How AI Coding Agents Handle Edge Cases and Error Handling

Identifying Edge Cases During Planning

Before writing implementation code, the agent identifies the edge cases that the code needs to handle. For a function that processes a list, what happens when the list is empty? When it has one element? When it has millions? For a function that accepts user input, what happens with empty strings, extremely long strings, special characters, or null values? For a database query, what happens when the record does not exist?

This systematic identification catches edge cases that human developers often miss because they are focused on making the main functionality work. The agent does not have that tunnel vision. It evaluates the full input space as part of its planning process, which means edge case handling is built into the initial implementation rather than discovered through bugs.

Common Edge Cases the Agent Handles

• Empty and null values: Empty arrays, null references, empty strings, missing object properties, and undefined variables.
• Boundary conditions: Zero, negative numbers, maximum integer values, empty collections, single-element collections, and maximum-length strings.
• Type mismatches: Strings where numbers are expected, arrays where objects are expected, and different date formats.
• Network failures: API timeouts, connection errors, malformed responses, and rate limiting.
• Concurrent access: Multiple requests modifying the same data, race conditions, and stale data scenarios.
• Permission issues: Missing file permissions, unauthorized API access, and expired authentication tokens.

Error Handling Patterns

The agent follows the error handling patterns established in the existing codebase. If the project uses exceptions for error handling, new code throws and catches exceptions in the same style. If the project uses result types or error codes, the agent follows that pattern. Consistency in error handling across the codebase makes the code easier to understand and maintain.

The agent also ensures that error handling is appropriate to the context. User-facing code provides helpful error messages. Internal code provides detailed technical information for debugging. API endpoints return appropriate HTTP status codes. Logging includes enough context to diagnose the issue later. Each error handling decision is informed by where the code sits in the application architecture.

Validation at System Boundaries

The agent applies the principle of validating input at system boundaries: where user input enters the system, where external API responses arrive, and where data crosses trust boundaries. Inside the system, between trusted components, the agent trusts the data because it was validated at the boundary. This approach provides thorough protection without redundant validation code scattered throughout the application.

Review Catches What Planning Missed

Even with thorough planning, some edge cases are missed. The agent's review step provides a second chance to catch them. The review specifically looks for unhandled edge cases: functions that do not check for null when null is possible, loops that do not handle empty collections, and API calls that do not handle failure responses. When the review finds a gap, the agent adds the appropriate handling before the code is delivered.