AI Agent Reliability

AI agent reliability asks whether an agent can complete goals correctly and consistently—not merely produce a plausible answer—while respecting permissions, grounding actions in evidence, and recovering from errors. Researchers measure repeated-run consistency, pass@k, calibration and abstention, trajectory quality, policy compliance, and verified changes to environment state on benchmarks such as τ-bench, SWE-bench, and OSWorld. Common approaches include fault-injection tests, trace-based failure diagnosis, formal contracts, deterministic runtime gates, tool-output validation, and uncertainty-aware escalation or rollback. Results show that model capability and average task scores can hide silent failures, drift, and poor recovery; the challenge is to provide deployment-representative assurance without excessive cost or restricting useful autonomy.