AI Agent Overview
DevFlow is built for AI-assisted development. It connects to AI coding agents like Claude through a standard protocol, giving the agent structured access to your flows, tasks, and project context. The agent works within the same workflow you see in the UI — planning, implementing, and reviewing — while you stay in control at every step.
How It Works
Section titled “How It Works”DevFlow communicates with AI agents through the Model Context Protocol (MCP). This gives the agent a set of tools to interact with your project:
- Read flows and tasks — The agent sees what needs to be done
- Create plans and tasks — The agent breaks work into structured steps
- Update progress — The agent reports what it has done
- Submit for review — The agent hands work back to you for approval
The agent never works in a vacuum. Every action it takes is visible in DevFlow, and every major decision requires your approval.
Agent Status Tracking
Section titled “Agent Status Tracking”You can see what the agent is doing at any time. The agent reports its current status as it works:
| Status | Meaning |
|---|---|
| Idle | Not currently working on anything |
| Analyzing | Reading requirements and exploring the codebase |
| Planning | Creating an implementation plan |
| Implementing | Writing code |
| Testing | Running and verifying tests |
| Reviewing | Checking its own work before submitting |
Status Indicator
Section titled “Status Indicator”The header bar shows an agent status indicator whenever the agent is actively working. Click it to open a dropdown with details about:
- Which flow the agent is working on
- What the agent is currently doing
- How long the current session has been running
This gives you a quick glance at agent activity without leaving your current view.
Pending Questions
Section titled “Pending Questions”Sometimes the agent needs clarification before it can continue. When this happens, you receive a notification in DevFlow. The agent pauses and waits for your response before proceeding.
This keeps the human-in-the-loop principle intact — the agent does not guess when it is unsure.
Agent Sessions
Section titled “Agent Sessions”Every unit of work the agent performs is logged as an agent session. A session records:
- Start and end time — When the agent began and finished working
- Activity log — Key decisions, progress updates, and any issues encountered
- Summary — A brief description of what was accomplished
You can view all agent sessions for a flow in the flow detail view. This gives you a complete history of the AI’s work, which is useful for auditing, debugging, or understanding how a feature was built.
Agent Time Tracking
Section titled “Agent Time Tracking”AI work time is tracked separately from your own time. This distinction matters because:
- You can see how much time the AI spent versus how much you spent
- Time reports clearly separate human and machine effort
- You get an accurate picture of total project effort
Agent time is calculated automatically from session start and end timestamps. It appears alongside your manual time entries in the flow detail and project dashboard.
What You Need
Section titled “What You Need”To use the AI agent integration, you need:
- An AI assistant that supports MCP — such as Claude Code
- The DevFlow MCP server — installed and configured to connect to your DevFlow instance
See MCP Setup for step-by-step installation instructions.