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PrefixGuard: From LLM-Agent Traces to Online Failur... | AI Research

Key Takeaways

  • Large language model (LLM) agents are increasingly used for complex, multi-step tasks in high-stakes fields like software engineering and finance.
  • Large language model (LLM) agents now execute long, tool-using tasks where final outcome checks can arrive too late for intervention.
  • Online warning requires lightweight prefix monitors over heterogeneous traces, but hand-authored event schemas are brittle and deployment-time LLM judging is costly.
  • We introduce PrefixGuard, a trace-to-monitor framework with an offline StepView induction step followed by supervised monitor training.
  • StepView induces deterministic typed-step adapters from raw trace samples, and the monitor learns an event abstraction and prefix-risk scorer from terminal outcomes.
Paper AbstractExpand

Large language model (LLM) agents now execute long, tool-using tasks where final outcome checks can arrive too late for intervention. Online warning requires lightweight prefix monitors over heterogeneous traces, but hand-authored event schemas are brittle and deployment-time LLM judging is costly. We introduce PrefixGuard, a trace-to-monitor framework with an offline StepView induction step followed by supervised monitor training. StepView induces deterministic typed-step adapters from raw trace samples, and the monitor learns an event abstraction and prefix-risk scorer from terminal outcomes. Across WebArena, $\tau^2$-Bench, SkillsBench, and TerminalBench, the strongest PrefixGuard monitors reach 0.900/0.710/0.533/0.557 AUPRC. Using the strongest backend within each representation, they improve over raw-text controls by an average of +0.137 AUPRC. LLM judges remain substantially weaker under the same prefix-warning protocol. We also derive an observability ceiling on score-based area under the precision-recall curve (AUPRC) that separates monitor error from failures lacking evidence in the observed prefix. For finite-state audit, post-hoc deterministic finite automaton (DFA) extraction remains compact on WebArena and $\tau^2$-Bench (29 and 20 states) but expands to 151 and 187 states on SkillsBench and TerminalBench. Finally, first-alert diagnostics show that strong ranking does not imply deployment utility: WebArena ranks well yet fails to support low-false-alarm alerts, whereas $\tau^2$-Bench and TerminalBench retain more actionable early alerts. Together, these results position PrefixGuard as a practical monitor-synthesis recipe with explicit diagnostics for when prefix warnings translate into actionable interventions.

Large language model (LLM) agents are increasingly used for complex, multi-step tasks in high-stakes fields like software engineering and finance. Because these tasks can take a long time to complete, a single mistake early on can lead to failure long before the final outcome is checked. PrefixGuard is a new framework designed to provide "online" warnings, allowing systems to detect when an agent is drifting toward failure while the task is still in progress, rather than waiting for a final, potentially delayed, verification.

From Raw Traces to Actionable Warnings

Existing methods for monitoring AI agents often rely on hand-written rules that are difficult to maintain as agent behaviors evolve, or they use LLMs to judge performance in real-time, which is prohibitively expensive. PrefixGuard solves this by treating monitor synthesis as a data-driven problem. It uses a component called "StepView" to convert messy, heterogeneous logs from different agent environments into a standardized format. This allows the system to learn from raw execution traces without needing manual event definitions or costly LLM inference during deployment.

How PrefixGuard Works

The framework operates in two main phases. First, StepView uses an offline, LLM-assisted process to create deterministic adapters that parse raw agent steps into structured fields. Second, the system trains a neural-symbolic monitor. This monitor includes an "event abstraction layer" that learns to group raw actions into a discrete set of symbols, which are then processed by a backend—such as a GRU or Transformer—to calculate a real-time risk score. Because the system learns these symbols end-to-end, it can adapt to the specific failure patterns of different benchmarks, from browser navigation to command-line interface tasks.

Evaluating Performance and Auditability

The researchers tested PrefixGuard across four diverse benchmarks: WebArena, $\tau^2$-Bench, SkillsBench, and TerminalBench. The results show that PrefixGuard consistently outperforms raw-text baseline models. A key feature of the framework is its ability to extract a Deterministic Finite Automaton (DFA) from the learned symbols. This allows for "finite-state auditing," where the monitor’s logic can be inspected as a compact state machine. The study found that while this audit remains compact for some tasks, it expands in complexity for others, providing a diagnostic boundary for when a system is simple enough to be fully audited versus when it requires more complex neural monitoring.

Beyond Simple Ranking

A critical contribution of this research is the distinction between "ranking" and "deployment utility." While a model might achieve a high score in ranking (AUPRC), it may not be useful for early intervention if it cannot distinguish between a minor drift and a guaranteed failure. The authors introduced an "observability ceiling" to help diagnose whether a failure is actually detectable from the current prefix or if it remains hidden until the very end. By analyzing "first-alert" diagnostics, the team demonstrated that some benchmarks are better suited for early, low-false-alarm interventions than others, providing a practical roadmap for developers to determine if their monitoring system is truly ready for real-world deployment.

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