# Preventing Premature Commitment in Coding Agents with an Evidence-Conditioned Execution Layer

arXiv: https://arxiv.org/abs/2607.28815 · HTML: https://mlsanity.com/paper/2607.28815/

## Key facts

- Cited by 3 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 17 of its 27 references that are in the map.
- First posted on arXiv 30 Jul 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, 17 of 17)

- [SWE-bench: Can Language Models Resolve Real-World GitHub Issues?](https://mlsanity.com/paper/2310.06770/) (2023)
- [OpenHands: An Open Platform for AI Software Developers as Generalist Agents](https://mlsanity.com/paper/2407.16741/) (2024)
- [AgentSpec: Customizable Runtime Enforcement for Safe and Reliable LLM Agents](https://mlsanity.com/paper/2503.18666/) (2025)
- [GuardAgent: Safeguard LLM Agents by a Guard Agent via Knowledge-Enabled Reasoning](https://mlsanity.com/paper/2406.09187/) (2024)
- [AGrail: A Lifelong Agent Guardrail with Effective and Adaptive Safety Detection](https://mlsanity.com/paper/2502.11448/) (2025)
- [VeriGuard: Enhancing LLM Agent Safety via Verified Code Generation](https://mlsanity.com/paper/2510.05156/) (2025)
- [Enforcing Temporal Constraints for LLM Agents](https://mlsanity.com/paper/2512.23738/) (2025)
- [MI9: An Integrated Runtime Governance Framework for Agentic AI](https://mlsanity.com/paper/2508.03858/) (2025)
- [LLM Agents Already Know When to Call Tools -- Even Without Reasoning](https://mlsanity.com/paper/2605.09252/) (2026)
- [SWE-TRACE: Optimizing Long-Horizon SWE Agents Through Rubric Process Reward Models and Heuristic Test-Time Scaling](https://mlsanity.com/paper/2604.14820/) (2026)
- [Accurate Failure Prediction in Agents Does Not Imply Effective Failure Prevention](https://mlsanity.com/paper/2602.03338/) (2026)
- [Agent-SAMA: State-Aware Mobile Assistant](https://mlsanity.com/paper/2505.23596/) (2025)
- [Coding Agents Don't Know When to Act](https://mlsanity.com/paper/2605.07769/) (2026)
- [REFINE: Enhancing Program Repair Agents through Context-Aware Patch Refinement](https://mlsanity.com/paper/2510.03588/) (2025)
- [From Risk Classification to Action Plan Remediation: A Guardrail Feedback Driven Framework for LLM Agents](https://mlsanity.com/paper/2606.05805/) (2026)
- [SWE-Doctor: Guiding Software Engineering Agents with Runtime Diagnosis from Multi-Faceted Bug Reproduction Tests](https://mlsanity.com/paper/2607.00990/) (2026)
- [ReProAgent: Tool-Augmented Multi-Stage Agentic Generation of Bug Reproduction Tests from Issue Reports](https://mlsanity.com/paper/2607.09123/) (2026)

## Built on by (most cited first, 3 of 3)

- [ExecCritic: Learn to Test, Test to Improve for Coding Agents](https://mlsanity.com/paper/2609.09133/) (2026)
- [Why Didn't It Check? Unsupported Final Claims and Their Repair in Two Tool-Equipped Language Models](https://mlsanity.com/paper/2608.27768/) (2026)
- [What Can Component-Replacement Evidence Establish? A Critical Scoping Review of Local Decisions in LLM Agents](https://mlsanity.com/paper/2609.39989/) (2026)

## Abstract (arXiv)

LLM-based coding agents often edit source code or submit patches before examining enough repository evidence to justify the change, a failure pattern we call premature commitment. We present ECLoop, an execution layer that interposes between the agent and the repository to enforce evidence-conditioned execution. For each task, ECLoop uses the issue description and repository structure to compile a set of conditions specifying what the agent should observe before each type of code modification or patch submission. During execution, ECLoop tracks which conditions the agent's runtime trajectory has satisfied and postpones any proposed action whose required conditions remain unmet. Evaluated on all 500 instances of SWE-bench Verified with two language models and two agent scaffolds, ECLoop raises Pass@1 by 4.8-11.8 percentage points without model retraining or scaffold changes. Ablation experiments show that each of ECLoop's three operations contributes distinct value and that structured evidence conditions outperform an equivalent natural-language summary. These gains come at no additional inference cost: by redirecting the agent before it pursues unsupported actions, ECLoop lowers average token consumption by up to 12.1%.