How to Evaluate AI Agents: Observability, Rubrics vs. Ground Truth, Regression Testing

Description

In this quick but dense breakdown, we tackle a question every serious builder faces: How do you evaluate AI agents when outputs are open-ended and paths vary run-to-run? The key: stop chasing a single “ground truth” answer for everything. Instead, pair observability with a small, growing rubric-based eval set and continuous regression testing. This workflow lets you ship faster without breaking what already works. What you’ll learn - Why observability first: Traces reveal the real cause of failures (usually context and step contracts, not model IQ). - Build a living eval set: Start with the mess-ups you see in the wild; turn each into a test case. - Rubrics vs. one right answer: For open-ended tasks, grade against criteria (tools called, facts included, format met) instead of a single target string. - Catch regressions early: Every prompt or tool change re-runs evals so you don’t re-break past fixes. - Outcome-oriented metrics: Define success like a PM—task completion, policy adherence, latency, and cost—then measure them. Practical framework (copy this) - Instrument everything - Capture inputs/outputs, tool calls, intermediate notes/scratchpad, and final responses. - Tag runs by model, prompt version, and tool config so you can compare. - Turn failures into tests - Any time an agent misbehaves, save the input + desired behavior. Write a short rubric (3–8 checks) that a grader can score deterministically. Write rubrics like specs Example (email assistant): - Called calendar tool when scheduling is requested. - Mentioned correct availability window. - Proposed 2–3 timeslots and asked for confirmation. - Signed off with the correct persona (“Harrison,” tone = professional). - No PII leaks; links correctly formatted. - Automate regression runs - On each prompt/tool change, run the suite and compare to baseline. - Fail the build on critical rubric violations (policy, safety, money-moving actions). Track outcomes, not just paths It’s okay if the model takes a different path—as long as the rubric passes and the outcome is correct, within latency/cost SLOs. Continuously improve - Add new cases whenever users hit edge conditions. - Diff prompts like code; record “why we changed it” next to the test it fixed. - When to use ground truth vs. rubrics - Ground truth fits narrow tasks (exact extraction, deterministic transforms). - Rubrics shine for open tasks (assistants, research, planning, multi-tool jobs). - Starter rubric ideas (mix & match) - Tooling: Required tools invoked? In the right order? With valid arguments? - Content: Key facts present; no contradictions; cites sources if requested.- - Format: JSON schema valid; sections present; persona/tone correct. - Policy/Safety: No disallowed actions; sensitive steps gated or escalated. - UX: Clear next step; asks for missing info succinctly. If this helped, like/subscribe. In the comments, drop the one task your agent must nail—I’ll suggest a 5-check rubric you can copy into CI.