feat: implement Phase 2 two-tier architecture with Steward
Add comprehensive two-tier architecture where Steward analyzes requests and Tatlock executes with scoped tools. Includes full infrastructure for request preprocessing, tool tracking, benchmarking, and streaming. **Added:** - Steward agent for request analysis and capability recommendation - Household Registry for centralized capability management - Request preprocessing pipeline (Steward → Tatlock flow) - Tool usage tracking and benchmarking system - Streaming transparency (Steward reasoning visible in streams) - Structured logging with operation timing - Redis benchmark storage with 30-day expiry - Benchmark analysis CLI tools **Infrastructure:** - src/agents/steward/ - Steward agent implementation - src/agents/tatlock_core/ - Tatlock capability domain - src/core/preprocessing.py - Request preprocessing pipeline - src/core/tool_tracking.py - Tool call tracking - src/core/benchmarks.py - Benchmark recording system - src/core/household_registry.py - Capability registry - src/core/startup.py - Application startup coordination - src/core/logging_config.py - Structured logging setup **Integration:** - Responses API uses Steward for Tatlock requests - Chat Completions wraps Responses API for OpenAI compatibility - Streaming coordinator supports Steward + Tatlock flow - Tool scoping per request based on Steward recommendations **Testing:** - Integration tests for Steward-Tatlock flow - Benchmark and registry unit tests - Steward streaming tests See PHASE2_PLAN.md and PHASE2_COMPLETE.md for detailed documentation. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
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Claude Sonnet 4.5
Executable
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"""
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Benchmark script for the Steward agent.
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Tests Steward's request analysis performance with various scenarios
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to ensure it meets latency targets:
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- Target max: 5 seconds
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- Target average: ~1.67 seconds
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Usage:
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python scripts/benchmark_steward.py [--iterations N] [--verbose]
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"""
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import argparse
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import asyncio
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import statistics
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from datetime import datetime
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from typing import List
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from src.agents.steward import analyze_request
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from src.core.startup import initialize_application
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class BenchmarkResult:
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"""Results from a single benchmark run."""
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def __init__(self, scenario: str, duration: float, success: bool, error: str = None):
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self.scenario = scenario
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self.duration = duration
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self.success = success
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self.error = error
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async def benchmark_scenario(
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name: str,
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request: str,
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history: list[dict],
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iterations: int = 10
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) -> List[BenchmarkResult]:
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"""
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Benchmark a specific scenario.
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Args:
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name: Scenario name
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request: User request to analyze
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history: Conversation history
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iterations: Number of times to run
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Returns:
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List of benchmark results
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"""
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results = []
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print(f"\n📊 Benchmarking: {name}")
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print(f" Request: {request[:50]}{'...' if len(request) > 50 else ''}")
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print(f" History length: {len(history)} turns")
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print(f" Iterations: {iterations}")
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for i in range(iterations):
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try:
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start = datetime.now()
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await analyze_request(request, history)
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duration = (datetime.now() - start).total_seconds()
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results.append(BenchmarkResult(name, duration, True))
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# Progress indicator
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print(".", end="", flush=True)
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except Exception as e:
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duration = (datetime.now() - start).total_seconds()
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results.append(BenchmarkResult(name, duration, False, str(e)))
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print("E", end="", flush=True)
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print() # New line after progress
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return results
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def analyze_results(results: List[BenchmarkResult], scenario_name: str):
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"""
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Analyze and display benchmark results.
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Args:
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results: List of benchmark results
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scenario_name: Name of the scenario
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"""
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successful = [r for r in results if r.success]
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failed = [r for r in results if not r.success]
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if not successful:
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print(f"\n❌ {scenario_name}: All runs failed!")
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for r in failed[:3]: # Show first 3 errors
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print(f" Error: {r.error}")
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return
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durations = [r.duration for r in successful]
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min_duration = min(durations)
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max_duration = max(durations)
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avg_duration = statistics.mean(durations)
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median_duration = statistics.median(durations)
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# Calculate percentiles
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sorted_durations = sorted(durations)
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p95_idx = int(len(sorted_durations) * 0.95)
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p99_idx = int(len(sorted_durations) * 0.99)
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p95 = sorted_durations[p95_idx] if p95_idx < len(sorted_durations) else max_duration
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p99 = sorted_durations[p99_idx] if p99_idx < len(sorted_durations) else max_duration
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# Targets
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target_max = 5.0
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target_avg = 1.67
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# Status emojis
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max_status = "✅" if max_duration <= target_max else "⚠️"
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avg_status = "✅" if avg_duration <= target_avg else "⚠️"
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print(f"\n Results ({len(successful)}/{len(results)} successful):")
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print(f" Min: {min_duration:6.3f}s")
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print(f" Avg: {avg_duration:6.3f}s {avg_status} (target: ≤{target_avg}s)")
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print(f" Median: {median_duration:6.3f}s")
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print(f" P95: {p95:6.3f}s")
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print(f" P99: {p99:6.3f}s")
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print(f" Max: {max_duration:6.3f}s {max_status} (target: ≤{target_max}s)")
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if failed:
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print(f" Failed: {len(failed)} runs")
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return {
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"min": min_duration,
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"avg": avg_duration,
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"median": median_duration,
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"p95": p95,
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"p99": p99,
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"max": max_duration,
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"success_rate": len(successful) / len(results) * 100,
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}
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async def run_benchmarks(iterations: int = 10, verbose: bool = False):
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"""
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Run comprehensive Steward benchmarks.
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Args:
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iterations: Number of iterations per scenario
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verbose: Enable verbose output
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"""
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print("=" * 60)
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print("🔬 Steward Performance Benchmark")
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print("=" * 60)
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print(f"\nTargets:")
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print(f" - Maximum response time: ≤5.0s")
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print(f" - Average response time: ≤1.67s")
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print(f"\nIterations per scenario: {iterations}")
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# Initialize application
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print("\n🚀 Initializing application...")
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initialize_application()
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all_stats = {}
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# Scenario 1: Simple greeting (no capabilities needed)
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results = await benchmark_scenario(
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"Simple Greeting",
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"Hello!",
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[],
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iterations
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)
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all_stats["simple_greeting"] = analyze_results(results, "Simple Greeting")
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# Scenario 2: Single tool request (calculator)
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results = await benchmark_scenario(
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"Calculator Request",
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"What's sqrt(144) + 25?",
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[],
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iterations
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)
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all_stats["calculator"] = analyze_results(results, "Calculator Request")
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# Scenario 3: Web search request
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results = await benchmark_scenario(
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"Web Search Request",
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"Search for the latest Python 3.12 features",
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[],
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iterations
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)
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all_stats["web_search"] = analyze_results(results, "Web Search Request")
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# Scenario 4: Request with conversation history (short)
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short_history = [
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{"role": "user", "content": "What's 15 times 7?"},
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{"role": "assistant", "content": "105"},
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]
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results = await benchmark_scenario(
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"With Short History",
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"And what's that divided by 3?",
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short_history,
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iterations
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)
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all_stats["short_history"] = analyze_results(results, "With Short History")
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# Scenario 5: Request with longer conversation history
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long_history = [
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{"role": "user", "content": f"Question {i}"} if i % 2 == 0
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else {"role": "assistant", "content": f"Answer {i}"}
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for i in range(20)
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]
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results = await benchmark_scenario(
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"With Long History",
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"What was the first question I asked?",
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long_history,
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iterations
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)
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all_stats["long_history"] = analyze_results(results, "With Long History")
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# Scenario 6: Complex request
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results = await benchmark_scenario(
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"Complex Request",
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"Calculate the compound interest on $5000 at 4.5% over 10 years, "
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"then search for current savings account rates to compare",
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[],
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iterations
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)
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all_stats["complex"] = analyze_results(results, "Complex Request")
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# Scenario 7: Missing capabilities
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results = await benchmark_scenario(
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"Missing Capabilities",
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"Generate an image of a sunset over mountains",
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[],
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iterations
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)
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all_stats["missing_caps"] = analyze_results(results, "Missing Capabilities")
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# Summary
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print("\n" + "=" * 60)
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print("📈 SUMMARY")
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print("=" * 60)
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# Calculate overall stats
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all_avgs = [stats["avg"] for stats in all_stats.values() if stats]
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all_maxs = [stats["max"] for stats in all_stats.values() if stats]
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if all_avgs:
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overall_avg = statistics.mean(all_avgs)
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overall_max = max(all_maxs)
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avg_status = "✅" if overall_avg <= 1.67 else "⚠️"
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max_status = "✅" if overall_max <= 5.0 else "⚠️"
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print(f"\nOverall Performance:")
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print(f" Average of averages: {overall_avg:.3f}s {avg_status}")
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print(f" Maximum observed: {overall_max:.3f}s {max_status}")
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# Performance verdict
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print(f"\n{'=' * 60}")
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if overall_avg <= 1.67 and overall_max <= 5.0:
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print("✅ PERFORMANCE TARGETS MET!")
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print(f" The Steward is operating within target parameters.")
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elif overall_max <= 5.0:
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print("⚠️ PARTIAL SUCCESS")
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print(f" Max response time is good, but average is above target.")
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print(f" Average: {overall_avg:.3f}s (target: ≤1.67s)")
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print(f"\n Recommendations:")
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print(f" - Consider using a faster model")
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print(f" - Optimize system prompt length")
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print(f" - Review tool call limits")
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else:
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print("❌ PERFORMANCE TARGETS NOT MET")
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print(f" Max: {overall_max:.3f}s (target: ≤5.0s)")
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print(f" Avg: {overall_avg:.3f}s (target: ≤1.67s)")
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print(f"\n Recommendations:")
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print(f" - Switch to a faster model (current: mistral-nemo)")
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print(f" - Reduce system prompt complexity")
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print(f" - Limit tool calls (currently limited to 3)")
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print(f" - Consider caching household registry responses")
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print("=" * 60)
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async def main():
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"""Main entry point."""
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parser = argparse.ArgumentParser(description="Benchmark Steward agent performance")
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parser.add_argument(
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"--iterations",
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type=int,
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default=10,
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help="Number of iterations per scenario (default: 10)"
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)
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parser.add_argument(
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"--verbose",
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action="store_true",
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help="Enable verbose output"
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)
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args = parser.parse_args()
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await run_benchmarks(iterations=args.iterations, verbose=args.verbose)
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if __name__ == "__main__":
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asyncio.run(main())
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