How to Build an AI Agent Team: OpenClaw's Multi-Agent Architecture

The Dream: Fully Autonomous AI Teams

Everyone building with AI eventually hits the same wall: one assistant isn't enough. You want a research agent, a content writer, a customer service bot, a developer — all working together. But most frameworks give you a single chatbot with a system prompt and call it a day.

OpenClaw takes a fundamentally different approach. It's built from the ground up for multi-agent orchestration — where each agent is a fully isolated "brain" with its own workspace, memory, personality, tools, and even its own channel connections. And critically, they can coordinate.

This article is a practical deep dive based on our real-world experience running a multi-agent system at ThinkSmart.Life, where we use OpenClaw to power daily news feeds, research articles, fintech coverage, and more — all with specialized agents.

Core Concept: What Is an "Agent" in OpenClaw?

An agent in OpenClaw is not just a prompt template. It's a fully scoped entity with:

• Its own workspace — a directory with files like SOUL.md (personality), AGENTS.md (operating instructions), USER.md (user context), IDENTITY.md (name, vibe, emoji), and TOOLS.md (local tool notes)
• Its own memory — daily logs in memory/YYYY-MM-DD.md and long-term memory in MEMORY.md
• Its own session store — chat history and routing state, completely isolated from other agents
• Its own authentication — separate auth profiles, API keys, and credentials
• Its own tool access — configurable allow/deny lists for which tools each agent can use
• Its own model — you can run one agent on a fast model (Sonnet) and another on a powerful model (Opus)

The Architecture: Main Agent + Sub-Agents

OpenClaw uses a two-tier architecture:

Tier 1: Named Agents (Persistent)

Named agents are the permanent team members. They live in the gateway config, each with their own workspace, identity, and channel bindings. They persist across restarts and maintain long-running memory.

For example, at ThinkSmart.Life, the main agent (Yaneth) handles direct conversations, orchestrates tasks, and manages the workspace. But you could also configure:

• A "Work" agent bound to your Slack workspace for professional tasks
• A "Family" agent bound to a WhatsApp group with restricted tools
• A "Research" agent running on Opus for deep analysis via Telegram

// openclaw.json — two agents, different models, different channels
{
  agents: {
    list: [
      {
        id: "chat",
        name: "Everyday",
        workspace: "~/.openclaw/workspace-chat",
        model: "anthropic/claude-sonnet-4-5"
      },
      {
        id: "deepwork",
        name: "Deep Work",
        workspace: "~/.openclaw/workspace-deepwork",
        model: "anthropic/claude-opus-4-6"
      }
    ]
  },
  bindings: [
    { agentId: "chat", match: { channel: "whatsapp" } },
    { agentId: "deepwork", match: { channel: "telegram" } }
  ]
}

Tier 2: Sub-Agents (Ephemeral)

Sub-agents are spawned on-demand by a main agent using sessions_spawn. They're isolated sessions that run a specific task, then report back. Think of them as contractors — you hire them for a job, they do it, and they leave.

Sub-agents get:

• The full tool set minus session tools (they can't spawn more sub-agents — no runaway chains)
• Their own isolated session with no access to the main agent's conversation history
• A specific task prompt that defines their entire mission
• Optional model override (use a cheaper model for simple tasks)

When they finish, OpenClaw automatically announces the result back to the requester's chat channel.

// How the main agent spawns a sub-agent
sessions_spawn({
  task: "Research the latest developments in quantum computing and write a 500-word summary",
  label: "quantum-research",
  model: "anthropic/claude-sonnet-4-5",  // cheaper model for research
  runTimeoutSeconds: 300  // 5 minute timeout
})

Can Sub-Agents Have Different Identities?

Yes — and this is where it gets powerful.

Named agents (Tier 1) absolutely have different identities. Each has its own SOUL.md that defines personality, tone, and behavior. One agent can be formal and analytical, another can be casual and creative. They can have different names, different vibes, different emoji — they're different "people."

Sub-agents (Tier 2) inherit the spawning agent's workspace context, but you control their behavior entirely through the task prompt. In practice, you can make a sub-agent behave completely differently by describing the role in the task:

sessions_spawn({
  task: `You are a senior financial analyst. Research and write a report on...`,
  label: "finance-analyst"
})

sessions_spawn({
  task: `You are a creative copywriter. Write engaging social media posts for...`,
  label: "copywriter"
})

Can Agents Be "Trained" Separately?

This is an important distinction. OpenClaw agents don't undergo traditional ML training. Instead, they use context engineering — their behavior is shaped by:

So while you're not fine-tuning weights, each agent develops its own "expertise" through accumulated context. An agent that's been running for weeks has daily memory files, lessons learned, and workflow patterns that make it increasingly effective at its specific role. That's as close to "training" as you get without actual model fine-tuning — and it's remarkably effective.

The Orchestrator Pattern: Your Main Agent as Team Lead

In practice, the most effective architecture is having one orchestrator agent that acts as team lead:

• Receives requests from the human (via Telegram, WhatsApp, etc.)
• Breaks down complex tasks into sub-agent assignments
• Spawns sub-agents in parallel for independent work streams
• Monitors progress via session listing and history
• Aggregates results and reports back to the human
• Creates blocker tasks on project boards when human input is needed

Here's what this looks like in practice at ThinkSmart.Life:

// Michel asks: "Add a new research post and create an RSS feed"
// Yaneth (orchestrator) spawns a sub-agent:

sessions_spawn({
  task: `
    1. Fetch and analyze the article at [URL]
    2. Write an original blog post at /research/posts/
    3. Generate audio narration via TTS
    4. Create RSS feed with all audio posts
    5. Update the research hub page
    6. Link from the main page
    7. Git commit and push
  `,
  runTimeoutSeconds: 300
})

// Sub-agent does all the work independently
// Result is announced back to Telegram when done
// Michel sees: "Done! Commit abc123 — View on GitHub"

Scaling: From One Agent to an AI Business

Here's the practical scaling path we've found effective:

Stage 1: One Agent, Many Hats

Start with a single main agent that handles everything. Use sub-agents for heavy tasks. This is where most people should begin — it's simpler and you learn the patterns.

Stage 2: Specialized Agents by Channel

Split by communication channel. WhatsApp gets a casual everyday agent, Telegram gets a deep-work agent. Same human, different contexts.

Stage 3: Role-Based Agent Team

Dedicated agents for distinct roles: content creation, customer service, research, DevOps. Each with optimized prompts, tools, and models for their function.

Stage 4: Multi-Tenant Agent Platform

This is the endgame — a platform where users create their own agents. Each user gets an isolated agent with their own workspace, prompt, and output channel. This is exactly what we're building with our Self-Service News Anchors feature.

Security: Isolation by Default

OpenClaw's multi-agent security model follows the principle of least privilege:

• Workspace isolation — each agent's workspace is separate; no cross-access by default
• Auth isolation — credentials are per-agent; main agent keys are not shared
• Session isolation — agents can't read each other's chat history unless explicitly enabled
• Tool restrictions — per-agent allow/deny lists control what each agent can do
• Sandboxing — agents can run in Docker containers with filesystem isolation
• Sub-agent limits — sub-agents can't spawn more sub-agents (no recursive chains)
• No self-modification — agents can't change system prompts or safety rules of other agents

// Example: Restricted family agent
{
  id: "family",
  workspace: "~/.openclaw/workspace-family",
  sandbox: { mode: "all", scope: "agent" },
  tools: {
    allow: ["read", "exec", "sessions_list"],
    deny: ["write", "edit", "browser", "cron"]
  }
}

Inter-Agent Communication

When agents need to talk to each other, OpenClaw provides controlled channels:

• sessions_send — send a message into another agent's session (with optional ping-pong conversation)
• sessions_list + sessions_history — discover and read other sessions (visibility configurable)
• Agent-to-agent messaging — must be explicitly enabled in config with allowlists

This is deliberately restrictive. Unrestricted inter-agent communication leads to chaos — agents talking in loops, amplifying errors, or escalating permissions. OpenClaw makes you opt-in to every communication channel.

Getting Started: Your First Multi-Agent Setup

Ready to build your own agent team? Here's the quickest path:

1. Install OpenClaw — npm install -g openclaw (docs)
2. Set up your main agent — openclaw setup creates workspace files
3. Edit SOUL.md — give your agent a personality and role
4. Connect a channel — WhatsApp, Telegram, Discord, or Signal
5. Start using sub-agents — ask your agent to spawn tasks and observe the pattern
6. Add a second agent — openclaw agents add work and configure bindings
7. Iterate — refine prompts, add skills, build memory over time

The key insight: agents are cheap to create but powerful in isolation. You don't need to design the perfect system upfront. Start with one agent, learn its patterns, then scale horizontally as you understand what works.

Conclusion

The AI agent space is moving fast, but the architecture for multi-agent systems is settling into clear patterns: isolated workspaces, role-based identities, orchestrated sub-agents, and controlled communication. OpenClaw implements these patterns in a production-ready framework that runs on your own hardware.

Whether you're building a personal productivity system or a revenue-generating AI business, the path is the same: start with one agent, make it excellent at its job, then scale. The agents don't need to be perfect — they need to be useful. And with the right architecture, even imperfect agents become incredibly powerful when they work as a team.

This article was written collaboratively by Michel (human) and Yaneth (AI agent) — a real-world example of the human-agent partnership we've described. The audio briefing was generated by Yaneth using OpenAI TTS.