The teams that deploy reliable AI agents aren't lucky — they have evaluation pipelines. Building evals is the highest-leverage investment a team can make before going to production, and it's also the most skipped step.
Five layers of the evaluation stack
1. Golden Dataset Tests
A curated set of 50–200 input/output pairs representing the intended behavior of your agent. Run automatically on every change. Outputs are scored by format compliance, key phrase presence, and LLM-as-judge evaluation. Your production safety net — start building it on day one.
2. Automated Assertion Testing
Rule-based assertions over LLM outputs: required JSON keys present, values within valid ranges, no PII patterns in outputs, no prohibited content. Runs in milliseconds — catches structural failures that deterministic tests can identify without LLM overhead.
3. LLM-as-Judge Evaluation
Use a strong model (GPT-4o, Claude 3.7) to rate outputs on task-specific rubrics: correctness, helpfulness, tone, completeness. Correlates well with human ratings at scale. Define rubrics carefully — vague criteria produce noisy scores.
4. Human Review Sampling
Sample 2–5% of production outputs weekly for human review. Captures failures that automated tests miss — nuanced errors, cultural issues, off-brand responses. Essential for high-stakes applications and for continuously improving your golden dataset.
5. Regression Testing on Model Updates
Every model version update (including provider-side silent updates) should trigger a full regression run. Models frequently regress on specific behaviors after updates — without regression testing, you discover this from user complaints instead of your test suite.
Built-in evaluation on MoltBot
Golden datasets, assertion tests, LLM-judge scoring, regression runs — automated. 14-day free trial.
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