What it means
Error handling in an AI deployment covers three layers: the model can fail (timeout, rate limit, garbled output), the integration can fail (CRM API down, webhook lost), and the workflow logic can fail (unexpected input shape). Each needs a defined response: retry, fall back, escalate, or quietly skip.
The logs are what makes a failure recoverable. A good log includes the inbound payload, the model version and prompt version, the chain of tool calls, the failure mode, and a unique correlation ID. With that, you can replay the exact failure on a developer machine in minutes.
Why it matters
An AI deployment without good error handling fails noisily, in front of customers, in ways that look like 'the AI is broken' even when the AI is fine. With good error handling, customers either do not notice (the workflow fell back) or the team is alerted before they do.
The logs also matter for compliance. When a customer complains 'your AI told me the wrong thing', the log is the artefact that lets you investigate and respond honestly.
Example
An e-commerce AI agent's product-lookup API goes down at 11pm. Error handling: agent detects the failure, falls back to a templated 'a human will be in touch in the morning' reply, logs the failure with correlation ID, alerts the on-call channel. By 8am, the on-call has the logs ready and the integration is fixed before the team arrives.