Quantifying Agent Reliability

As language model agents are increasingly deployed for multi-step, autonomous workflows, their reliability becomes a critical engineering concern. This paper proposes a formal framework for measuring 'cross-task behavioral consistency.' The core argument is that an agent's performance on a single benchmark is insufficient to predict its reliability in real-world applications where the agent must navigate varied, non-linear task environments. The authors define consistency not merely as output accuracy, but as the stability of an agent's decision-making logic and operational patterns when faced with functionally similar but contextually distinct challenges.

The Challenge of Behavioral Drift

The research highlights that LLM agents often exhibit 'behavioral drift'—where the underlying reasoning process changes significantly even when the task objective remains constant. By mapping agent trajectories across different task domains, the authors identify that models frequently rely on brittle heuristics rather than robust internal representations. This lack of consistency poses a significant risk for production systems, as it makes agent behavior difficult to debug, audit, or predict under edge-case conditions. The framework provides a methodology for developers to stress-test agents by measuring the variance in their action sequences, helping to distinguish between models that 'solve' a task through robust reasoning versus those that succeed through lucky prompt-alignment or memorization.