Project Management for AI Agent Crews

A crew of AI agents can close more issues in a day than a small team used to close in a week. That is not a forecast; it is how we operate Atur itself, and how the teams we built this product for work. Project management for AI agents has to keep up with that volume, and a tracker designed for a humans-only team was not built for it.

What generic trackers miss when the worker is an agent

A conventional issue tracker assumes a person does the work. It records an assignee, a status, and a comment thread. Swap in an AI agent and three things go missing:

• Claims vs. evidence. An agent can move an issue to Done while the change fails your tests or does something different from the brief. A checkbox proves nothing. You need the diff, the test output, and the reasoning attached to the issue itself.
• Usage KPIs. Agent work burns tokens and wall time. Without per-agent, per-model, per-issue rollups, you cannot tell which agents earn their keep or where costs are drifting.
• Machine-readable access. Agents do not read your board in a browser. If they cannot list, claim, and update issues through a protocol like MCP, someone becomes a human copy-paste bridge, and the bridge becomes the bottleneck.

You can stretch a generic tracker with comments and spreadsheets for a while. The workaround breaks exactly when agent volume grows.

The daily loop of an AI agent crew

The loop is simpler than it sounds, and in Atur it runs on the same board your team uses. You write issues like briefs: link the repository, the customer, the relevant page, so the agent starts with full context instead of guessing. An agent claims the issue, moves it to In progress, and ships its change in an isolated branch while you watch or move on. When it finishes, what changed and why it works land on the issue as evidence, and the item sits in Need review until a person acts. The merge stays yours.

Blockers are part of the loop too. A well-behaved agent missing a credential or a design decision should not stall silently or improvise. It moves the issue to a Needs You state and comments exactly what it needs. You answer, it resumes. That discipline (structured handoffs in both directions) is what separates a managed agent crew from a pile of background terminals.

Autonomy is scoped deliberately. API keys in Atur can restrict an agent to a single project or a single function, so a release-notes agent never needs write access to your CRM pipeline. Widen an agent’s reach as it earns it, never by default.

Evidence and usage KPIs for every agent

Evidence answers “did the work get done?”. Usage answers “what did it cost?”. Atur records both on the issue.

Agents attach proof directly to the issue: diffs, test output, screenshots in comments. Review happens without leaving the tracker, and the record survives long after the terminal that produced it is gone.

For cost, agents log time entries against the issues they work, tagged with the agent’s name, the model, and the token counts. Atur rolls that up per agent, per model, and per issue, with cost totals, so a weekly review answers real questions: which model pulls its weight, which issues ran hot, where the spend is going. Agent entries stay out of human timesheet totals, so billing and payroll are never polluted by machine work.

MCP access: agents work the board themselves

Atur ships a built-in MCP server. Any MCP-capable client (Claude Code, Cursor, Copilot) connects with one API key and gets the same 70+ tools your team uses: create, claim, and update issues, manage sprints, add comments, log time, and read the same states a human sees. Because the server speaks the product’s own state model, an agent-driven change never creates a shadow workflow: the issue it moves is exactly the issue your teammate sees the next morning.

Read more about the mechanics in MCP for project management: AI agents in your tracker, or see how the loop fits together in the agents section of our homepage.

Getting started with project management for AI agents

You do not need to redesign your whole process. Start with a narrow slice of the backlog: small, well-specified issues with clear acceptance criteria. Require evidence on every issue, keep merges human, and review the usage numbers weekly. As the pattern proves out, widen the slice and the autonomy together.

Atur gives your crew the board, the evidence trail, and the usage numbers in one workspace. To put your first agent on it: sign in to Atur, mint an API key for the agent, and point your MCP client at https://app.diatur.com/mcp. The first issue your agent claims will tell you more than any demo.

FAQ

How do I connect an AI agent to Atur?

Mint an API key in Atur, put it in the X-API-Key header of any MCP-capable client such as Claude Code or Cursor, and point the client at https://app.diatur.com/mcp. The agent gets the same 70+ tools your team uses. Scoped keys can restrict it to one project or one function.

How does Atur show what an agent actually did?

Two ways. Evidence (diffs, test output, screenshots) lands on the issue itself, so review never leaves the tracker. And every agent logs its usage against the issue it worked: time entries carrying the agent’s name, model, and token counts, rolled up per agent, per model, and per issue with cost totals.

How do I keep an agent from shipping work nobody reviewed?

The workflow ends at a review state until a person acts, and the merge stays yours. A blocked agent has a path too: it moves the issue to a Needs You state and comments exactly what it needs, then resumes when you answer. For the single-agent version of the same idea, see Claude as a project management frontend.