# Near-Miss: Latent Policy Failure Detection in Agentic Workflows

arXiv: https://arxiv.org/abs/2603.29665 · HTML: https://mlsanity.com/paper/2603.29665/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 9 of its 26 references that are in the map.
- First posted on arXiv 31 Mar 2026.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 9 of 9)

- [τ-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains](https://mlsanity.com/paper/2406.12045/) (2024)
- [AgentSpec: Customizable Runtime Enforcement for Safe and Reliable LLM Agents](https://mlsanity.com/paper/2503.18666/) (2025)
- [TradingAgents: Multi-Agents LLM Financial Trading Framework](https://mlsanity.com/paper/2412.20138/) (2024)
- [CRMArena: Understanding the Capacity of LLM Agents to Perform Professional CRM Tasks in Realistic Environments](https://mlsanity.com/paper/2411.02305/) (2024)
- [SABER: Small Actions, Big Errors -- Safeguarding Mutating Steps in LLM Agents](https://mlsanity.com/paper/2512.07850/) (2025)
- [Towards Enforcing Company Policy Adherence in Agentic Workflows](https://mlsanity.com/paper/2507.16459/) (2025)
- [Effective Red-Teaming of Policy-Adherent Agents](https://mlsanity.com/paper/2506.09600/) (2025)
- [ARPaCCino: An Agentic-RAG for Policy as Code Compliance](https://mlsanity.com/paper/2507.10584/) (2025)
- [TOD-ProcBench: Benchmarking Complex Instruction-Following in Task-Oriented Dialogues](https://mlsanity.com/paper/2511.15976/) (2025)

## Built on by (most cited first, 4 of 4)

- [PolicyGuard: A Dialogue-Grounded Sub-Agent Verifier for Policy Adherence in LLM Agents](https://mlsanity.com/paper/2606.29225/) (2026)
- [PolicyGuide: From Guarding One Action to Guiding the Whole Workflow for Policy-Compliant LLM Agents](https://mlsanity.com/paper/2608.19861/) (2026)
- [Policy Loopholes in Agent Evaluation: When Policy Ambiguity Masquerades as Agent Error](https://mlsanity.com/paper/2609.14400/) (2026)
- [Authorization Closure Graph: Minimal Repair for LLM Agents with Evolving User Instructions](https://mlsanity.com/paper/2609.32428/) (2026)

## Abstract (arXiv)

Agentic systems for business process automation often require compliance with policies governing conditional updates to the system state. Evaluation of policy adherence in LLM-based agentic workflows is typically performed by comparing the final system state against a predefined ground truth. While this approach detects explicit policy violations, it may overlook a more subtle class of issues in which agents bypass required policy checks, yet reach a correct outcome due to favorable circumstances. We refer to such cases as near-misses or latent failures. In this work, we introduce a novel metric for detecting latent policy failures in agent conversations traces. Building on the ToolGuard framework, which converts natural-language policies into executable guard code, our method analyzes agent trajectories to determine whether agent's tool-calling decisions where sufficiently informed. We evaluate our approach on the τ2-verified Airlines benchmark across several contemporary open and proprietary LLMs acting as agents. Our results show that latent failures occur in 8-17% of trajectories involving mutating tool calls, even when the final outcome matches the expected ground-truth state. These findings reveal a blind spot in current evaluation methodologies and highlight the need for metrics that assess not only final outcomes but also the decision process leading to them.