Monitoring Web Agents Without Internal Signals: Observable Trajectories and Key-Step Supervision
Proposes prefix-level risk prediction for web agents using observable trajectory signals when internal model signals (e.g., token logits) are unavailable Introduces two trajectory representations: Macro features (cross-step agent-environment behavior) and Micro features (intention-action-state consistency via black-box queries) Introduces key-step supervision, labeling the first uncorrected critical error as a boundary to preserve valid early prefixes of failed trajectories Achieves competitive
Analysis
TL;DR
- Proposes prefix-level risk prediction for web agents using observable trajectory signals when internal model signals (e.g., token logits) are unavailable
- Introduces two trajectory representations: Macro features (cross-step agent-environment behavior) and Micro features (intention-action-state consistency via black-box queries)
- Introduces key-step supervision, labeling the first uncorrected critical error as a boundary to preserve valid early prefixes of failed trajectories
- Achieves competitive performance against internal-signal baselines across WebArena-Lite and Online Mind2Web benchmarks with five backbones
- Supports early intervention under fixed false-cut budgets and transfers across held-out website categories
Why It Matters
This work addresses a critical practical gap: many production web-agent systems operate behind APIs where internal model uncertainty signals are inaccessible, yet reliable monitoring is essential for safety and cost control. By demonstrating that observable trajectory signals can match internal-signal baselines, it provides a viable path for deploying risk-aware web agents in real-world settings where model internals are opaque.
Technical Details
- Macro features aggregate cross-step agent-environment interactions and feedback signals to capture high-level behavioral patterns throughout the trajectory
- Micro features measure consistency between the agent's stated intention, executed action, and anticipated state change through repeated black-box queries to the model
- Key-step supervision replaces final-result labels by identifying the first critical uncorrected error in a trajectory as a boundary point, treating all prefixes before that point as "on track"
- Evaluated on WebArena-Lite and Online Mind2Web benchmarks using five open- and closed-source model backbones
- Predictors support early intervention under fixed false-cut budgets and demonstrate cross-category transfer to held-out website domains
Industry Insight
- API-only access to LLMs is the norm in enterprise deployments; this work provides a monitoring framework that does not require internal model access, making it immediately applicable to production systems
- Key-step supervision offers a principled alternative to trajectory-level labeling, which is often noisy and misleading—this could influence how future agent evaluation benchmarks are designed
- Early intervention with bounded false-cut budgets enables cost-effective safety nets; practitioners should consider integrating observable-trajectory monitors as a lightweight layer above existing agent frameworks
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