Capstone — Coding-Agent Eval Harness with Golden Set
Build a real eval harness — golden set, scoring, regression tracking.
What You Will Learn
- Build eval harness for coding agent.
- Golden set + scoring.
- Regression tracking.
- Automate end-to-end.
- Measure agent quality over time.
Why This Matters
This capstone builds a real eval harness that continuously tests the coding agent. Combines eval (PR-29 to PR-36), coding agent (PR-15 to PR-20).
Concept Explained
Harness: define tasks, delegate to agent, score PRs, track over time, alert on regressions.
How It Works
Define 15–25 agent-eligible tasks. Each with acceptance criteria. Delegate to agent. Score: criteria met? Tests pass? Quality? Track. Alert on regressions.
Step-by-Step Tutorial
1. Define tasks
15–25 agent-eligible with acceptance criteria.2. Delegate
Run via agent.3. Score
Criteria (automated), tests (CI), quality (LLM-as-judge).4. Track
Scores over time.5. Alert
On regression (>5% drop).6. Automate
Scripted: delegate, wait, score, report.Real-World Example
A team built this harness. Ran weekly. Caught regression when model upgraded — score dropped from 80% to 70%. Rolled back. Harness saved them from shipping regression.
Example Prompts / Commands / Code
"""[Task definitions (15-25)]
|
v
[Delegation script] -> [Agent] -> [PRs]
|
v
[Scoring]
- Criteria met? (scripted check)
- Tests pass? (CI)
- Quality? (LLM-as-judge)
|
v
[Report] -> [Dashboard] -> [Alerts]
|
v
[Weekly run; before/after upgrades]
"""
"""Eval run: 2026-08-30
Model: claude
Tasks: 20
Results:
- Pass: 16 (80%)
- Fail: 4 (20%)
Failures:
- Task 3: missed edge case (null input)
- Task 7: scope creep (refactored unrelated code)
- Task 12: hallucinated API
- Task 18: tests didn't pass
Comparison to last week: 80% (no change)
Comparison to baseline: 78% (+2%)
Status: No regression. Proceed.
"""
Common Mistakes
- Too few tasks — low signal.
- No automation — manual burden.
- No tracking — can't detect regressions.
- Not running before upgrades — surprises.
Best Practices
- 15–25 agent-eligible tasks with criteria.
- Automate scoring (criteria + tests + LLM-as-judge).
- Track scores over time.
- Run weekly and before/after upgrades.
- Alert on regressions (>5% drop).
Troubleshooting
| Problem | How to Fix |
|---|---|
| Agent produces variable results | Run each task 3 times; average. |
| LLM-as-judge unreliable | Calibrate against humans. |
Practical Exercise
Your Turn
Build an agent eval harness with 10 tasks. Run. Score. Track.
Professional Challenge
Run harness weekly for a quarter. Track agent quality. Identify trends. Document.
Key Takeaways
- Agent eval harness: tasks, delegate, score, track.
- 15–25 tasks with criteria.
- Automate scoring.
- Track over time.
- Run weekly and before/after upgrades.
Frequently Asked Questions
How often to run?
How many tasks?
Further Reading
Official References
SEO Metadata
SEO title: Capstone — Coding-Agent Eval Harness with Golden Set
Meta description: Build a real eval harness — golden set, scoring, regression tracking.
Primary keyword: capstone
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URL slug: /capstone-coding-agent-eval-harness-golden-set
Categories: AI Tools, GitHub Copilot
Tags: GitHub Copilot, Professional, Capstone, Eval Harness, Coding Agent, Regression, IMCSEIAN, Tutorial, IMCSEIAN
Featured image concept: IMCSEIAN lesson card for Capstone — Coding-Agent Eval Harness with Golden Set
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