feat: integrate tracing throughout request pipeline

Instrument the full request flow with trace spans for debugging:

- Wrap expert delegations (librarian/biographer/housekeeper) in spans
- Add orchestrate and synthesize spans to TatlockAgent
- Trace Steward analysis in preprocessing
- Start/end traces in response service with context management
- Simplify router by moving context handling to service layer
- Include tracing router in debug mode
- Remove benchmark recording from tool_tracking and steward service

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

src/agents/steward/service.py

@@ -1,8 +1,8 @@
 """
 Steward service layer.
 
-Provides high-level interface for request analysis with logging,
-benchmarking, and error handling.
+Provides high-level interface for request analysis with logging
+and error handling.
 
 Parses plain text recommendations into structured data.
 Includes memory pre-fetch for user context injection.
@@ -10,7 +10,6 @@ Includes memory pre-fetch for user context injection.
 import re
 from typing import Any, Optional
 
-from src.core.benchmarks import PerformanceBenchmark, get_benchmark_store
 from src.core.household_registry import get_household_registry
 from src.core.logging_config import get_logger, log_operation
 from src.core.memory_service import memory_service
@@ -285,8 +284,7 @@ async def analyze_request(
     This is the main entry point for Steward analysis. It:
     1. Calls the Steward agent with full conversation history
     2. Logs the operation with timing
-    3. Records performance benchmarks to Redis
-    4. Returns structured recommendations
+    3. Returns structured recommendations
 
     Args:
         user_request: The current user message to analyze
@@ -365,23 +363,6 @@ async def analyze_request(
                 reasoning=analysis_text[:200],  # First 200 chars
             )
 
-            # Record performance benchmark
-            if log_ctx.get("duration_seconds"):
-                benchmark = PerformanceBenchmark(
-                    operation="steward_analysis",
-                    duration_seconds=log_ctx["duration_seconds"],
-                    success=True,
-                    recommendation_count=len(recommendation.recommended_capabilities),
-                    confidence=None,  # Could add confidence scoring in future
-                    conversation_id=conversation_id,
-                    metadata={
-                        "complexity": recommendation.estimated_complexity,
-                        "has_context": recommendation.conversation_context.has_previous_context,
-                        "missing_capabilities": recommendation.missing_capabilities is not None,
-                    },
-                )
-                await get_benchmark_store().record(benchmark)
-
             return recommendation
 
         except Exception as e: