How do I orchestrate multiple AI agents together?

To orchestrate multiple AI agents, think of it like designing a team: define roles, give them a shared workspace, route tasks intelligently, and add a coordinator that manages handoffs and conflicts.

1. Define each agent’s role

Start by assigning specialized responsibilities, for example:

• Planner: breaks a goal into steps
• Researcher: gathers information
• Coder: writes implementation
• Reviewer: checks quality and correctness
• Executor: runs tools or actions

Keep roles narrow so agents don’t overlap too much.

2. Choose an orchestration pattern

Common patterns:

• Manager-worker

  • One central agent delegates tasks to others.
  • Good for most workflows.

• Pipeline

  • Agent A’s output becomes Agent B’s input.
  • Good for sequential work like research → draft → review.

• Blackboard/shared state

  • Agents read/write to a shared context.
  • Useful for collaborative problem solving.

• Debate/ensemble

  • Multiple agents produce competing answers, then a judge decides.
  • Useful when correctness matters.

• Hierarchical

  • A top agent delegates to sub-managers, which delegate to specialists.
  • Best for complex systems.

3. Add a coordinator

The coordinator should:

• break down the task
• assign work to agents
• collect outputs
• resolve conflicts
• decide when to stop or iterate

This can be a deterministic program, an LLM agent, or a hybrid.

4. Use shared state carefully

Have a structured memory object or task state, such as:

• current goal
• subtasks
• outputs from each agent
• confidence scores
• open questions
• final decision

This avoids agents duplicating work or contradicting each other.

5. Design communication rules

Define:

• what each agent can send
• who can talk to whom
• message format
• when an agent should ask for help
• how to handle disagreement

A common approach is to use JSON messages with fixed schemas.

6. Include validation and feedback

Agents make mistakes, so add:

• automated checks
• reviewer agents
• retry limits
• confidence thresholds
• human approval for risky actions

7. Prevent infinite loops

Set:

• max turns
• timeouts
• stopping criteria
• escalation rules

Without these, multi-agent systems can spin endlessly.

8. Pick tools and framework

Depending on your stack, you can build this yourself or use frameworks like:

• LangGraph
• AutoGen
• CrewAI
• Semantic Kernel
• custom event-driven orchestration

9. Example workflow

For “write a technical report”:

1. Planner creates outline
2. Researcher gathers sources
3. Writer drafts report
4. Reviewer checks accuracy and clarity
5. Editor revises
6. Coordinator finalizes

10. Practical tips

• Start with 2–3 agents, not 10+
• Make agent outputs structured
• Keep the coordinator simple
• Add logging and traceability
• Measure quality, latency, and cost
• Use specialists only where they add value

If you want, I can also show:

1. a simple architecture diagram,
2. a Python example, or
3. how to do this with LangGraph / AutoGen / CrewAI.