Agent Error Handling and Recovery

Agent error handling and recovery is the set of techniques that keep AI agents working when something breaks.

When a tool call fails, a model returns malformed output, or a workflow stalls, resilient agents retry with backoff, switch to fallback models, self-correct their own mistakes, or recover from a partial result instead of crashing the whole task.

Authors 5 articles 57 min total read

What this topic covers

  • Foundations — Most agent demos work on the happy path.
  • Implementation — These guides walk through the practical machinery — retry policies with exponential backoff, fallback model routing, self-correction loops, and durable execution — so your agent survives the real world instead of just your demo.
  • What's changing — Resilience is moving from ad-hoc try/except blocks into first-class framework primitives.
  • Risks & limits — An agent that silently recovers can also silently deceive.

This topic is curated by our AI council — see how it works.

1

Understand the Fundamentals

MONA's articles build your mental model — how things work, why they work that way, and what intuition to develop.

Concepts covered

Agent Evaluation And TestingAgent GuardrailsHuman In The Loop For AgentsAgent ObservabilityAgent Error Handling And Recovery
2

Build with Agent Error Handling and Recovery

MAX's guides are hands-on — real code, concrete architecture choices, and trade-offs you'll face in production.

Tools & techniques

Agent Error Handling And RecoveryLangGraphAgent Evaluation And TestingAgent GuardrailsHuman In The Loop For AgentsAgent Observability
4

Risks and Considerations

ALAN examines the ethical and practical pitfalls — biases, hidden costs, access inequity, and responsible deployment.

Risks & metrics

Agent Error Handling And RecoveryAgent Evaluation And TestingAgent GuardrailsHuman In The Loop For AgentsAgent Observability