Metrics & Benchmarks
You can’t improve what you don’t measure. These metrics help you evaluate whether your agentic workflows are effective and where to optimize.
Key Metrics
Section titled “Key Metrics”1. Task Completion Rate
Section titled “1. Task Completion Rate”What: Percentage of tasks that reach “done” without human code intervention.
How to measure: Track tasks assigned to agents → tasks that pass all verification without manual edits.
Target: > 85% for well-structured tasks with clear specs.
2. First-Pass Accuracy
Section titled “2. First-Pass Accuracy”What: Percentage of tasks correct on the first attempt (no corrections needed).
How to measure: Count tasks that pass review without revision.
Target: > 70% with proper context engineering.
3. Context Efficiency
Section titled “3. Context Efficiency”What: Average context utilization when task completes.
How to measure: Monitor context % at task completion via your tool’s status indicator (see Tool Configuration Reference for setup).
Target: Under 60% for complex tasks, under 40% for simple tasks.
4. Regression Rate
Section titled “4. Regression Rate”What: Percentage of agent tasks that introduce new test failures.
How to measure: Run full test suite before and after agent work. Count new failures.
Target: Under 5% with TDD workflows, under 2% with full guardrail stack.
Benchmark: TDAD achieves 1.82% regression rate (down from 6.08% baseline).
5. Token Cost per Task
Section titled “5. Token Cost per Task”What: Total tokens consumed across all agents for a task.
How to measure: Sum input + output tokens across all sessions for a task.
Target: Decreasing trend over time as context engineering improves.
6. Human Intervention Rate
Section titled “6. Human Intervention Rate”What: How often humans must step in to correct agent work.
How to measure: Track corrections, redirections, and manual edits during agent sessions.
Target: Under 20% of tasks require intervention.
7. Cycle Time
Section titled “7. Cycle Time”What: Time from task assignment to verified completion.
How to measure: Timestamp task start and successful verification end.
Benchmark: 2-3x speedup over manual development with proper practices (CodeScene data).
Industry Benchmarks
Section titled “Industry Benchmarks”| Organization | Metric | Result |
|---|---|---|
| TELUS | Shipping speed | 30% faster |
| Zapier | AI adoption | 89% org-wide |
| Rakuten | Large codebase navigation | 12.5M lines in 7 hours, 99.9% accuracy |
| CodeScene customers | Speed with guardrails | 2-3x improvement |
| TDAD paper | Regression reduction | 70% decrease |
| BAML project | Feature development | 7 hours for 35k LOC feature (300k LOC codebase) |
Optimization Strategies
Section titled “Optimization Strategies”Low Completion Rate?
Section titled “Low Completion Rate?”- Improve spec quality (more detail, clearer success criteria)
- Add verification mechanisms (tests, type checks)
- Check code health of target files (refactor if needed)
High Token Cost?
Section titled “High Token Cost?”- Use sub-agents for exploration (reduce main context pollution)
- Compact between phases
- Scope tasks more narrowly
- Use a smaller/cheaper model for review and research agents
High Regression Rate?
Section titled “High Regression Rate?”- Implement TDD workflows with contextual test targets
- Add post-edit hooks for type checking
- Enforce coverage thresholds
- Use the TDAD approach (tell agents which tests to check)
High Intervention Rate?
Section titled “High Intervention Rate?”- Improve agent configuration file clarity (remove ambiguity)
- Add skills for domain-specific knowledge
- Provide more context in prompts
- Review at plan level, not code level
Tracking Dashboard
Section titled “Tracking Dashboard”For teams, track these metrics in a simple dashboard:
## Weekly Agentic Development Metrics
| Metric | This Week | Last Week | Trend ||--------|-----------|-----------|-------|| Tasks completed by agents | 47 | 42 | +12% || First-pass accuracy | 74% | 68% | +6% || Avg context at completion | 52% | 63% | -11% (better) || Regression rate | 3.2% | 4.8% | -1.6% (better) || Avg tokens per task | 45,000 | 52,000 | -13% (better) || Human interventions | 18% | 24% | -6% (better) |