What Makes an AI Agent Reliable?
Reliability for agents comes from bounded autonomy, observable decisions and engineered recovery—not model capability alone.
Reliability is a system property
An agent combines a probabilistic model with deterministic software, data, tools and policy. Reliability emerges from the full chain: context quality, tool contracts, permissions, orchestration, validation and recovery.
Bound autonomy deliberately
State what the agent may read, decide and change. Use scoped credentials and task-specific tools. Require human approval when an action is irreversible, high-impact or outside a well-tested boundary.
Evaluate trajectories, not just answers
A correct final answer can hide wasteful or unsafe steps. Capture tool selection, arguments, intermediate state, policy decisions and outcome. Test representative tasks, difficult edges and deliberate attacks.
Operate for graceful failure
Use timeouts, idempotency, circuit breakers and compensating actions. Make stalled work visible. A dependable agent knows when to stop, ask for help and preserve enough context for recovery.