Can an AI agent complete a task and still fail?
Summary
This paper introduces the concept of 'Verifier Tax' to categorize AI agent outcomes as safe success, unsafe success, or failure, and proposes a two-tier verification architecture for tool-using LLM agents.
Similar Articles
Should AI agent benchmarks separate “safe success” from “unsafe success”?
This article discusses the concept of 'Verifier Tax' in AI agent benchmarks, distinguishing between safe success (completing tasks without violating constraints) and unsafe success (completing tasks but violating constraints), and questions how to properly measure agent performance considering safety tradeoffs.
The Verifier Tax: Horizon-Dependent Safety–Success Tradeoffs in Tool-Using LLM Agents [R]
This paper presents a safety evaluation framework for tool-using LLM agents, introducing the concept of the 'Verifier Tax'—a horizon-dependent tradeoff between safety and task completion. It proposes a two-tier verification architecture and uses Tau-bench scenarios to demonstrate how verification can reduce unsafe successes but also decrease task completion as task horizon increases.
From Confident Closing to Silent Failure: Characterizing False Success in LLM Agents
This paper characterizes 'false success' in LLM agents, where agents claim task completion despite environment state showing otherwise, finding it accounts for 45-75% of failures across benchmarks. LLM judges fail to detect this reliably, while lightweight TF-IDF detectors achieve high AUROC with much lower latency, suggesting production monitoring should use calibrated detectors instead of LLM judges.
The Verification Horizon: No Silver Bullet for Coding Agent Rewards
This paper explores the challenges of verifying AI coding agents' outputs, arguing that verification is becoming harder than generation as models improve. It analyzes four reward constructions and shows that no fixed reward function remains effective as model capability grows.
How do you handle the 'verification gap' when an agent completes a long-running task?
Discusses the difficulty of verifying outputs from autonomous agents after long-running tasks and asks about using critic agents or traceability tools to ensure trustworthiness.