Error handling is the practice of catching, categorizing, and responding to failures in AI systems before they reach users. It distinguishes between recoverable errors that can be retried and fatal errors that need escalation. For businesses, this means AI assistants that stay helpful even when components fail. Without it, a single API timeout breaks the entire user experience.
Your AI assistant times out on a complex request. The user sees a spinning wheel, then nothing.
The support ticket says "it just stopped working." Your logs show 47 different error types.
You fix one failure and three more appear. Every external API has its own way of breaking.
Systems do not fail gracefully by accident. They fail gracefully by design.
QUALITY LAYER - Ensuring AI systems stay helpful even when components fail.
Error handling is the practice of catching failures before they reach users and responding appropriately. When an AI model times out, when an API returns malformed data, when a database connection drops, error handling determines what happens next.
The goal is not to prevent all errors. That is impossible. The goal is to detect errors quickly, categorize them correctly, and respond in ways that preserve user experience and system stability. A well-handled error is invisible to users. A poorly-handled error breaks trust.
Every external dependency is a potential failure point. Error handling turns those failure points from catastrophic crashes into manageable hiccups.
Error handling solves a universal problem: how do you keep operations running when individual components fail? The same pattern appears anywhere reliability matters more than perfection.
Wrap risky operations in protective layers. Catch failures at the point they occur. Categorize by type and severity. Respond with the appropriate recovery action. Log everything for later analysis.
Select an error scenario and handling strategy, then see the difference in user experience.
Catch failures where they happen
Wrap external calls in try-catch blocks. Validate API responses before using them. Check for null values before accessing properties. The earlier you detect an error, the more options you have for recovery.
Not all errors are created equal
Classify errors by recoverability: Can this be retried? Is it temporary or permanent? Does it affect one user or everyone? Different categories trigger different responses. A rate limit gets retried. Invalid credentials get escalated.
Do something useful with the failure
Each error category maps to a response: retry with backoff, fall back to a simpler approach, return cached data, show a helpful message, or escalate to humans. The response should minimize user impact while preserving system data.
Answer a few questions to get a recommended error handling approach.
What type of error occurred?
A support agent requests an AI-generated summary. The first attempt times out after 30 seconds. Error handling catches this, recognizes it as a transient failure, waits 5 seconds, and retries. The second attempt succeeds, and the agent gets their summary without ever knowing the first attempt failed.
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This component works the same way across every business. Explore how it applies to different situations.
Notice how the core pattern remains consistent while the specific details change
Your code catches exceptions but does nothing with them. The function returns null or undefined. Downstream code has no idea something went wrong. Problems accumulate invisibly until the whole system is in a broken state.
Instead: Always log errors before handling them. Even if you recover gracefully, you need a record that something went wrong for debugging and monitoring.
A network timeout and an authentication failure both trigger the same generic "Something went wrong" message. Users cannot tell if they should wait and retry or if their account has a problem.
Instead: Map error categories to user-appropriate messages. Temporary errors get "Please try again." Permission errors get "Please check your credentials." Unrecoverable errors get "Please contact support."
An API returns errors so your code keeps retrying. Hundreds of times per second. Now you are making the problem worse by overwhelming the service, and you are burning through your rate limits for when it recovers.
Instead: Implement exponential backoff with maximum retry counts. After the limit, fail gracefully rather than retrying forever. Circuit breakers can stop retries entirely when a service is clearly down.
Error handling in AI systems is the practice of detecting when something goes wrong and responding appropriately. This includes catching API failures, handling malformed responses, managing rate limits, and dealing with model timeouts. Good error handling categorizes failures by type and severity, enabling different recovery strategies for different situations.
Implement error handling before your AI system goes into production. Every external API call, every model invocation, and every data transformation should have error handling. If your system currently shows users raw error messages or crashes silently, you need error handling. Start with the most common failure points: API timeouts, rate limits, and malformed model outputs.
The most common mistake is treating all errors the same way. A temporary rate limit and a permanently invalid API key require different responses. Another mistake is catching errors without logging them, making debugging impossible. Swallowing errors silently is equally problematic. The user gets no feedback while problems accumulate invisibly.
Categorize errors by recoverability and source. Recoverable errors like rate limits or timeouts can be retried automatically. Non-recoverable errors like invalid credentials need human intervention. Source categories include: infrastructure errors (network, database), AI model errors (malformed output, content filters), and integration errors (third-party API failures).
Error handling is the broader practice of catching and responding to failures. Retry strategies are one specific response within error handling. Error handling decides what type of error occurred and what response is appropriate. For some errors, the appropriate response is a retry. For others, it is a fallback, an escalation, or a graceful failure message.
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Choose the path that matches your current situation
You have no structured error handling yet
You catch errors but handling is inconsistent
Error handling works but you want better reliability
You have learned how to catch, categorize, and respond to failures. The natural next step is understanding how to maintain partial functionality when components fail.