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AI Agents · Lesson

Eval-Driven Development for Agents

Write the eval before you ship the agent — the only way to iterate without regressions.

Eval-Driven Development for Agents is a free AI Agents lesson on CoddyKit — lesson 1 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the AI Agents learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.

Evals Are Tests for AI

You wouldn't ship code without tests. The same logic applies to LLM agents — without evals, every change is a coin flip.

Eval-Driven Development (EDD) means writing the eval BEFORE you ship the change.

The EDD Loop

  1. Write a failing eval: input + expected behavior
  2. Improve the agent until the eval passes
  3. Add the eval to your suite
  4. Run on every change

What to Eval

For an agent, eval at multiple levels:

  • Component — does the retriever return the right chunks?
  • Step — does the LLM pick the right tool?
  • End-to-end — does the agent produce the right final answer?

Eval Data

Each eval row is at minimum:

eval_example = {
    'input': 'What is our return policy?',
    'expected_output': 'mentions 30-day window',
    'expected_tool_calls': ['retrieve'],
    'metadata': {'tags': ['policy', 'common']}
}
for k, v in eval_example.items():
    print(f"{k}: {v}")

Run an Eval Suite

def run_evals(suite, agent):
    results = []
    for case in suite:
        actual = agent.run(case['input'])
        scores = [
            score_correctness(actual, case['expected_output']),
            score_tool_calls(actual.tool_calls, case['expected_tool_calls'])
        ]
        results.append({'case': case, 'actual': actual, 'scores': scores})
    return results

CI Integration

Run evals on every PR. If quality drops below threshold, block the merge:

# .github/workflows/evals.yml
on: pull_request
jobs:
  evals:
    runs-on: ubuntu-latest
    steps:
      - run: python -m evals.run --fail-below 0.85

Eval Cadence

  • Component evals — every PR
  • End-to-end evals — every PR (sampled) + nightly (full)
  • Production traces -> eval set — weekly

Production -> Eval Pipeline

Mine real traces. Bad outcomes become tomorrow's evals:

for trace in production_traces_with_thumbs_down():
    add_to_eval_suite(trace.input, expected=trace.fixed_output_from_human)

LangSmith / Langfuse Eval

Both tools have built-in eval support: dataset versioning, eval functions, comparison views.

Manual Spot-Checks

Automated evals miss style and nuance. Periodically read 20 random traces by hand — catches issues evals miss.

Anti-Pattern: Eval Set Memorisation

If you tune the agent until it passes your eval, but the eval is small, you over-fit. Keep evals expanding and rotate test/train splits.

Eval Set Maintenance

Evals drift as your product changes. Add cases for new features; retire cases for removed features.

EDD Definition

What does Eval-Driven Development mean?

Recap

Eval-Driven Development is non-negotiable for serious agents. Write evals at every level, run in CI, grow your suite from production traces.

Frequently asked questions

Is the “Eval-Driven Development for Agents” lesson free?

Yes — the full text of “Eval-Driven Development for Agents” is free to read here on the web, and the AI Agents course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the AI Agents course, upgrade to CoddyKit PRO.

What will I learn in “Eval-Driven Development for Agents”?

Write the eval before you ship the agent — the only way to iterate without regressions. You practise AI Agents with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.

Do I need any experience to start AI Agents?

No prior experience is required. AI Agents on CoddyKit is structured for beginners through advanced learners; this is — lesson 1 of 4, so you can start here or from the beginning and move at your own pace.

How long does the “Eval-Driven Development for Agents” lesson take?

Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.

Can I write and run code in this AI Agents lesson?

Yes. Every AI Agents lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.

All lessons in this course

  1. Eval-Driven Development for Agents
  2. Building a Golden Test Set
  3. LLM-as-a-Judge Pitfalls
  4. Benchmark Suites: SWE-Bench, GAIA, ToolBench
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