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Tool Composition Patterns
Error Handling in Tools
Build resilient AI agents through robust error handling and graceful degradation
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0 / 5 completedWhy Error Handling Is Critical
In production, everything that can fail, will fail. Networks drop connections. APIs rate-limit requests. Databases timeout. Disk fills up. The question isn't if errors happen—it's how your agent responds when they do.
Great error handling is what separates production-ready agents from demo prototypes. It's not glamorous, but it's the foundation of reliability.
The Cost of Poor Error Handling
💥
Silent Failures
Tasks fail without notification—users get wrong answers
🔄
Cascading Errors
One tool failure crashes entire workflow
😡
Poor UX
Cryptic error messages confuse users
🐛
Debug Nightmares
No logs = impossible to diagnose issues
Interactive: Common Error Types
Explore different error categories agents encounter
API timeouts, connection errors, DNS failures
Common
High Severity
Common Examples:
Request timeout
Connection refused
DNS resolution failed
SSL error
Recovery Strategy:
Retry with exponential backoff
The Error Handling Mindset
1
Assume Failure
Every external call can fail—plan for it
2
Fail Gracefully
Partial results beat complete failure
3
Log Everything
You can't debug what you can't see
4
Communicate Clearly
Error messages should help, not confuse