Designing Reliable Fallbacks for AI Agents

Agent retries carry state

An agent request may have already called tools, written data, or advanced a workflow before a failure occurs. Retrying the entire turn without understanding that state can duplicate side effects or produce inconsistent results.

Classify failures before retrying

Use the failure type to decide whether another attempt is useful and safe.

• Retry transient network and capacity errors within a time budget.
• Do not repeat invalid requests without changing the payload.
• Preserve completed tool results across model fallback.
• Use idempotency controls for tools with external side effects.

Choose capability-compatible fallbacks

A backup model must support the context size, tool schema, output format, and modalities the agent requires. Build fallback groups around capabilities and validated behavior rather than model names alone.

Keep the user deadline visible

Every retry consumes time. Pass a remaining deadline through the routing layer and stop when another attempt cannot finish within the product's response target. A clear partial result is often better than an invisible chain of late retries.