Multi-Agent Orchestration
When work spans departments, a team of agents finishes it end to end. A supervisor agent breaks a goal into steps and hands each to a specialist — sales, support, finance or a task agent — coordinating the handoffs to a result, with a person in the loop and every hop governed.
Overview
The Flow-Cognition Agent handles a single task end to end. But real work spans functions — a refund touches support, finance and comms. That is where several agents work as a coordinated team.
A supervisor agent plans and delegates; specialist agents each own their domain; they hand off with shared context instead of a cold start; and every step is permission-checked, approvable and logged. You compose the team in AI Studio from your agents, prompts and catalog nodes.
Explore AI Intelligence Layer →- ✓ Supervisor agent that plans & delegates
- ✓ Specialist agents per department or task
- ✓ Agent-to-agent handoff with context
- ✓ Shared memory across the team
- ✓ Human-in-the-loop approval gates
- ✓ Every agent governed and audited
How multi-agent teams work
Supervisor agent
A lead agent breaks the goal into steps and decides which specialist handles each.
Specialist agents
Each agent owns a domain — a sales agent, a support agent, a finance agent, a task agent.
Agent-to-agent handoff
Agents pass work to each other with the full context, not a fresh start.
Shared memory
A shared, governed context so the team works from one source of truth.
Human-in-the-loop
Approval gates where a person signs off before a high-stakes action.
Governed & audited
Every agent runs under field-level permissions with a full audit trail.
Single agent vs agent team
| Single agent | Agent team | |
|---|---|---|
| Scope | One task, end to end | A goal that spans functions |
| Coordination | — | A supervisor plans and delegates |
| Context | One agent’s memory | Shared across the team, with handoff |
| Best for | Score a lead, draft a reply | Resolve a refund, run an onboarding, research |
Live in four steps
Define the goal
Describe the outcome you want.
Supervisor plans
The lead agent breaks it into steps.
Specialists execute
Each agent does its part with context.
Handoff & finish
Agents hand off and complete, governed.
A worked example: a refund request
The agents divide the work and hand off with full context — grounded in your records, governed and logged.
Supervisor checks the order
The supervisor agent reads the order and routes the refund to the right specialists.
Finance calculates, a person approves
The finance agent calculates the refund, and a person approves it before it is issued.
Comms confirms
The comms agent confirms the refund to the customer — every hop logged and governed.
Five-tier model routing · field-level permissions · full audit trail. See the AI layer →
Ways agents coordinate
Compose these patterns to fit the work — from a simple pipeline to nested teams.
Supervisor
A lead agent breaks the goal into steps and delegates each to the right specialist.
Sequential
Agents run as a pipeline — each passes its output to the next.
Parallel
Several agents work at once; their results are merged.
Hierarchical
Teams of agents, each with its own supervisor, nested into bigger goals.
Loop / iterative
An agent repeats a step, refining until the result is good enough.
Human-in-the-loop
A person approves or steers the team at the key moments.
Where teams put it to work
Cross-functional workflows
A refund or onboarding that spans support, finance and comms, run by a team of agents.
Customer onboarding
A supervisor sets up the account, a finance agent raises the first invoice and a comms agent sends the welcome sequence, with a person approving go-live.
Complex research
A supervisor splits a research task across agents and assembles the answer.
Questions, answered
Several AI agents working as a team — a supervisor plans and delegates, specialist agents each own a domain, and they hand off to each other.
A lead agent that breaks a goal into steps, decides which specialist handles each, and assembles the result.
Agents pass work with the full shared context, so the next agent continues rather than starting over.
Yes. Every agent runs under field-level permissions, with human-in-the-loop gates and a full audit trail.
Compose the team in AI Studio from your agents, prompts and the Knowledge & Agent nodes in the catalog.
Put a team of agents on the work
Start free, or get a guided walkthrough with our team — on the one platform that runs the AI Intelligence Layer and your whole business.