Agents fail in ways traditional software doesn't: silent reasoning errors, tool misuse, subtle output drift. An eval harness designed for agents — not unit tests — catches these. The 2026 maturity bar is real and rising.
Trace-level evals
Capture every tool call, model call, decision in a trace. Evaluate: did it use the right tools? Did it call them with right arguments? Did it stop at the right time? Tools like Langfuse, Arize Phoenix, OTel-based custom: all support this.
Outcome evals + reasoning evals
Outcome: did the agent achieve the goal? Reasoning: was the path reasonable? An agent that gets the right answer by accident still has a brittle policy. Both matter.
LLM-as-judge
Use a strong model to grade outputs against a rubric. Cheaper than human review for large eval sets. Calibrate the judge against human grades on a sample (judge correlation should be >0.7 with humans).
Regression catching
Run the full eval suite on every agent version. Block deploys that regress beyond threshold. Track specific examples that flip — those reveal what changed.
Production sampling back into eval
Sample real production traces. Add interesting ones (errors, low confidence, user-reported issues) to the eval set. The eval set should grow as you learn what production looks like.