How to Coordinate Multiple AI Agents and Subagents in Make

A practical field guide from Automation Ace.

Short answer: coordinate multiple AI agents in Make by giving each agent one job and connecting them with scenarios. Use a pipeline for fixed stages, a router to send each request to the right specialist, or an orchestrator agent whose tools are scenarios that run specialist “subagents.” Define a clear input and output contract for each agent, pass only the context each needs, cap steps and depth, and log every hand-off.

Make's agent features evolve quickly, and dedicated multi-agent features may change; the patterns below use building blocks documented at the time of writing, such as scenarios as agent tools and the Run an agent module.

Short answer

• One job per agent.
• Connect agents with scenarios: pipelines, routers, or orchestrators.
• Use JSON contracts between agents.
• Limit steps, depth, and context; log hand-offs.

Why use multiple agents

• Focus: a narrow agent with a short prompt and a few tools is more reliable than one agent that does everything.
• Different tools and permissions: a research agent can be read-only while a ticketing agent can write.
• Reuse: one specialist can serve many workflows.
• Review: a checker agent can catch mistakes before they reach people.

Coordination patterns

Building subagents with scenarios as tools

1. Build each specialist agent with its own prompt and tools; see how to build AI agents in Make.
2. Wrap each specialist in a scenario that takes structured input, runs the agent with Run an agent, validates its output, and returns JSON.
3. Add those scenarios as tools to the orchestrator agent, with clear names and descriptions.
4. Tell the orchestrator when to use each subagent and how to combine results.

Define contracts between agents

Subagent contract (scenario tool wrapping a specialist agent)

Name:        research_company
Description: Researches a company and returns a short profile. Use when
             you need company size, industry, or recent news.
Input:       { "company_name": string, "website": string (optional) }
Output:      { "summary": string, "industry": string,
               "employee_range": string, "sources": [string],
               "confidence": "high" | "medium" | "low" }
Limits:      Read-only. Max 8 steps. No outreach.
On error:    Return { "error": "...", "confidence": "low" }

Contracts make agents swappable and testable, and they make failures obvious. Validate every subagent output before the orchestrator uses it.

Passing context and state

• Pass only the fields each agent needs, not the whole history.
• Store shared state (case ID, decisions so far) in a data store or table, not in prompts.
• Use thread IDs only where an agent genuinely needs conversation history.
• Record each hand-off: which agent, input reference, output, and time.

See data stores vs Airtable vs Sheets.

Controlling cost and loops

• Set tight max steps on every agent.
• Limit depth: an orchestrator calls subagents; subagents do not call other orchestrators.
• Prevent agents calling each other in circles.
• Use cheaper models for narrow specialists.
• Track credits per case, not just per agent; see monitoring costs.

Testing and monitoring multi-agent systems

• Test each subagent alone against its contract first.
• Then test the whole system on realistic cases, including ones that should be escalated.
• Log hand-offs and review full traces when something goes wrong.
• Give each agent only the permissions its job needs; see agent access and permissions.

When not to use multiple agents

If a single agent with a few tools works, or a scenario with an AI module works, do not add more agents. Multi-agent systems cost more and are harder to debug. Start simple; see Make AI agents vs scenarios and from process to agent.

Frequently asked questions

How do I coordinate multiple AI agents in Make?

Give each agent one job and connect them with scenarios using a pipeline, a router that sends requests to the right specialist, or an orchestrator agent whose tools are scenarios that run specialist subagents. Define input and output contracts, pass minimal context, cap steps, and log hand-offs.

How do I build subagents in Make?

Build each specialist as its own agent, wrap it in a scenario that takes structured input, runs it with the Run an agent module, validates the output, and returns JSON, then add those scenarios as tools to an orchestrator agent.

How should agents share information in Make?

Pass only the fields each agent needs, store shared state such as case IDs and decisions in a data store or table, use thread IDs only when history is needed, and log every hand-off.

How do I stop multi-agent systems in Make from looping or overspending?

Set tight max steps, limit depth so subagents do not call orchestrators, prevent circular calls, use cheaper models for narrow specialists, and track credits per case.

When should I avoid using multiple agents?

When a single agent with a few tools, or a scenario with an AI module, can do the job. Multi-agent systems cost more and are harder to debug, so start simple.