Building autonomous AI agents that work in demos is easy. Building agents that work reliably in production — across edge cases, adversarial inputs, and failure conditions — requires systematic evaluation frameworks, sandboxed execution environments, and explicit failure mode analysis before deployment.

Autonomous agents fail in ways that are qualitatively different from single-turn LLM failures. Errors compound: a wrong assumption in step 2 of a 20-step task can invalidate all subsequent work. Failures are often silent: the agent completes the task with apparent confidence but produces a subtly wrong result that passes superficial review. And some agent actions are irreversible: a sent email, a deleted file, a submitted form cannot be undone. Reliability engineering for agents starts with evaluation. Trajectory evaluation — assessing not just the final output but the sequence of actions taken — is more informative than output-only evaluation, catching agents that reach correct answers via incorrect reasoning. Benchmark suites like SWE-bench, GAIA, and WebArena provide standardized task environments for measuring real-world autonomous task completion. Sandboxing is essential for agents with write capabilities: production agents that interact with filesystems, databases, APIs, or UIs should execute in isolated environments (containers, virtual machines, read-only API modes) during testing, with graduated permission grants as reliability on task classes is established. Minimal footprint principles — granting agents only the permissions needed for the current task, preferring reversible over irreversible actions, requesting human confirmation before high-impact steps — reduce the blast radius of failures. Human-in-the-loop checkpoints at high-uncertainty or high-consequence decision points, rather than full autonomy from start to finish, represent the current best practice for deploying capable agents on consequential tasks.

Agent Reliability Engineering: Evaluation, Sandboxing, and Failure Modes