannot control costs |
+
+### Medium (Production Hardening)
+
+| Gap | Why |
+|-----|-----|
+| Circuit Breakers | Partial failures cascade |
+| Formal Evaluation | Cannot measure accuracy |
+| Human Escalation | Low-confidence results unhandled |
+
+### Low (Future Enhancement)
+
+| Gap | Why |
+|-----|-----|
+| Drift Detection | Long-term quality monitoring |
+| A/B Testing | Optimization infrastructure |
+
+---
+
+## Comparison to Industry Standards
+
+### Microsoft Agent Framework Checklist
+
+| Requirement | Status |
+|-------------|--------|
+| Surface errors | ✅ |
+| Circuit breakers | ⚠️ Partial |
+| Agent isolation | ✅ |
+| Checkpoint/recovery | ⚠️ Timeout fallback only |
+| Security mechanisms | ❌ No guardrails |
+| Rate limit handling | ⚠️ Basic retry |
+
+### AWS Multi-Agent Guidance
+
+| Requirement | Status |
+|-------------|--------|
+| Supervisor agent | ✅ Manager |
+| Task delegation | ✅ |
+| Response aggregation | ✅ ResearchMemory |
+| Built-in monitoring | ❌ |
+| Serverless scaling | ❌ Single instance |
+
+### Shopify Production Lessons
+
+| Lesson | Status |
+|--------|--------|
+| Stay simple | ✅ |
+| Avoid premature multi-agent | ✅ Right-sized |
+| Evaluation framework | ❌ Missing |
+| "Vibe testing" is insufficient | ⚠️ Judge is vibe-based |
+| 40% budget for post-launch | N/A (hackathon) |
+
+---
+
+## Honest Assessment
+
+**Is DeepBoner enterprise-ready?** No.
+
+**Is DeepBoner a hobbled-together mess?** Also no.
+
+**What is it?** A well-architected hackathon project with solid foundations that lacks production observability and safety features.
+
+**What would enterprises laugh at?**
+1. No tracing (how do you debug?)
+2. No guardrails (what about security?)
+3. No cost tracking (how do you budget?)
+
+**What would enterprises respect?**
+1. Clear architecture patterns
+2. Comprehensive documentation
+3. Strong typing with Pydantic
+4. Honest gap analysis (this document)
+5. Exception hierarchy and error handling
+
+---
+
+## Next Steps (If Going to Production)
+
+### Phase 1: Observability (2-3 weeks)
+1. Add OpenTelemetry instrumentation
+2. Emit trace IDs in AgentEvents
+3. Add token counting to LLM clients
+
+### Phase 2: Safety (1-2 weeks)
+1. Add input validation layer
+2. Implement prompt injection detection
+3. Add confidence thresholds for escalation
+
+### Phase 3: Resilience (1-2 weeks)
+1. Add per-tool circuit breakers
+2. Improve rate limit handling
+3. Add health checks
+
+### Phase 4: Evaluation (2-4 weeks)
+1. Create evaluation datasets
+2. Implement meta-evaluation of Judge
+3. Establish quality baselines
+
+---
+
+## Sources
+
+- [Microsoft AI Agent Design Patterns](https://learn.microsoft.com/en-us/azure/architecture/ai-ml/guide/ai-agent-design-patterns)
+- [AWS Multi-Agent Orchestration Guidance](https://aws.amazon.com/solutions/guidance/multi-agent-orchestration-on-aws/)
+- [Shopify: Building Production-Ready Agentic Systems](https://shopify.engineering/building-production-ready-agentic-systems)
+- [OpenTelemetry: AI Agent Observability](https://opentelemetry.io/blog/2025/ai-agent-observability/)
+- [IBM: AI Agent Orchestration](https://www.ibm.com/think/topics/ai-agent-orchestration)
+- [McKinsey: Six Lessons from Agentic AI Deployment](https://www.mckinsey.com/capabilities/quantumblack/our-insights/one-year-of-agentic-ai-six-lessons-from-the-people-doing-the-work)
+
+---
+
+*This document is intentionally honest. Acknowledging gaps is a sign of engineering maturity, not weakness.*