Build Trustworthy AI Agents Powered by Evals [Testμ 2026]

Rushabh Mehta’s Testμ Conf 2026 session argued that evaluating AI agents differs from conventional testing because long, tool-driven tasks can silently compound an early error across hundreds of steps, creating costly and difficult-to-diagnose failures. He described an eval harness as a combination of models, tools, tasks, captured execution trajectories, graders, and measurements such as cost and token usage, designed to verify both desired behavior and prohibited behavior across regressions and deployment environments. Because LLM behavior remains inherently non-deterministic, teams should reduce variability through fixed settings, schemas, repeated pass@k trials, and deterministic checks rather than expect to eliminate it. The session emphasized testing hallucinations by making necessary information inaccessible and rewarding honest abstention, while safeguarding real-world tool calls through idempotency, backoff, compensation mechanisms, detailed failure classification, checkpoints, and rollback. Mehta also highlighted memory management, including provenance, expiration, confidence levels, and structure-aware chunking, as essential for avoiding stale or misleading agent behavior. He recommended using code-based graders wherever possible, supplementing them with rubric-driven model judges and human review for nuanced judgments, and noted that benchmark results from GAIA 2 show strong progress in search and execution but persistent weaknesses in temporal reasoning, ambiguity, and adaptability.

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