Before you can handle errors, you need to know which errors occur:

• API errors: The provider returns an error code (429 rate limit, 500 server error, 503 temporarily unavailable).
• Timeouts: The call takes too long and is aborted.
• Invalid output: The model returns text that does not match what you expected, such as text instead of JSON.
• Hallucinations: The model gives a factually wrong but syntactically correct answer.
• Tool errors: An external tool or API that the model calls fails.

Each error category requires a different approach.

The most basic error handling for API errors is retrying. But never do this naively: an immediate retry on an overloaded API makes the problem worse. Use exponential backoff: wait briefly after the first failure, a little longer after the second, and so on.

A simple pattern:

• First retry: wait 1 second
• Second retry: wait 2 seconds
• Third retry: wait 4 seconds
• Then give up and log the error

Libraries like tenacity (Python) or p-retry (Node.js) implement this pattern out of the box.

Not every error justifies a retry. Sometimes it is better to fall back to an alternative:

• Alternative model: If GPT-4o is unavailable, try a lighter model version or a different platform.
• Cached response: If the same question was answered successfully before, return the cached response.
• Human escalation: Route the task to a team member if the model fails to solve it repeatedly.
• Graceful degradation: Show a limited version of the functionality instead of an error message.

The right fallback depends on how critical the output is. For a chatbot response, a simpler answer is acceptable; for a financial document, it is not.

Even when a call technically succeeds, the output may be unusable. Always build in a validation step that checks whether the output meets your expectations:

• Is the JSON valid and does it contain the expected fields?
• Does a number fall within the expected range?
• Is the text not empty or abnormally short?

Use schema validation (Pydantic, Zod) for structural checks. Add domain-specific checks for content validation.

Always set a timeout on AI calls. A call that hangs blocks your system. Set a timeout that fits the expected response time: for fast models 10 seconds is more than enough, for complex reasoning models 60 seconds may be needed.

Combine timeouts with retry logic: if a call fails due to a timeout, retry with the same or a longer timeout.

Good error handling is invisible to the user but clearly visible to the development team. Log every error with sufficient context: the timestamp, the input that caused the error, the error type, and whether the retry succeeded.

Connect your logs to a monitoring dashboard so you can quickly see when the error rate rises. A sudden spike in errors is often the first sign of an API change at the provider.

If an external service fails repeatedly, there is no point in continuing to try. The circuit breaker pattern automatically stops calls when the error rate exceeds a threshold, waits a set amount of time, and then tries again. This protects your system from cascade failures and gives the external service time to recover.

Robust error handling is not an optional extra but a core component of every AI application running in production. Mach8 builds AI workflows with retry logic, validation, and monitoring built in, so they remain stable even when the model or underlying API lets you down.

Want to know how Mach8 builds reliable AI systems? View our AI agents service or schedule a conversation.