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Efficient Agent Evaluation via Diversity-Guided Use... | AI Research

Key Takeaways

  • Efficient Agent Evaluation via Diversity-Guided User Simulation Evaluating customer-facing AI agents is difficult because their behavior is stochastic and un...
  • Large language models (LLMs) are increasingly deployed as customer-facing agents, yet evaluating their reliability remains challenging due to stochastic, multi-turn interactions.
  • Current evaluation protocols rely on linear Monte Carlo rollouts of complete agent-user conversations to estimate success.
  • However, this approach is computationally inefficient, repeatedly regenerating identical early prefixes, and often fails to uncover deep failure modes that arise from rare user behaviors.
  • We introduce DIVERT (Diversity-Induced Evaluation via Branching of Trajectories), an efficient, snapshot-based, coverage-guided user simulation framework for systematic exploration of agent-user interactions.
Paper AbstractExpand

Large language models (LLMs) are increasingly deployed as customer-facing agents, yet evaluating their reliability remains challenging due to stochastic, multi-turn interactions. Current evaluation protocols rely on linear Monte Carlo rollouts of complete agent-user conversations to estimate success. However, this approach is computationally inefficient, repeatedly regenerating identical early prefixes, and often fails to uncover deep failure modes that arise from rare user behaviors. We introduce DIVERT (Diversity-Induced Evaluation via Branching of Trajectories), an efficient, snapshot-based, coverage-guided user simulation framework for systematic exploration of agent-user interactions. DIVERT captures the full agent-environment state at critical decision points and resumes execution from these snapshots, enabling reuse of shared conversation prefixes and reducing redundant computation. From each junction, the framework branches using targeted, diversity-inducing user responses, allowing directed exploration of alternative interaction paths. By focusing evaluation on semantically diverse and underexplored trajectories, DIVERT improves both efficiency and coverage. Empirical results show that it discovers more failures per token compared to standard linear rollout protocols, while expanding the set of tasks on which failures are identified.

Efficient Agent Evaluation via Diversity-Guided User Simulation
Evaluating customer-facing AI agents is difficult because their behavior is stochastic and unfolds over long, multi-turn conversations. Current methods rely on "linear rollouts," where the entire conversation is generated from scratch every time a test is run. This is both expensive and inefficient, as it forces the system to repeatedly regenerate the same early conversation steps. This paper introduces DIVERT, a framework that treats agent-user interactions as a tree structure rather than a line, allowing for more efficient and thorough testing of agent reliability.

Moving Beyond Linear Testing

Standard evaluation methods are wasteful because they regenerate identical conversation prefixes—such as initial greetings or routine diagnostic questions—for every single test run. DIVERT changes this by using a snapshot-based approach. It saves the state of the agent and the environment at critical "junctions" in the conversation. Instead of restarting from the beginning, the system can resume from these saved snapshots, significantly reducing the number of tokens required to run multiple test scenarios.

Targeted Exploration of Failure Modes

A major limitation of traditional testing is that it often fails to uncover "deep" failure modes—errors that only appear after specific, rare user behaviors. DIVERT addresses this by using a "junction chooser" to identify key moments in a conversation where a different user response could lead to a new, unexplored path. Once a junction is selected, the framework generates diverse, intent-consistent user responses to steer the conversation toward these potentially problematic areas. By focusing on these semantically distinct paths, the system can systematically test how robust an agent is against unusual or challenging user inputs.

Improved Efficiency and Coverage

The researchers tested DIVERT across several service-oriented domains, such as airline, retail, and telecom support. The results show that DIVERT is consistently more efficient than standard linear rollouts, discovering more agent failures per token generated. Furthermore, because the framework actively branches into different interaction paths, it achieves broader coverage, identifying failures in a larger number of unique tasks. This suggests that by reallocating computational resources from redundant prefix generation to targeted branching, developers can gain a much clearer picture of an agent’s reliability.

Key Considerations

While DIVERT significantly improves the efficiency and depth of agent evaluation, it is designed to be a flexible framework rather than a specific type of user simulator. It can be paired with various user strategies, including benign, adversarial, or red-teaming policies. The framework’s effectiveness relies on its ability to generate meaningful variations in user behavior while maintaining the original task's intent. By providing a structured way to explore the "tree" of possible conversations, DIVERT offers a more scalable and informative approach to ensuring that AI agents perform reliably in real-world, multi-turn interactions.

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