release: v3.24.0 - Agent Evaluation Framework
Major addition: Complete agent evaluation framework with production-ready template. ## Added - **Resource Evaluation**: nao framework (score 3/5) - Identified critical gap: agent evaluation not documented - Technical challenge adjusted score 2/5 → 3/5 - All claims fact-checked (TypeScript 58.9%, Python 38.5%) - **Guide Section**: Agent Evaluation (guide/agent-evaluation.md, ~3K tokens) - Metrics: response quality, tool usage, performance, satisfaction - Patterns: logging hooks, unit tests, A/B testing, feedback loops - Example: analytics agent with built-in metrics - Tools: nao framework reference, Claude Code hooks integration - **AI Ecosystem**: Section 8.2 Domain-Specific Agent Frameworks - nao (Analytics Agents): Database-agnostic, built-in evaluation - Transposable patterns: context builder, evaluation hooks, DB integrations - **Template**: Analytics Agent with Evaluation (5 files, ~1K lines) - README: setup, usage, troubleshooting - Agent: SQL generator with evaluation criteria, safety rules - Hook: automated metrics logging (safety, performance, errors) - Script: analysis with stats, safety reports, recommendations - Report template: monthly evaluation format ## Changed - Agent Evaluation Guide: updated template references, verified links - Landing Site: templates count 110 → 114 - Version: 3.23.5 → 3.24.0 Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
This commit is contained in:
Florian BRUNIAUX
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@ -16,6 +16,7 @@ Core documentation for mastering Claude Code.
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| [architecture.md](./architecture.md) | How Claude Code works internally (master loop, tools, context) | 25 min |
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| [learning-with-ai.md](./learning-with-ai.md) | Guide for juniors on using AI without losing skills | 15 min |
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| [adoption-approaches.md](./adoption-approaches.md) | Implementation strategies for teams | 15 min |
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| [agent-evaluation.md](./agent-evaluation.md) | **Agent quality metrics**: Measuring custom agent effectiveness with hooks, tests, and feedback loops | 20 min |
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| [data-privacy.md](./data-privacy.md) | Data retention and privacy guide | 10 min |
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| [observability.md](./observability.md) | Session monitoring and cost tracking | 15 min |
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| [methodologies.md](./methodologies.md) | 15 development methodologies reference (TDD, SDD, BDD, etc.) | 20 min |
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guide/agent-evaluation.md
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guide/agent-evaluation.md
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# Agent Evaluation
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**Quick nav**: [Why Evaluate?](#why-evaluate-agents) · [Metrics to Track](#metrics-to-track) · [Implementation](#implementation-patterns) · [Example](#example-agent-with-evaluation) · [Tools](#tools--references)
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---
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## Why Evaluate Agents?
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When you create custom agents in `.claude/agents/`, you're encoding specialized expertise into reusable workflows. But how do you know if your agents are actually effective?
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**Without evaluation**, you're building blind:
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- ❌ No way to measure if agent responses are improving or degrading over time
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- ❌ Can't compare different agent configurations objectively
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- ❌ Difficult to identify which aspects of agent context/instructions need refinement
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- ❌ No data to justify investment in agent development
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**With evaluation**, you iterate with confidence:
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- ✅ Quantify agent quality through metrics (response time, accuracy, tool usage)
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- ✅ A/B test different agent configurations with measurable outcomes
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- ✅ Identify patterns in successful vs failed interactions
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- ✅ Build feedback loops for continuous improvement
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**Core principle**: Agents are code. Like all code, they need tests, metrics, and observability.
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---
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## Metrics to Track
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### 1. Response Quality Metrics
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**What to measure**:
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- **Task completion rate**: Did the agent accomplish the stated goal?
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- **Correctness**: Were the agent's outputs factually accurate?
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- **Relevance**: Did the response stay on-topic and address the actual question?
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- **Hallucination rate**: How often did the agent invent information?
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**How to track**:
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```bash
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# Post-response hook: .claude/hooks/log-response-quality.sh
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# Triggered after each agent response
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# Log structure:
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{
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"timestamp": "2026-02-10T14:32:00Z",
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"agent_id": "backend-architect",
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"task_completed": true,
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"correctness_score": 4.5, # User rating 1-5
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"hallucinations": 0,
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"response_tokens": 1250
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}
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```
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**Implementation tip**: Use user feedback prompts (thumbs up/down) or automated checks (test suite passing after agent code generation).
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---
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### 2. Tool Usage Metrics
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**What to measure**:
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- **Tool call success rate**: Percentage of tool calls that executed without errors
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- **Tool selection accuracy**: Did agent choose the right tool for the task?
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- **Tool call efficiency**: Minimum calls to achieve goal (avoid unnecessary reads/searches)
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- **Error recovery**: Did agent handle tool failures gracefully?
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**How to track**:
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```bash
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# Post-tool-use hook: .claude/hooks/log-tool-usage.sh
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# Triggered after each tool call
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# Log structure:
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{
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"timestamp": "2026-02-10T14:32:05Z",
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"agent_id": "backend-architect",
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"tool_name": "Read",
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"tool_success": true,
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"tool_parameters": {"file_path": "src/auth.ts"},
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"execution_time_ms": 45
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}
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```
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**Implementation tip**: Use Claude Code hooks system (see `examples/hooks/`) to automatically log tool calls.
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---
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### 3. Performance Metrics
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**What to measure**:
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- **Response time**: Total time from user prompt to complete response
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- **Token efficiency**: Input/output tokens used per task
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- **Context utilization**: How much of context window was used?
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- **Cost per task**: API cost for the full interaction
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**How to track**:
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```bash
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# Session-end hook: .claude/hooks/log-performance.sh
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# Triggered at end of session
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# Log structure:
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{
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"timestamp": "2026-02-10T14:35:00Z",
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"agent_id": "backend-architect",
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"session_duration_s": 180,
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"input_tokens": 3500,
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"output_tokens": 2800,
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"total_cost_usd": 0.15,
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"context_utilization": 0.42
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}
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```
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**Implementation tip**: Parse Claude Code session logs or use MCP observability tools.
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---
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### 4. User Satisfaction Metrics
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**What to measure**:
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- **Explicit feedback**: User ratings, comments, bug reports
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- **Implicit signals**: Did user accept agent's suggestions? Did they retry the prompt?
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- **Adoption rate**: How often is this agent used vs alternatives?
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- **Retention**: Do users return to this agent for similar tasks?
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**How to track**:
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```bash
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# Manual feedback collection
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# After agent completes task, prompt user:
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"Rate this agent's performance (1-5): _"
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# Log:
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{
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"timestamp": "2026-02-10T14:35:10Z",
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"agent_id": "backend-architect",
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"user_rating": 5,
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"user_comment": "Perfect analysis of auth flow",
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"would_use_again": true
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}
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```
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**Implementation tip**: Add feedback prompts to agent templates or use post-session surveys.
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---
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## Implementation Patterns
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### Pattern 1: Logging Hook System
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**Use Case**: Automatically track all agent interactions without manual intervention
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**Setup**:
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```bash
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# .claude/hooks/post-tool-use.sh
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#!/bin/bash
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# Triggered after every tool call
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AGENT_ID=$(echo "$CLAUDE_AGENT_ID" | jq -r)
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TOOL_NAME=$(echo "$CLAUDE_TOOL_NAME" | jq -r)
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TOOL_SUCCESS=$(echo "$CLAUDE_TOOL_SUCCESS" | jq -r)
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# Append to metrics log
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echo "{\"timestamp\":\"$(date -Iseconds)\",\"agent\":\"$AGENT_ID\",\"tool\":\"$TOOL_NAME\",\"success\":$TOOL_SUCCESS}" \
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>> .claude/logs/agent-metrics.jsonl
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```
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**Pros**: Zero manual overhead, complete coverage, time-series data
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**Cons**: Requires parsing Claude Code environment variables (may change across versions)
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---
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### Pattern 2: Agent Unit Tests
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**Use Case**: Regression testing to ensure agent improvements don't break existing capabilities
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**Setup**:
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```bash
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# tests/agents/backend-architect.test.sh
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#!/bin/bash
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# Test 1: Agent correctly identifies hexagonal architecture layers
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echo "Test: Hexagonal architecture analysis"
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RESULT=$(claude agent backend-architect "Analyze src/auth.ts for layer violations")
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if echo "$RESULT" | grep -q "domain layer"; then
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echo "✅ PASS: Identified layers"
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else
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echo "❌ FAIL: Did not identify layers"
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exit 1
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fi
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# Test 2: Agent recommends correct patterns
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echo "Test: Pattern recommendations"
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RESULT=$(claude agent backend-architect "Improve error handling in src/api.ts")
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if echo "$RESULT" | grep -q "Result<T, E>"; then
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echo "✅ PASS: Recommended Result pattern"
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else
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echo "❌ FAIL: Incorrect pattern"
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exit 1
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fi
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```
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**Pros**: Automated, catches regressions, CI/CD integration
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**Cons**: Requires maintenance, may have false positives/negatives
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---
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### Pattern 3: A/B Testing Configurations
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**Use Case**: Compare two versions of agent to determine which performs better
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**Setup**:
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```yaml
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# .claude/agents/backend-architect-v1.md (control)
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name: backend-architect
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version: 1.0
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instructions: |
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You are a backend architect specializing in...
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[original instructions]
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# .claude/agents/backend-architect-v2.md (experiment)
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name: backend-architect-v2
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version: 2.0
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instructions: |
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You are a backend architect specializing in...
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[modified instructions with new pattern emphasis]
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```
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**Evaluation**:
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```bash
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# Run same task with both agents, compare metrics
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# Task: "Analyze src/auth.ts for security issues"
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# Version 1 metrics:
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# - Response time: 45s
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# - Issues found: 3
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# - User rating: 4/5
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# Version 2 metrics:
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# - Response time: 38s
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# - Issues found: 5 (2 additional critical issues)
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# - User rating: 5/5
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# Conclusion: Version 2 is more thorough and faster → promote to production
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```
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**Pros**: Data-driven decisions, quantifiable improvements
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**Cons**: Requires discipline to run controlled experiments
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---
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### Pattern 4: Feedback Loop Integration
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**Use Case**: Continuously improve agent based on real-world usage data
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**Setup**:
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```bash
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# After agent completes task
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echo "How would you rate this response? (1-5, or 'skip'): "
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read RATING
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if [ "$RATING" != "skip" ]; then
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echo "Any specific feedback?: "
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read COMMENT
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# Log feedback
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echo "{\"timestamp\":\"$(date -Iseconds)\",\"agent\":\"$AGENT_ID\",\"rating\":$RATING,\"comment\":\"$COMMENT\"}" \
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>> .claude/logs/agent-feedback.jsonl
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fi
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# Weekly: Review feedback.jsonl, identify patterns
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# Monthly: Update agent instructions based on aggregated feedback
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```
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**Pros**: Aligns agent with actual user needs, identifies edge cases
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**Cons**: Requires manual review and action on feedback
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---
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## Example: Agent with Evaluation
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**Full template available**: [`examples/agents/analytics-with-eval/`](../../examples/agents/analytics-with-eval/) includes complete agent definition, hooks, analysis scripts, and report template.
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### Setup: Analytics Agent with Built-in Metrics
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```yaml
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# .claude/agents/analytics-agent.md
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---
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name: analytics-agent
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description: SQL query generator with evaluation hooks
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version: 1.0
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tools:
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- Read
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- Write
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- Bash
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hooks:
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post_response: .claude/hooks/log-analytics-metrics.sh
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---
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# Analytics Agent
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You are an expert SQL analyst helping users query databases.
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## Evaluation Criteria
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After each query:
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1. **Correctness**: Does query produce expected results?
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2. **Performance**: Query execution time < 5s?
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3. **Safety**: No destructive operations (DELETE, DROP, TRUNCATE)?
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4. **Best practices**: Uses proper JOINs, indexes, parameterized queries?
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## Instructions
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[... agent instructions ...]
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```
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### Metrics Hook
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```bash
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# .claude/hooks/log-analytics-metrics.sh
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#!/bin/bash
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# Triggered after analytics-agent response
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# Extract query from response (naive grep, improve with jq)
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QUERY=$(echo "$CLAUDE_RESPONSE" | grep -oP 'SELECT.*?;')
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if [ -n "$QUERY" ]; then
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# Test query (requires database connection)
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EXEC_TIME=$( (time psql -U user -d db -c "$QUERY") 2>&1 | grep real | awk '{print $2}')
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# Check for destructive operations
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if echo "$QUERY" | grep -iE 'DELETE|DROP|TRUNCATE'; then
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SAFETY="FAIL"
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else
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SAFETY="PASS"
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fi
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# Log metrics
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echo "{\"timestamp\":\"$(date -Iseconds)\",\"query\":\"$QUERY\",\"exec_time\":\"$EXEC_TIME\",\"safety\":\"$SAFETY\"}" \
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>> .claude/logs/analytics-metrics.jsonl
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fi
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```
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### Analysis
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```bash
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# Monthly review: Analyze metrics
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jq -s 'group_by(.safety) | map({safety: .[0].safety, count: length})' \
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.claude/logs/analytics-metrics.jsonl
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# Output:
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# [
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# {"safety": "PASS", "count": 127},
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# {"safety": "FAIL", "count": 3}
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# ]
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# Action: Review 3 failed queries, update agent instructions to prevent future violations
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```
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---
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## Tools & References
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### Open-Source Evaluation Frameworks
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#### nao (Analytics Agents)
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**URL**: [github.com/getnao/nao](https://github.com/getnao/nao/)
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**What it provides**:
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- Built-in evaluation framework for analytics agents
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- Unit testing capabilities for agent responses
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- Metrics collection (response quality, tool usage, performance)
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- Feedback loop integration
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**How to adapt for Claude Code**:
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- **Context builder pattern**: Apply nao's structured context approach to `.claude/agents/` config
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- **Evaluation hooks**: Translate nao's evaluation framework to Claude Code hooks system
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- **Metrics schema**: Use nao's metrics schema as template for your logs
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**Status**: Production-ready, actively maintained, TypeScript + Python
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---
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### Claude Code Native Patterns
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**Hooks system**: `.claude/hooks/` for automated logging (see `examples/hooks/README.md`)
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**Agents directory**: `.claude/agents/` for custom agent definitions (see `guide/ultimate-guide.md` Section 4)
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**MCP observability**: Use MCP servers for advanced logging and metrics aggregation
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---
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## Best Practices
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### Start Simple
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**Week 1**: Add basic logging hook (tool calls only)
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**Week 2**: Add user feedback prompt (manual ratings)
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**Week 3**: Build dashboard to visualize metrics
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**Week 4**: Run first A/B test on agent configuration
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### Focus on Actionable Metrics
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Don't track metrics you won't act on. Prioritize:
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1. **Task completion rate** → Refine agent instructions
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2. **Tool call errors** → Improve context or add examples
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3. **User ratings** → Identify confusing or unhelpful responses
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### Automate Where Possible
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Manual evaluation doesn't scale. Use:
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- Hooks for automatic logging
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- CI/CD integration for agent unit tests
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- Scripts for periodic metric aggregation
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### Build Feedback Loops
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Metrics are useless without action:
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- Weekly: Review metrics, identify patterns
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- Monthly: Update agent instructions based on data
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- Quarterly: Major agent refactoring if needed
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|
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---
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## Related Sections
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|
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- **[Agents](./ultimate-guide.md#4-agents)**: Creating custom agents
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- **[Hooks](./ultimate-guide.md#5-hooks)**: Automation with event hooks
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- **[Observability](./observability.md)**: Logging and monitoring strategies
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- **[AI Ecosystem](./ai-ecosystem.md#82-domain-specific-agent-frameworks)**: External frameworks like nao
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---
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**Next steps**:
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1. Add logging hook to your most-used agent
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2. Collect 1 week of metrics
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3. Analyze and refine agent based on data
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**Template**: See `examples/agents/analytics-with-eval/` for complete implementation with hooks, scripts, and report template
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|
|
@ -1609,6 +1609,32 @@ If you're not using Gas Town/multiclaude, you can still:
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- Experimentation tolerance is high (work may be lost/redone)
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- Team has SRE capacity to monitor/intervene
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|
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### 8.2 Domain-Specific Agent Frameworks
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Beyond general-purpose coding assistants, specialized frameworks target specific use cases with built-in context, evaluation, and deployment patterns.
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#### nao (Analytics Agents)
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**URL**: [github.com/getnao/nao](https://github.com/getnao/nao/) | **Stack**: TypeScript 58.9%, Python 38.5%
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**What it is**: Open-source framework for building and deploying analytics agents. Two-step architecture: build agent context via CLI (databases, docs, metadata) → deploy chat UI for natural language data queries.
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**Key features**:
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- Database agnostic (PostgreSQL, BigQuery, Snowflake, Databricks)
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- Built-in evaluation framework with unit testing
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- Native data visualization in chat interface
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- Self-hosted deployment with Docker
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- Stack: Fastify, Drizzle ORM, tRPC, React, shadcn UI
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**Relevance to Claude Code**: While nao deploys agents as standalone services (not Claude Code plugins), its patterns are transposable:
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- **Context builder architecture**: Structuring complex agent context (similar to `.claude/agents/` best practices)
|
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- **Evaluation framework**: Measuring agent quality through metrics, unit tests, and feedback loops (gap in current Claude Code workflows)
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- **Database integrations**: Patterns for injecting database context into agent prompts
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|
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**When to use**: Data teams building conversational analytics interfaces for business users. For Claude Code users, nao serves as reference architecture for agent evaluation and database context patterns.
|
||||
|
||||
**Status**: Active open-source project, production-ready, well-documented
|
||||
|
||||
---
|
||||
|
||||
## 9. Cost & Subscription Strategy
|
||||
|
|
|
|||
|
|
@ -6,7 +6,7 @@
|
|||
|
||||
**Written with**: Claude (Anthropic)
|
||||
|
||||
**Version**: 3.23.5 | **Last Updated**: February 2026
|
||||
**Version**: 3.24.0 | **Last Updated**: February 2026
|
||||
|
||||
---
|
||||
|
||||
|
|
@ -520,4 +520,4 @@ where.exe claude; claude doctor; claude mcp list
|
|||
|
||||
**Author**: Florian BRUNIAUX | [@Méthode Aristote](https://methode-aristote.fr) | Written with Claude
|
||||
|
||||
*Last updated: February 2026 | Version 3.23.5*
|
||||
*Last updated: February 2026 | Version 3.24.0*
|
||||
|
|
|
|||
|
|
@ -10,7 +10,7 @@
|
|||
|
||||
**Last updated**: January 2026
|
||||
|
||||
**Version**: 3.23.5
|
||||
**Version**: 3.24.0
|
||||
|
||||
---
|
||||
|
||||
|
|
@ -4241,7 +4241,7 @@ The `.claude/` folder is your project's Claude Code directory for memory, settin
|
|||
| Personal preferences | `CLAUDE.md` | ❌ Gitignore |
|
||||
| Personal permissions | `settings.local.json` | ❌ Gitignore |
|
||||
|
||||
### 3.23.5 Version Control & Backup
|
||||
### 3.24.0 Version Control & Backup
|
||||
|
||||
**Problem**: Without version control, losing your Claude Code configuration means hours of manual reconfiguration across agents, skills, hooks, and MCP servers.
|
||||
|
||||
|
|
@ -19293,4 +19293,4 @@ We'll evaluate and add it to this section if it meets quality criteria.
|
|||
|
||||
**Contributions**: Issues and PRs welcome.
|
||||
|
||||
**Last updated**: January 2026 | **Version**: 3.23.5
|
||||
**Last updated**: January 2026 | **Version**: 3.24.0
|
||||
|
|
|
|||
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