ControlScope: Workflow Revision and Reliability in LLM Agents

2026-09-28

AI Summary

This study addresses the critical question of how much content LLM agents should revise during workflow execution to enhance reliability. Methodologically, it proposes a nested permission framework that decouples the available repair space from agent action selection, systematically comparing three strategies: continuing execution, editing parameters, and replacing workflows. Furthermore, multi-turn reasoning is evaluated by integrating one-shot and iterative review mechanisms. Experiments conducted on file system tasks and the ALFWorld/AppWorld benchmarks demonstrate that the full replacement strategy achieves the highest success rate. The results also quantitatively reveal the trade-off between output savings and success rates introduced by conservative invocation.

Abstract

How much of a running workflow should a language model agent revise? ControlScope compares continuing generated code, editing the next tool call's data arguments, and replacing the unfinished workflow from the same public execution state. The nested permissions separate available repairs from the actions an agent selects. We evaluate one-time and repeated reviews across filesystem tasks, ALFWorld, and AppWorld. Across two source programs per task and three reasoning-reviewer draws on 20 filesystem tasks, FULL completes 15-16 tasks versus 13 for KEEP; across four fast draws it completes 10-13 versus 13. Fresh student-record confirmation reproduces a batch-read repair. ALFWorld fast panels yield KEEP/ARG/FULL scores of 85/86/87 on 87 tasks across 52 scenes and 134/134/127 on 134 tasks across four scenes; reasoning on the 87-task cohort also yields 85/86/87 with substantial review cost. An AppWorld V1 official-test panel of 585 task instances from 195 scenario templates shows small net differences. Frozen replays expose viable agent-written replacements interrupted by later revision in two failed file-organization runs. An offline source-trajectory midpoint comparison shows later reviews completing an insufficient repair. Five-call protection saves 19.4% of logged model output and loses one success across 20 fresh source runs. An argument-only shortcut shows that the broader sampled policy can overlook a cheaper successful edit available in both operation sets. These outcomes tie repair access to actual choices and subsequent execution.

Problem

• LLM Agents
• Workflow Revision
• Control Scope
• Reliability
• Tool Use

Innovation

• workflow revision
• LLM agents
• nested permissions
• repair granularity
• execution reliability