VS Code Agent Kanban: Markdown-based task management for AI coding agents

What VS Code Agent Kanban does

VS Code Agent Kanban is a VS Code extension that provides task management specifically designed for developers using AI coding agents like GitHub Copilot. It solves the problem of context rot where planning conversations, decisions, and rationale disappear when chat sessions are cleared or closed.

Key features

• GitOps & team friendly kanban board integration inside VS Code - The entire .agentkanban/ folder is designed to be committed to version control
• Structured plan / todo / implement via @kanban commands - Users type commands like 'plan', 'todo', and 'implement' in the chat window to guide the agent workflow
• Leverages your existing agent harness - Uses your current AI coding agent setup rather than bundling a custom one
• .md task format as permanent source of truth - Each task is a Markdown file with YAML frontmatter that tracks title, kanban lane, and timestamps

How it works

Every task lives in a .agentkanban/tasks/ folder as a .md file. The body contains structured conversation logs using [user] and [agent] markers. Here's the example format from the source:

---
title: Implement OAuth2
lane: doing
created: 2026-03-08T10:00:00.000Z
updated: 2026-03-08T14:30:00.000Z
description: OAuth2 integration for the API
---
## Conversation
[user] Let's plan the OAuth2 implementation. We need to support both device code and client credentials flows.
[agent] Here's my analysis of the two OAuth2 approaches for your API...
[user] Make changes here, here and here
[agent] Here's my updated plan ...

The extension creates a clear plan/todo/implement flow where users confirm readiness in GitHub Copilot chat, then the agent harness starts work with all the context preserved in the markdown file.

Benefits for teams

Because tasks are stored as plain text files:

• They're diffable and mergeable naturally - no merge conflicts from opaque binary state
• Teams get shared visibility into what the AI is working on and what's been decided
• Future developers can see not just what was built but why, in the actual words of the planning conversation
• Provides an audit trail for regulated or enterprise environments

This approach is particularly useful for developers who regularly hit context limits with AI agents or need to maintain continuity across multiple work sessions.