Recovery is a product feature
Every production agent will fail: a tool times out, context is missing, the task is ambiguous, or the model chooses the wrong path. Recovery paths define what happens next. The user should know what failed, what the agent still knows, what can be retried, and what needs human input.
Design repair loops
A repair loop lets users correct the agent without starting over. That can mean editing task state, changing a tool input, approving a safer plan, narrowing scope, or handing off to a deterministic workflow. Repair loops preserve trust because the system remains legible under stress.
Measure recovery explicitly
Recovery should be part of eval, logs, and product analytics. Track which failures recover, which require escalation, and which cause abandonment. Coop designs recovery as part of the agent loop rather than a generic error message after the fact.
Direct answers
What is an agent recovery path?
An agent recovery path is the designed next step after an AI agent fails, including retry logic, user repair, safe fallback, escalation, or handoff.
Why do recovery paths matter for AI agents?
They keep users from losing context, prevent silent failure, and make agent behavior easier to trust, debug, and improve.