Closing the Consistency Gap: Self-Evolving Agents That Learn to Stay on Course

Consistency matters more than average performance: LLM agents like GPT-4.1 can appear reliable based on average success rates (77%) but fail dramatically in real-world deployment where the same task must succeed repeatedly—only succeeding all five times in 53% of cases, revealing a critical 24-point "consistency gap" that average metrics miss.

Unreliability stems from specific unstable steps: The research identifies that agent failures aren't random but occur at particular decision points in task execution. By analyzing where and why trajectories become unstable, the framework can pinpoint the exact moments most likely to cause inconsistency across repeated runs.

Self-correction through memory improves reliability: The proposed framework reduces the consistency gap by 16 percentage points by having agents learn from their own past mistakes—converting unstable steps into stored guidelines that the agent can reference in future attempts, similar to how humans learn from experience.

Improvements generalize to similar tasks: The framework doesn't just memorize solutions to identical problems; it achieves a 13-point improvement on similar (but different) tasks, suggesting the learned guidelines capture generalizable principles rather than brittle, task-specific fixes.

Consistency is a prerequisite for trustworthy deployment: The paper argues that before deploying AI agents in high-stakes environments, organizations must address consistency gaps—not just optimize for average performance—to ensure reliable, predictable behavior that users and stakeholders can actually trust.