Multi-Agent Orchestration: When One Agent Isn't Enough

One agent can do a lot. But one agent trying to do everything — code, research, write, deploy, monitor — burns through context and makes mistakes. The solution isn’t a smarter agent. It’s more agents, coordinated well.

The CEO Agent Pattern

Think of your main agent as a CEO. It doesn’t write code. It doesn’t scrape websites. It doesn’t draft emails. It delegates to specialists and verifies the results.

graph TD
    CEO["🤖 CEO Agent<br/>(Opus)<br/>Strategist"]
    CODE["💻 Coding Agent<br/>(Haiku)<br/>Executes tasks"]
    RES["🔍 Research Agent<br/>(Haiku)<br/>Gathers info"]
    WRITE["✍️ Writer Agent<br/>(Sonnet)<br/>Creates content"]

    CEO -->|"Delegate:<br/>Code this feature"| CODE
    CEO -->|"Delegate:<br/>Research alternatives"| RES
    CEO -->|"Delegate:<br/>Draft proposal"| WRITE

    CODE -->|"Result: Code written"| CEO
    RES -->|"Result: Data gathered"| CEO
    WRITE -->|"Result: Draft ready"| CEO

    CEO -->|"Verify &<br/>synthesize"| Output["📋 Final output<br/>to user"]

    style CEO fill:#ff6b6b,stroke:#c92a2a,color:#fff
    style CODE fill:#4ecdc4,stroke:#0b8a8d,color:#fff
    style RES fill:#4ecdc4,stroke:#0b8a8d,color:#fff
    style WRITE fill:#95a8d1,stroke:#5a6e8c,color:#fff
    style Output fill:#90ee90,stroke:#228b22,color:#000

The CEO agent runs on Opus (or your best model) because it makes strategic decisions: what to delegate, when to iterate, and whether the result is good enough. Workers run on cheaper, faster models because they’re executing well-defined tasks.

When to Delegate vs Handle Directly

This is the critical decision. Delegate too aggressively and you waste time on spawn overhead. Delegate too little and your CEO agent drowns in details.

The decision tree:

Is this < 30 seconds?
  └─ YES → Do it yourself (file read, quick lookup)
  └─ NO ↓

Is this > 5 minutes OR > 3 steps?
  └─ YES → Delegate to specialist
  └─ NO ↓

Can parts run in parallel?
  └─ YES → Spawn multiple specialists
  └─ NO → Single specialist

Handle Directly

Quick file reads and lookups
Verifying sub-agent results
Making strategic decisions (iterate vs ship)
Task decomposition
Result synthesis

Delegate Always

Software builds and test suites
Web research and data gathering
Content drafting (articles, emails)
Deployment pipelines
Monitoring and alerting

Model Selection for Workers

Not every task needs the same brain. Match the model to the work:

Task	Model	Timeout	Why
Code builds/tests	Haiku	15 min	Fast iteration, well-defined scope
Web research	Haiku	5 min	Pattern matching, extraction
Content writing	Sonnet	3 min	Needs voice and nuance
Strategic analysis	Opus	10 min	Complex reasoning required
Data transformation	Haiku	5 min	Mechanical, well-structured

The Verification Loop

This is where most orchestration systems fail. They delegate, receive output, and forward it to the user. That’s not orchestration — that’s a relay.

Real orchestration includes verification:

CEO spawns coding agent
  → Agent returns result
  → CEO runs tests
  → Tests fail?
    → CEO sends feedback to same agent
    → Agent fixes
    → CEO runs tests again
  → Tests pass?
    → CEO delivers to user

The user never sees broken code. The CEO is the quality gate. It runs tests, checks formatting, validates links, and verifies deployments before reporting success.

In OpenClaw, this looks like:

# CEO spawns a coding sub-agent
sessions_spawn coding-agent \
  --task "Build the contact form with validation" \
  --timeout 15m

# Sub-agent completes → CEO verifies
npm run test
npm run build

# If broken → iterate
sessions_steer coding-agent \
  --message "Tests failing on email validation. Fix the regex."

# If passing → deploy and report

Parallel Execution

The real power of multi-agent orchestration is parallelism. When tasks are independent, spawn them simultaneously:

CEO receives: "Research competitors and build a landing page"

  ┌─ Spawn researcher: "Find top 5 AI agent competitors"
  │
  └─ Spawn coder: "Build landing page from template"

Both run simultaneously. CEO waits for both.
Researcher finishes → CEO holds results
Coder finishes → CEO integrates competitor data
                → Verifies build
                → Deploys

This cuts wall-clock time dramatically. A 20-minute sequential task becomes 10 minutes parallel.

Common Pitfalls

1. Over-Delegation

Spawning a sub-agent to read a file is wasteful. The spawn overhead (context loading, model invocation) exceeds the task cost. Use the 30-second rule.

2. No Timeout Discipline

Without timeouts, a stuck agent burns tokens indefinitely. Set explicit timeouts on every spawn. Kill and retry if needed.

3. Passing Too Much Context

Don’t dump your entire memory into sub-agent prompts. Give them only what they need. A coding agent doesn’t need your calendar. A researcher doesn’t need your codebase.

4. Skipping Verification

“The sub-agent said it worked” is not verification. Run the tests. Check the output. Validate the links. You are the quality gate.

5. Too Many Concurrent Agents

More isn’t always better. Each active agent consumes API resources. Cap concurrent agents at 5 per session. Queue the rest.

The Result

With proper orchestration, a single CEO agent can coordinate:

Coding agents building features
Research agents gathering data
Writer agents drafting content
Monitor agents watching deployments

All while maintaining strategic context and ensuring quality. The CEO thinks in outcomes. The workers think in tasks.

About the author: JD Davenport builds AI agent systems at OpenClaw. Follow on LinkedIn for updates on building AI agents for business.