Why AI Systems Need Clearly Defined Failure States To Improve Debugging, Enable Better Recovery Strategies And Build More Reliable Applications
· Avery NXR
Most systems treat all failures the same.
They return a generic error.
“Something went wrong.”
This is not useful.
And in AI systems, it is especially harmful.
The Problem With Undefined Failures
AI failures are diverse:
Invalid outputs Timeouts Ambiguous inputs Integration failures
Treating them as one category hides critical information.
Why Failure States Matter
Failure states define:
What went wrong Why it went wrong What should happen next
Types Of Failure States
Validation failure → output doesn’t meet requirements Timeout → system exceeded time limit Model error → generation failed Input error → insufficient or invalid input
Why Granularity Is Important
Different failures require different responses.
Retrying a timeout makes sense.
Retrying invalid input does not.
How Clear Failure States Improve Systems
Better debugging Smarter retries Effective fallbacks
Designing Failure-Aware Systems
Define failure categories Handle each category differently Log failure context
How Avery NXR Handles Failures
Workflows define failure paths.
Each failure triggers specific actions.
Final Thought
Failure is not the problem.
Undefined failure is.