PrefixGuard: From LLM-Agent Traces to Online Failure-Warning Monitors

PrefixGuard introduces a novel framework for monitoring LLM-agent execution in real-time by detecting failures before they occur through prefix analysis rather than post-hoc outcome checks. The system combines offline trace induction with supervised learning to achieve strong performance across multiple benchmarks, outperforming both raw-text baselines and direct LLM judging approaches.

PrefixGuard addresses a critical challenge in autonomous LLM-agent systems: the timing gap between task execution and failure detection. When agents perform multi-step tool-using tasks, final outcome verification often arrives too late for meaningful intervention. This research proposes a practical solution by shifting from reactive outcome monitoring to proactive prefix-based risk assessment.

The framework operates in two phases: StepView first induces deterministic typed-step adapters from raw execution traces, creating a structured representation of agent behavior. The monitor then learns to identify risk signals from terminal outcomes, building an abstraction layer that generalizes across heterogeneous trace formats. This approach avoids brittle hand-authored schemas while remaining computationally lightweight compared to deployment-time LLM judging.

The technical contributions extend beyond accuracy metrics. The researchers derive an observability ceiling on AUPRC scores, explicitly separating monitor failures from scenarios where insufficient evidence appears in observable prefixes. This diagnostic framework reveals a nuanced finding: strong ranking performance (WebArena) doesn't guarantee deployment utility if false-alarm rates remain unacceptably high, while other benchmarks (τ²-Bench, TerminalBench) maintain actionable early alerts.

For the AI infrastructure ecosystem, PrefixGuard's monitor-synthesis recipe enables safer deployment of autonomous agents in production environments. The finite-state DFA extractions provide interpretability, supporting auditing requirements in regulated applications. However, the significant state-space expansion on complex tasks (151-187 states) suggests scalability challenges for increasingly sophisticated agent behaviors. This work represents meaningful progress toward reliable agent monitoring but highlights remaining engineering challenges in production deployment.

• →PrefixGuard achieves 0.900 AUPRC on WebArena by learning failure patterns from execution prefixes rather than final outcomes
• →The framework outperforms LLM judges by +0.137 AUPRC on average through structured trace adaptation and supervised risk scoring
• →Observability ceiling analysis reveals that ranking strength doesn't guarantee deployment utility when false-alarm rates compromise actionability
• →DFA extraction remains compact on simpler tasks (20-29 states) but scales to 151-187 states on complex benchmarks, indicating scalability constraints
• →Strong early-alert capability on τ²-Bench and TerminalBench positions PrefixGuard for practical deployment in monitored agent environments