Introduction to Error Handling and Failure Modes

Understanding how errors and failures are managed is critical for building reliable AI-powered applications. In this chapter, you will explore the principles and strategies for handling errors and identifying failure modes within a Spring AI system. The focus is on the internal mechanisms Spring provides, rather than on any specific external AI provider.

You will learn how to:

• Detect and respond to common errors that can occur during AI operations;
• Implement consistent error handling patterns to improve system stability;
• Recognize and mitigate typical failure modes that may impact application reliability.

By mastering these concepts, you will be equipped to build Spring AI applications that are robust, predictable, and easier to maintain.

Error Detection and Propagation Mechanisms

Spring AI applications rely on robust internal mechanisms to detect and propagate errors, ensuring that failures are handled consistently and transparently.

Error Detection

• Use exception handling to identify issues such as invalid input, timeouts, or integration failures;
• Implement validation checks at key points, such as before sending requests to AI models or when receiving responses;
• Monitor system health and resource availability to detect infrastructure-related errors.

Error Propagation

• Throw meaningful exceptions when an error is detected, such as IllegalArgumentException for invalid data or custom exceptions for domain-specific failures;
• Use error response objects to communicate failure details back to the caller in a structured format;
• Propagate errors up the call stack, allowing higher-level components or global exception handlers to manage them consistently.

By combining these mechanisms, your Spring AI system can quickly identify errors, provide detailed feedback, and maintain reliability even when issues occur.

Simple Analogy: Error Handling as a Safety Net

Imagine you are using a voice assistant built with Spring AI to answer customer questions. If the AI cannot understand a question, it should not crash or give a confusing answer. Instead, it should catch the error and respond with a helpful message.

Minimal Example: Handling Errors in a Spring AI Service

Suppose you have a QuestionAnswerService that calls an AI model. You wrap the AI call in a try-catch block to manage errors gracefully:

public String answerQuestion(String question) {
    try {
        // Call to AI model that might fail
        return aiModel.generateAnswer(question);
    } catch (Exception e) {
        // Handle the error and provide a safe fallback
        return "Sorry, I couldn't process your question. Please try again.";
    }
}

Key point: By catching exceptions, you prevent system failures and give users a clear, safe response.