Error Handling Patterns for AI-Enhanced Applications

Your AI service will go down. Not might. Will.

OpenAI has outages. Anthropic has outages. Every API on the planet has outages. The question is not whether your AI backend will fail. The question is what your users see when it does.

If the answer is a blank screen with a spinner that never stops, you've failed at the most basic level of engineering. Your application should work, albeit with reduced capability, even when every AI service you depend on is completely offline.

This is the principle of graceful degradation, and it's the single most important pattern in AI application development.

The Fallback Chain

Think of your AI features as a stack of progressively simpler alternatives.

Level 1: Your primary AI model. Claude Opus, GPT-4, whatever your flagship model is. This gives the best results and handles the most complex tasks.

Level 2: A faster, cheaper model. Claude Haiku, GPT-3.5. It's less capable but responds in milliseconds instead of seconds, and it handles 80% of requests perfectly well.

Level 3: Cached responses. You've seen this prompt before, or something semantically similar. Serve the cached response. The user gets an instant answer and you pay zero API costs.

Level 4: Rule-based defaults. No AI involved. The system follows predetermined logic to produce a reasonable, if generic, response. A product recommendation engine falls back to "most popular items." A writing assistant falls back to grammar-check-only mode.

Level 5: Honest communication. "This feature is temporarily unavailable. Here's what you can do instead."

Each level catches the failure of the level above it. The user's experience degrades gradually rather than collapsing entirely.

AI agents implement this entire chain when building your features. They don't just call the API and hope. They wrap every AI call in error handling that cascades through the fallback stack automatically.

Input Validation: Your First Defense

Half of AI errors are caused by bad inputs, not bad models.

A user pastes 50,000 characters into a text field that feeds an AI prompt. The prompt exceeds the model's context window. The API returns an error. If you didn't validate the input length, your user sees a cryptic error message.

A user includes Unicode characters that break your tokenizer. A user submits an empty string. A user submits JSON when you expected plain text. A user submits a file when you expected text.

Validate inputs before they reach the AI. Check length. Check format. Check character encoding. Check for obviously malicious content. Return clear, helpful error messages that tell the user exactly what to fix.

This sounds elementary. It is. And yet I see AI applications in production that pass raw user input directly to the model without any validation. Every one of them breaks in predictable ways.

Output Sanitization: Your Last Defense

AI models generate text. Sometimes that text contains things it shouldn't.

If your AI generates HTML, it might include script tags. If it generates SQL, it might include drop statements. If it generates JSON, it might produce invalid syntax. If it generates user-facing text, it might include your system prompt, internal instructions, or information about other users.

Sanitize every AI output before rendering it. Strip dangerous HTML. Validate JSON against your expected schema. Run content through a toxicity filter. Check that the output doesn't contain fragments of your system prompt.

Output sanitization is not optional. It's a security requirement. An AI model is an untrusted input source, just like a user form submission. Treat it with the same suspicion.

Circuit Breakers: Stopping the Cascade

When an AI service starts failing, the worst thing you can do is keep hammering it with requests. Each failed request consumes time and resources. Your response times increase. Your timeout errors pile up. Your users wait longer and longer for responses that never come.

A circuit breaker detects sustained failures and short-circuits the failing service. After five consecutive failures, stop trying. Route all traffic directly to the fallback chain. Check the primary service every thirty seconds with a probe request. When it responds successfully, gradually reopen the circuit.

This pattern prevents a single failing dependency from dragging down your entire application. Without it, an AI service outage becomes a full application outage.

Request Hedging: For When Speed Matters

Some AI features are latency-critical. An autocomplete suggestion needs to appear in under 200 milliseconds or users will keep typing past it. A real-time translation needs to keep pace with the speaker.

For these features, send the same request to multiple providers simultaneously and use whichever responds first. Cancel the slower requests when the first response arrives. You pay for slightly more API calls, but your P99 latency drops dramatically.

This only works if you have multiple providers configured. Which is another argument for building your AI integrations behind an abstraction layer rather than coupling directly to a single provider's SDK.

The Error Message Contract

When something fails, tell the user three things:

What happened. "We couldn't generate your summary right now."

What they can do about it. "Try again in a few minutes, or use the manual editor."

That their data is safe. "Your document has been saved and won't be lost."

Never show raw error messages from AI APIs. Never show stack traces. Never show "An unexpected error occurred." Every error message should be written by a human, for a human.