AI Response Drift (LLM Inconsistency)
Definition
The phenomenon where a language model produces different or conflicting answers to the same prompt across repeated interactions, due to probabilistic token generation and lack of persistent truth-state.
Why It Matters
In agentic workflows, response drift breaks deterministic systems. If an enterprise agent returns different parameter structures or logic on the exact same input, workflow automation fails. Software cannot rely on APIs that randomly change their schema or business logic.
How Exogram Addresses This
Exogram ignores response drift entirely. The deterministic execution boundary functions exactly the same way regardless of how the model drifts. If the LLM generates a hallucinated or drifted payload, the execution boundary catches it and deterministically blocks it.
Is AI Response Drift (LLM Inconsistency) vulnerable to execution drift?
Run a static analysis on your LLM pipeline below.
Related Terms
Key Takeaways
- → This concept is part of the broader AI governance landscape
- → Production AI requires multiple layers of protection
- → Deterministic enforcement provides zero-error-rate guarantees