A multi-agent system divides a goal across multiple agents, each with a role, and coordinates their work into one outcome.
One agent may research, another may write code, another may analyze data, and another may review the result. A manager or orchestration layer decides what happens next and what information is passed between them.
Product Manager lens: think in terms of ownership, handoffs, dependencies, approvals, and measurable output quality—not simply “more AI.”
Architecture: a manager and specialist agents
A common design gives one manager agent responsibility for the overall objective while specialist agents own parts of the work.
Manager AgentBreaks down the goal · delegates · checks progress · assembles the result
Research AgentFinds and summarizes evidence
Coding AgentBuilds or changes software
Data AgentQueries and analyzes data
Review AgentChecks quality, risk, and requirements
Single Agent vs Multi-Agent System
Single Agent
One decision-maker plans, uses tools, and completes the task. This is often simpler, faster, and easier to debug.
Multi-Agent System
Several role-specific decision-makers collaborate. This can improve separation of concerns, parallel work, and review—but adds coordination work.
Rule of thumb: start with one capable agent. Add roles only when a clear boundary, parallel path, expertise gap, or independent review justifies the complexity.
Key concepts
Agent Role
The responsibility and scope assigned to an agent.
Agent Handoff
Passing a task, result, or responsibility to another agent.
Agent Communication
Messages, artifacts, events, or state exchanged between agents.
Agent Coordination
Managing dependencies, ordering, conflicts, and shared progress.
Agent Orchestration
The control logic that routes work and decides what runs next.
Shared Memory
Common state or knowledge that agents can read and update.
Task Delegation
Assigning a well-defined subtask to the agent best suited for it.
Example: making an industry report
The manager owns the report outcome. Specialists own the evidence and quality of each step.
01 · MANAGERDefines the question, scope, format, and deadline.
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02 · RESEARCHSearches sources and returns cited findings.
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03 · DATAAnalyzes market data and highlights patterns.
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04 · WRITERTurns evidence into a clear report draft.
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05 · REVIEWChecks accuracy, gaps, and unsupported claims.
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06 · MANAGERResolves feedback and delivers the final report.
Important: the writing step is a Writer Agent. The same principle applies to a Coding Agent or another specialist: give each role a clear input, output, and acceptance check.
More agents create more system cost
Multi-agent is not automatically better. Each new agent introduces another boundary where context can be lost and work can fail.
CostMore model calls and infrastructure
LatencyMore steps before a result
ContextMore state to pass and maintain
CoordinationMore routing and synchronization
Error propagationOne bad handoff can affect later work
When is one agent enough?
Use one agent when...
the task follows one coherent loop
the same context is needed throughout
latency and operating cost matter
one review step is enough
Consider multiple agents when...
work has independent parallel branches
roles need different tools or permissions
specialized expertise improves quality
an independent reviewer reduces risk
Remember this
Single Agent = one person completing the whole job. Multi-Agent System = a project team with different responsibilities. The best product decision is the simplest architecture that reliably achieves the outcome.