Deep Work Plan: An open-source tool that gives AI agents structured plans to prevent task drift in long coding sessions
By
Oscar Humberto Marín Molina
Summary
Deep Work Plan is an open-source tool that transforms any code repository into a structured development harness for AI agents. Instead of relying on chat windows where context gets lost over long sessions, it embeds a spec directly into the repo with atomic tasks, acceptance criteria, validation gates, and resumable state. This allows AI agents to maintain focus on long-running tasks, survive context resets, and have any agent pick up where another left off — solving the problem of AI agents drifting from the original goal during extended work sessions.
Source
Key quotes
· 5 pulledModels matter. Context matters more.
I stopped treating that as a prompting problem and started treating it as a structural one.
The fix wasn't a smarter model. It was giving the agent a plan.
Long runs survive context resets; any agent picks up where the last left off.
Point an agent at it, walk away, come back to work you can verify.
You might also wanna read
Building an AI Agent From Scratch: Implementing Long Task Planning
This article is part of a tutorial series on building a basic AI agent from scratch. It focuses on implementing long task planning capabilit
A Structured Workflow for Using Claude Code: Research-Plan-Implement Methodology
The article describes an author's unique workflow for using Claude Code, an AI coding assistant, emphasizing a structured approach where cod
DeepSWE: A New Long-Horizon Benchmark for Evaluating Frontier Coding Agents on Complex Engineering Tasks
DeepSWE is a new long-horizon software engineering benchmark designed to evaluate frontier coding agents on original, complex engineering ta
Disciplined AI Software Development: A Structured Methodology for AI Collaboration in Software Projects
This article presents a structured methodology called 'Disciplined AI Software Development' for collaborating with AI systems on software de
A Field Guide to Production-Ready AI Agents: Context Windows, Security, and Drift Monitoring
Karl Mehta presents a field guide for building production-ready AI agents, focusing on four key engineering challenges: context-window disci
ClioAI's kw-sdk: A Python SDK for Building AI Agents for Knowledge Work Tasks
The article introduces a Python SDK called kw-sdk for building AI agents that perform knowledge work tasks like research, analysis, writing,
