Building Resilient Systems in the Age of AI
How traditional fault tolerance patterns apply to non-deterministic systems, and why we need new paradigms for evaluating application health.
In traditional software engineering, we deal with deterministic systems. If you pass the same input to a function, you expect the same output, every single time. Our entire paradigm of testing, monitoring, and debugging relies on this fundamental assumption.
The Non-Deterministic Shift
When we integrate Large Language Models (LLMs) into our critical paths, we introduce non-determinism as a core feature of the system. The same prompt might yield slightly different responses, or wildly different ones if temperature settings aren't strictly controlled.
We are no longer just writing code that executes instructions; we are orchestrating systems that evaluate probabilities.
Structural Patterns for LLM Integration
To handle this, we need to adapt our traditional resiliency patterns:
- Circuit Breakers: Not just for timeouts, but for semantic drift.
- Fallbacks: Gracefully degrading to heuristic-based systems when the model fails.
- Idempotency Keys: Tricky to implement when the output varies.
Author Name
Software engineer and writer exploring the intersection of distributed systems and machine learning. Previously lead architect at Tech Co.
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