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This guide covers synthetic AI agent evals with production-focused examples and trade-offs.
Agent quality dies quietly — a prompt tweak, a model swap, a broken tool schema — and accept-rate dashboards stay green. The unfair advantage is fixed canary coding tasks in CI: tiny, deterministic jobs with golden diffs and tool traces that must pass on every agent-stack release before developers feel the regression.⚡ TL;DR: Maintain 10–30 frozen tasks (bugfix, rename, test authoring) with golden outputs and allowed tool traces. Run on every prompt/model/tool change; fail the release on score drop. Not a substitute for blind A/B on real tickets — a smoke alarm. Pair with RAG Evaluation on AWS.
Canary task format — synthetic AI agent evals
yaml
# evals/canaries/rename-helper.yaml
id: rename-helper-01
repo_fixture: fixtures/tiny-lib
prompt: |
Rename export `add` to `addNumbers` and update callers.
Do not change behavior.
budget:
max_tool_calls: 12
max_tokens: 8000
expect:
diff_golden: goldens/rename-helper-01.diff
tests: ["pnpm test"]
forbidden_paths: ["pnpm-lock.yaml", ".github/**"]
typescript
// evals/run-canary.ts
export async function runCanary(c: Canary): Promise<Score> {
const session = await agent.run({ prompt: c.prompt, cwd: c.repo_fixture });
const diff = await gitDiff(c.repo_fixture);
const match = normalizeDiff(diff) === read(c.expect.diff_golden);
const toolsOk = session.toolCalls.length <= c.budget.max_tool_calls;
const forbidden = touchedForbidden(diff, c.expect.forbidden_paths);
// ✅ binary gates for smoke; keep graded rubrics elsewhere
return { pass: match && toolsOk && !forbidden && testsGreen(c) };
}
Where canaries gate
| Change | Gate |
|---|---|
| System prompt | Full canary suite |
| Model ID / temperature | Full suite |
| Tool schema | Suite + schema contract tests |
| IDE extension only | Smoke subset (3 tasks) |
bash
# ✅ CI on agent-stack repo
pnpm evals:canaries --fail-under 0.95
# ❌ Shipping prompt changes with “tried a few chats locally”
Avoid canary overfit
Rotate 10% of tasks quarterly; keep a held-out set. Prefer tasks that assert behavior (tests green) plus a golden diff for mechanical edits. For flaky agent nondeterminism, allow N seeds with majority pass — never hide failures by raising temperature. Tie to Deterministic Replay so failing canaries dump replay bundles.Closing checklist
✅ Dos – ✅ Freeze fixtures + golden diffs in git – ✅ Run on every prompt/model/tool change – ✅ Fail release under threshold (e.g. 95%) – ✅ Budget tool calls and forbid lockfile touches – ✅ Dump replay artifacts on failure ❌ Don’ts – ❌ Don’t use canaries as the only eval (still run human A/B) – ❌ Don’t update goldens casually to “make CI green” – ❌ Don’t allow network/prod tools in canary sandboxes – ❌ Don’t measure only accept rate – ❌ Don’t skip canaries for “emergency” model swapsRelated reading
- Evaluating AI Coding Tools: Blind A/B Tests on Real Tickets
- RAG Evaluation on AWS: Hit Rate, Faithfulness, and Cost Curves
- Deterministic Replay: Agent Sessions You Can Debug in Postmortems
- LLM Output Validators: Parse Trees Before Accepting Any Patches
Related: Day 57: Coding Agents That Only Emit Intents · Claude Code: The Developer Workflow That Replaces 80% of Manual Coding Tasks · RAG Over ADRs: Architecture Decision Retrieval for Coding Agents
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