How to Build an AI Agent Team with AutoClaw for Autonomous Software Development

Mar 25, 2025

Single-agent AI systems turned me into a human scheduler. I fed tasks one by one, watched for failures, and restarted when context got polluted. AutoClaw’s multi-agent architecture fixed this by giving each agent its own workspace and the ability to coordinate without my constant oversight.

The Problem with Single-Agent AI

Traditional AI setups require you to:

Feed prompts one at a time
Monitor output for errors
Restart when the agent loses context
Manually switch between different task types

This works for simple tasks. But when you want an agent to handle market research, write a PRD, develop code, run tests, and deploy—you become the bottleneck. The agent’s context gets polluted with too many different responsibilities, and quality drops.

The Solution: Multi-Agent Architecture

AutoClaw provides a multi-agent system where each SubAgent has:

Independent context: No pollution from other agents’ work
Separate workspace: Isolated file access and state
Message-based coordination: Agents communicate without sharing context
A Chief Agent: Orchestrates workflow and monitors progress

I configured six SubAgents for a complete development pipeline:

Development Director: Code implementation
Product Director: PRD creation and design
Testing Director: Test planning and execution
Content Director: Documentation and articles
Marketing Director: Launch materials
Full-Stack Director: End-to-end features

Configuration Examples

Here’s how I configured the SubAgents:

subagents:
  - name: "Development Director"
    role: "Code development and implementation"
    workspace: "./workspaces/dev_director/"
    skills:
      - "tmux_control"
      - "claude_code_integration"
    model: "glm-5-turbo"

  - name: "Product Director"
    role: "PRD creation and product design"
    workspace: "./workspaces/product_director/"
    skills:
      - "document_generation"
      - "web_research"
    model: "glm-5-turbo"

  - name: "Testing Director"
    role: "Test planning and quality assurance"
    workspace: "./workspaces/testing_director/"
    skills:
      - "test_generation"
      - "coverage_analysis"
    model: "glm-5-turbo"

Each agent gets its own workspace directory. This prevents file conflicts and keeps each agent’s context clean.

For notifications and inter-agent communication, I set up Feishu bot permissions:

{
  "scopes": {
    "tenant": [
      "im:message:send_as_bot",
      "im:message.p2p_msg:readonly",
      "aily:file:read",
      "aily:file:write"
    ]
  }
}

The Chief Agent (总管) monitors all SubAgents and handles:

Task distribution based on agent capabilities
Progress tracking across the pipeline
Escalation when agents need human input
Final output aggregation

Why GLM-5-Turbo Works for Long Chains

I tested several models for multi-agent workflows. GLM-5-Turbo handles long-chain tasks effectively because:

It maintains context over extended conversations
It follows multi-step instructions without dropping tasks
It recognizes when to delegate to other agents
It provides structured output for inter-agent messaging

In a real-world test, I triggered a complete personal website development with a single prompt. The agents handled research, planning, development, and testing autonomously. I only intervened for deployment credentials.

Common Mistakes to Avoid

Treating multi-agent like single-agent: If you supervise each agent constantly, you lose the autonomy benefit. Configure proper skills and permissions, then let them run.

Not setting workspace boundaries: Without isolated workspaces, agents can overwrite each other’s files or get confused by unrelated context.

Overloading one agent: Each SubAgent should focus on one domain. Don’t make your Development Director also handle marketing copy.

How Agents Coordinate

The coordination flow works like this:

Chief Agent receives your prompt
Chief identifies which SubAgents to involve
Chief sends initial task messages to relevant agents
Each SubAgent works in its workspace with its context
SubAgents send completion messages back to Chief
Chief routes outputs to next agent in pipeline
Process continues until completion

You receive notifications at key milestones and can intervene at any point.

In this post, I showed how to configure AutoClaw’s multi-agent system for autonomous software development. Each SubAgent operates with independent context and workspace, coordinating through messages while you focus on high-level decisions rather than task-by-task supervision.

Final Words + More Resources

My intention with this article was to help others share my knowledge and experience. If you want to contact me, you can contact by email: Email me

Here are also the most important links from this article along with some further resources that will help you in this scope:

Oh, and if you found these resources useful, don’t forget to support me by starring the repo on GitHub!