Resilient Write: A Six-Layer Durable Write Surface for LLM Coding Agents

2026-04-12

AI Summary

This work addresses the vulnerability of large language model (LLM) coding agents to unstructured feedback and draft loss upon file write failures, which often leads to blind retry attempts. To mitigate diverse real-world write failure modes while preserving task continuity, the authors propose a six-layer persistence middleware situated between the agent and the file system. This middleware integrates orthogonal mechanisms—including pre-write risk scoring, atomic transactional writes, recoverable chunking, structured type-error feedback, out-of-band draft storage, and task handoff envelopes. Empirical evaluation demonstrates that each layer remains independently deployable while collectively reducing recovery time by 5× and improving the agent’s self-correction rate by 13× compared to baseline approaches.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Multiagent Systems: Agent Communication

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

LLM-powered coding agents increasingly rely on tool-use protocols such as the Model Context Protocol~(MCP) to read and write files on a developer's workstation. When a write fails -- due to content filters, truncation, or an interrupted session -- the agent typically receives no structured signal, loses the draft, and wastes tokens retrying blindly. We present \textbf{Resilient Write}, an MCP server that interposes a six-layer durable write surface between the agent and the filesystem. The layers -- pre-flight risk scoring, transactional atomic writes, resume-safe chunking, structured typed errors, out-of-band scratchpad storage, and task-continuity handoff envelopes -- are orthogonal and independently adoptable. Each layer maps to a concrete failure mode observed during a real agent session in April~2026, in which content-safety filters silently rejected a draft containing redacted API-key prefixes. Three additional tools -- chunk preview, format-aware validation, and journal analytics -- emerged from using the system to compose this paper. A 186-test suite validates correctness at each layer, and quantitative comparison against naive and defensive baselines shows a 5x reduction in recovery time and a 13x improvement in agent self-correction rate. Resilient Write is open-source under the MIT license.

Problem

• LLM coding agents
• durable write
• write failure
• tool-use protocols
• error recovery

Innovation

• Resilient Write
• durable write surface
• LLM coding agents
• Model Context Protocol
• failure recovery