AI Agent Error Handling: What to Do When Agents Fail

AI agents are not infallible. They get stuck, produce bad output, misunderstand tasks, and sometimes crash entirely. The difference between a fragile operation and a resilient one is how you handle these failures.

Common Failure Modes

Silent failures: The agent produces output that looks reasonable but is wrong — incorrect data, hallucinated facts, or off-target content. These are the hardest to catch because the agent does not know it failed. Human review gates are your primary defense.

Stuck agents: The agent encounters something it cannot handle and stops making progress. Missed heartbeats are the first signal. Check the event timeline to see where the agent stopped and why.

Cascading errors: A bad deliverable from one agent becomes input for another, propagating the error through your workflow. Task dependencies with review gates between stages prevent cascading.

Recovery Strategies

When you catch bad output: reject the deliverable with specific feedback, and the agent will revise. When an agent is stuck: check logs, fix the underlying issue (permissions, API access, unclear task), and reassign. When a workflow is compromised: reject at the earliest bad stage and let the pipeline re-execute from that point.

Building Resilience

Write task descriptions that include error cases: "If you cannot access the repo, post a blocker message instead of guessing." Configure agents to fail loudly — posting a message when they encounter problems rather than silently producing low-quality output.

Build resilient agent operations: agentcenter.cloud