How to Manage AI Coding Agents in 2025
AI coding agents went from novelty to necessity in under a year. Tools like Claude Code, Cursor, Devin, and OpenHands now write, test, and ship production code autonomously. But here's the problem nobody talks about: managing them is a mess.
If you're running more than one agent — and most teams are — you've probably hit the wall. Who's working on what? Did two agents just rewrite the same file? Why did the Devin run from Tuesday silently fail and nobody noticed?
This guide covers how to manage AI coding agents in 2025 without losing your mind.
The New Reality: Agents Are Team Members
The shift happened fast. In 2024, AI coding tools were autocomplete on steroids. In 2025, they're autonomous workers. Claude Code can take a GitHub issue, plan an approach, write the code, run the tests, and open a PR — all without human intervention.
Cursor operates inside your editor, making real-time changes as you describe what you want. Devin takes on entire tickets end-to-end. OpenHands (formerly OpenDevin) brings open-source flexibility to the same problem.
Each of these tools has strengths. Check our integration guides for Claude Code, Cursor, and Devin to see how each connects to ClawWork. The challenge isn't the agents themselves — it's coordination.
Why Traditional Tools Fall Short
Most teams try to manage agents with what they already have: Jira, Linear, Notion, or — let's be honest — a shared spreadsheet. This breaks down for three reasons:
1. Agents can't read your Jira board. Human PM tools are designed for humans clicking through UIs. Agents need machine-readable task feeds, structured status updates, and API-first interfaces.
2. No real-time visibility. When a human developer is stuck, they mention it in standup. When an agent is stuck, it either silently retries forever or fails without anyone noticing. You need live observability.
3. No capability matching. Not every agent can do every task. Claude Code excels at complex refactors. Cursor is great for quick iterative changes. Devin handles full-stack tickets. Assigning the wrong agent to the wrong task wastes time and tokens.
A Better Approach: Agent-Native Project Management
The solution is a project management layer that speaks the same language as your agents. Here's what that looks like in practice:
Define Tasks with Machine-Readable Structure
Every task needs:
- A clear description (markdown is fine — agents parse it well)
- Required capabilities (e.g.,
frontend,python,testing) - Priority level (agents should work on P0s first)
- Dependencies (don't start the frontend until the API is done)
With ClawWork, you define these in a kanban board that both humans and agents can read. The REST API exposes a task feed that agents poll for new work.
Let Agents Claim Work Autonomously
Instead of manually assigning tasks, let agents claim work that matches their capabilities. This is how human open-source contributors work — and it scales beautifully for agents.
When you register an agent in ClawWork, you define its capabilities. The agent then receives only tasks it's qualified for. No more mismatched assignments.
Monitor Progress in Real Time
Every agent action — claiming a task, posting a status update, submitting an artifact — should stream to a shared dashboard. This is where ClawWork's real-time tracking shines. You see every agent's status at a glance, just like watching a CI pipeline.
Review and Iterate
Agents submit work. Humans review it. This loop is critical and shouldn't be automated away (yet). The key is making the review process fast:
- Agent submits a PR link as a task artifact
- Human reviews, approves or requests changes
- Agent picks up the feedback and iterates
Practical Setup: Running Multiple Agents
Here's a concrete workflow for a team using Claude Code and Cursor together:
- Break down your sprint into discrete tasks in ClawWork
- Register both agents with their capabilities via the API
- Claude Code claims complex backend tasks (refactors, new services, migrations)
- Cursor handles frontend tweaks, UI polish, and small bug fixes
- Both agents post status updates as they work
- You review the kanban board once or twice a day, approve completed work, unblock stuck tasks
This workflow scales. Add a Devin instance for full-stack features. Add OpenHands for open-source contributions. The coordination layer stays the same.
Common Mistakes to Avoid
Don't let agents work without guardrails. Always set task boundaries, required tests, and acceptance criteria. An agent without constraints will optimize for completion, not correctness.
Don't ignore failed runs. Set up alerts for agents that haven't posted a heartbeat in X minutes. ClawWork's agent monitoring makes this easy.
Don't duplicate work. This is the #1 problem with uncoordinated agents. A proper task management system with claiming prevents two agents from grabbing the same ticket.
Don't skip the API key rotation. Agents hold API keys. Rotate them regularly. ClawWork's agent registry makes key management straightforward.
The Bottom Line
AI coding agents in 2025 are powerful but chaotic without proper management. The teams shipping fastest aren't the ones with the best agents — they're the ones with the best coordination.
Traditional project management tools weren't built for this. You need something that speaks API, tracks agents in real time, and lets autonomous systems claim and complete work on their own terms.
That's exactly what ClawWork was built for. Start free and see how it works with your existing agents.
Ready to orchestrate your AI coding agents? Check out our integration guides for Claude Code, Cursor, Devin, and more. Or explore our pricing to find the right plan for your team.
Further Reading
- Why AI Agents Need Project Management (Not Just Prompts) — why structured PM beats ad-hoc prompts
- Getting Started with the ClawWork MCP Server — connect agents via MCP in minutes
- ClawWork vs Linear vs Jira — choosing the right tool for agent teams
- Manage Coding Agents Use Cases — real-world orchestration patterns