AI

Agent Harness Awareness: Turning Verified Telemetry into Operational Control for Tool-Using Agents

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Briefings

This briefing reports on a new method called Agent Harness Awareness that uses verified system state snapshots to prevent tool misuse. The study tested this approach against standard benchmarks using GPT-5.6, Claude Sonnet 5, and Gemini 3.7 Flash models. Results showed a 96.9% reduction in regret and a 95.8% increase in successful single-dispatch completions when verified health states were provided. A live replication test further confirmed zero excess operational cost during actual execution.

Key points

• The method introduces a trust-separated design where the host provides a verified state snapshot before a tool action, preventing the model from acting on unsafe tools.
• In a 216-decision ablation, verified state reduced mean observable policy regret by 96.9% compared to a neutral control, while completing 95.8% of single-dispatch tasks successfully.

Context

Tool descriptions define actions but lack real-time health data, creating 'operational blindness'. Agent Harness Awareness fills this gap by supplying verified state snapshots to host systems, enabling safer tool selection without compromising model autonomy.

Limitations

Evidence is limited to a 216-decision ablation and a 27-decision end-to-end replication with simulated faults. The architecture remains open for future extensions regarding runtime state, hardware pressure, and heterogeneous selection.

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