Cadence: Strategic Guidance for Coding Agents

2026-10-08

AI Summary

This study addresses the limitations of static monitoring mechanisms in LLM-based coding agents, including delayed intervention, excessive overhead, and difficulty detecting complex reasoning errors. We propose a dynamic monitoring framework grounded in execution health assessment. This framework introduces a novel adaptive scheduling mechanism that dynamically adjusts inspection frequency based on the agent's real-time state. Furthermore, it incorporates a dual-layer intervention module comprising suggestion-level and replacement-level components, which delivers corrective guidance stratified by error severity to balance precision with efficiency. The proposed method has been integrated into systems such as mini-swe-agent, achieving up to a 25.33% improvement in resolution rate on SWE-bench Lite while maintaining high token efficiency, thereby significantly enhancing the reliability of code generation.

Abstract

Runtime monitors are increasingly used to improve the reliability of LLM-based coding agents by inspecting execution trajectories and delivering corrective guidance upon detecting misbehavior. However, their effectiveness remains limited by static guidance triggering schemes. Existing monitors rely either on fixed inspection intervals, missing timely guidance during severe misbehaviors while incurring unnecessary overhead during healthy execution, or on rigid heuristic rules, failing to detect complex reasoning errors. To address these limitations, we propose Cadence, a dynamic monitoring framework that adaptively schedules inspections and delivers guidance according to the agent's real-time execution health. Cadence consists of two core modules: a two-tier intervention module and an inspection scheduler. The intervention module delivers advisory-level guidance for normal executions and minor lapses, while providing replacement-level guidance for severe misbehaviors. Driven by the intervention level, the scheduler adjusts the inspection frequency by tightening supervision after replacement-level guidance and relaxing it after advisory-level guidance. Evaluated on 300 SWE-bench Lite tasks across two distinct agents, mini-swe-agent and Moatless, Cadence achieves the highest resolve rate among all evaluated monitors. Specifically, Cadence outperforms vanilla agents by 25.33\% (+76 resolved tasks) on mini-swe-agent and 15.67\% (+47 resolved tasks) on Moatless, while maintaining competitive token efficiency compared to state-of-the-art baselines.

Problem

• coding agents
• runtime monitors
• LLM
• static guidance triggering
• reliability

Innovation

• Dynamic Monitoring Framework
• Adaptive Scheduling
• Two-tier Intervention
• Coding Agents
• Runtime Monitors