How to Test AI Agents Before Production
How to test AI agents before production: a 7-step method — define success, build a realistic user population, run multi-turn tests, score, and gate.
Evaluation, from foundations to practice: what evals are, how LLM judges work (and where they're biased), which metrics matter for agents, and what public benchmarks do and don't tell you.
How to test AI agents before production: a 7-step method — define success, build a realistic user population, run multi-turn tests, score, and gate.
How LLM as a judge works: judge designs, writing rubric prompts, the known biases (position, verbosity, self-preference) and how to mitigate each.
A practical guide to AI agent evaluation: outcome vs. trajectory metrics, four evaluation methods, and a step-by-step framework for running agent evals.
AI evals explained: what an eval is (task + data + scoring), how LLM evals differ from agent evals, and how to write your first 20 eval cases.
Why single-turn evals miss real failures, and how multi-turn evaluation works: scripted flows, simulated users, and conversation-level scoring.
What AI agent benchmarks actually measure — τ-bench, GAIA, SWE-bench — what scores tell you about your own agent, and how to build an internal benchmark.
Eval-driven development means writing evals before you build, iterating against them, and gating releases on results. How the loop works — and its limits.