Your Agent Aced the Task. Will It Do It Again?

An agent's performance can vary significantly during live demonstrations compared to rehearsals. In a production environment, this inconsistency can pose reliability issues, especially for critical tasks like financial transactions or contract reviews. Benchmarks often hide this variability behind an average score, which can be misleading.

For instance, a ReAct agent using GPT-4.1 achieved an average of 77.4% success across five runs, but only succeeded in all runs 53.0% of the time, indicating a 24.4 point consistency gap. Most benchmarks report only the average pass rate, which doesn't capture the real-world concern of whether the agent will consistently succeed when asked the same question again.

To address this, a new type of guideline called "consistency guidelines" has been introduced in the ALTK-Evolve system. These guidelines are derived from the agent's past performance and injected into the inference process to improve task success rates. The key insight is that an agent's decision-making process can be modeled as a probability distribution, and the shape of this distribution determines the consistency of the agent's output.

If the distribution is sharp, the agent's behavior is consistent; if it's flat, the agent's output is more variable. The consistency gap arises because small changes in the agent's internal probability distributions can lead to large differences in performance across different runs. The new consistency guidelines aim to reduce this variability by targeting specific decision points in the agent's trajectory that are prone to instability.

These guidelines are generated through a two-stage pipeline that detects which decisions are inconsistent and then creates targeted recommendations to mitigate this inconsistency. By implementing these consistency guidelines, the ReAct agent's reliability can be significantly improved, ensuring consistent performance even when the same task is repeated.