Your Agent Aced the Task. Will It Do It Again?
What Changed
The article reports that a ReAct agent using GPT‑4.1 achieved a Mean@5 success rate of 77.4% but only 53.0% Pass^5, revealing a 24.4‑point consistency gap. Using the Consistency Analyzer and consistency guidelines from ALTK‑Evolve, the authors reduced the gap to 12.0 points, raising Pass^5 to 69.0% while increasing Mean@5 to 81.0%. The method requires one additional model call per decision step and does not rely on ground truth or full task re‑execution.
Why It Matters
Enterprise architects should note that high average accuracy can mask unreliability; a large consistency gap can lead to mission‑critical failures. Implementing consistency diagnostics can improve reliability without sacrificing overall performance, reducing the risk of costly errors in production workflows.
The Limitation
The approach adds inference cost proportional to the number of decision steps and may not fully eliminate variability on extremely complex or highly dynamic tasks.
What You Can Do
Deploy the Consistency Analyzer on your agent logs to identify flip‑prone decision points and generate consistency guidelines before production rollout.