Use separate agents when tasks have distinct context, tools, permissions, or evaluation criteria; keep one agent when boundaries are weak. Multi-agent systems coordinate specialized agents through explicit handoffs, shared state, or routing, so complex work is divided rather than forced through one general agent.
The choice shapes architecture, operating cost, and failure handling. In practice, coordination earns its complexity only when specialization creates a clear boundary.
In this guide you will learn:
- What multi-agent systems are, including roles, objectives, and coordination.
- When to use one agent or several for a workflow.
- How AutoAgents separates work by responsibility.
- The four contracts every specialized agent needs.
- Which orchestration topology fits different workflows.
- How to verify a multi-agent system in production.
Key insights
- The test for splitting a workflow is whether one stage can fail on its own and be retried without restarting everything.
- A coordinator needs explicit state and handoff contracts before it needs more agents.
- AutoAgents separates drafting from execution, then assigns execution work to specialized agents.
- Sequential, parallel, supervisor, and graph-based topologies create different failure and latency profiles.
- Production verification must trace handoffs alongside reliability, cost, and latency.
What are multi-agent systems?
A multi-agent system divides a workflow among specialized agents that communicate through defined interactions. Each agent owns a bounded responsibility, while coordination logic controls how work moves between them.
That structure resembles a small engineering team. A planner decides what needs to happen, a specialist performs a task, and an observer checks whether the work can continue.
Separate roles, shared objectives, and coordination
Specialization starts with a role that has a real job. "Research agent" is weak until you define what it receives, what it may do, and what evidence it must return.
A role needs three boundaries:
- The context it can read.
- The actions it can take.
- The conditions that define acceptable work.
The shared objective sits above those boundaries. Each agent can optimize its own step while the coordinator protects the workflow's broader outcome.
A research agent can gather evidence without deciding the final answer, while a verification agent can challenge that evidence without rewriting the task. The separation makes disagreements visible.
Coordination then becomes a protocol. The coordinator passes state, selects the next role, records the handoff, and decides whether the workflow continues.
How multi-agent systems differ from a single agent with tools
A single agent with tools still has one reasoning loop, one active context, and one responsibility for deciding what happens next. More tools increase its reach, but they do not automatically create ownership boundaries.
AutoGen's paper describes programmable agents with customizable tools, human inputs, and interaction behaviors. That flexibility supports either a single tool-using agent or a larger conversation among specialized agents.
Dimension
One agent with tools
Several specialized agents
Context
One broad working context
Separate, bounded contexts
Tool access
Shared access unless restricted
Permissions assigned by role
Evaluation
One reasoning loop judges progress
Each stage can have its own checks
Failure handling
One retry and recovery policy
Handoffs can stop, retry, or escalate
Coordination
Internal tool selection
Explicit routing and state transfer
A single agent is easier to debug because fewer moving parts can fail. Several agents provide stronger boundaries, but every boundary adds serialization, routing, and observability work.
Multi-agent systems are coordination architectures, not collections of prompts.
When should you use one agent or several?
Start with one agent when the task has one context, one decision loop, and one clear evaluation rule. Split the workflow when those assumptions stop holding.
The question is architectural: does specialization reduce ambiguity enough to justify the coordination it introduces?
Task boundaries and independent evaluation
A separate agent makes sense when you can state its input, output, tools, and acceptance criteria without borrowing the entire workflow context.
Independent evaluation is an even stronger signal. If one stage can fail while another remains valid, separate ownership lets you retry or replace that stage without restarting everything.
According to AutoAgents' April 29, 2024 revision, its specialized setup scored 82.0% versus 74.6% for standard prompting. The result came from the five-trivia-question setting.
That comparison supports specialization where a task benefits from separate roles. It does not justify splitting every workflow into agents.
Use one agent when:
- The task has a short path from input to answer.
- The same context is needed throughout the work.
- One evaluator can judge the complete result.
- Tool permissions do not need separation.
Use several agents when:
- Different stages require different context.
- Tool access must follow distinct permissions.
- Independent reviewers can reject faulty work.
- Parallel investigation can shorten the critical path.
Signals that coordination overhead is not worth it
Coordination introduces its own workload. State has to be serialized and messages interpreted, while retries need bounds and someone has to decide when the workflow is complete.
Keep one agent when the proposed split creates more protocol than work. Watch for these signals:
- Agents need to exchange nearly the entire context after every step.
- The coordinator makes more decisions than the specialists.
- Failure in one stage invalidates every downstream result.
- The workflow has too little volume to justify tracing and maintenance.
Teams often split by job title when the real seam is the failure boundary. "Researcher," "writer," and "reviewer" sound distinct, but they may still need the same context and evaluation criteria.
When the boundaries are artificial, the coordinator becomes a message-passing wrapper around one agent. That adds latency without adding control.
Use several agents only when the workflow can preserve useful work across a handoff.
How AutoAgents separates work by responsibility
AutoAgents provides a practical reference architecture for separating planning, observation, and execution. Its paper describes a two-stage Drafting/Execution design with dedicated roles around each stage.
The design matters because planning and acting create different failure modes. A plan can be incomplete, while an action can be unauthorized or incorrectly executed.
Drafting with planners and observers
The Drafting stage creates a plan before execution begins. The Planner turns the task into steps, dependencies, and intended outcomes.
The Agent Observer watches the planner's work and helps maintain the agent profile and role behavior. The Plan Observer checks whether the proposed plan is coherent and ready for execution.
AutoAgents' architecture description assigns these roles separately. The observers exist to provide control points around planning.
That distinction gives you a place to reject a weak plan before it reaches tools. It also lets you inspect whether the planner misunderstood the objective or simply lacks information.
A drafting record should include the task interpretation, planned steps, required evidence, and unresolved questions. The execution stage should receive that record instead of reconstructing the plan from raw conversation history.
Execution with specialists and an action observer
Execution assigns individual steps to specialists with narrower responsibilities. One specialist retrieves documents, another checks the numbers, and a third tests the result before it leaves the system.
The Action Observer tracks tool calls and execution state. It can identify failed actions, missing evidence, or a step that produced an output outside its contract.
This pattern avoids giving every agent the same broad authority. It also creates a clean place for approval when an action has external side effects.
The architecture resembles a production workflow more than a chat session. Each specialist receives a task, acts within its permissions, and returns structured evidence for the next decision.
Separating planning from execution gives you an inspection point before external actions begin.
The 4 contracts every specialized agent needs
Specialization fails when agents share responsibilities but lack explicit operating rules. Four contracts carry those rules: input, output, tools and permissions, and termination.
AutoAgents' role contract includes a profile, goal, constraints, description, toolset, and suggestions. Those fields are a workable starting point for your own contracts.
Inputs and outputs
The input contract defines what the agent receives and what context it should ignore. The output contract defines what the next stage can trust.
Write both contracts in terms your coordinator can validate:
- State the accepted input shape and required fields.
- Define the output schema, including evidence and confidence.
- Separate completed work from unresolved questions.
- Specify whether the agent may return partial progress.
- Include identifiers that connect the output to its source task.
A free-form paragraph creates interpretation work for the next agent. Structured output turns a handoff into data.
Context engineering happens at this boundary. Pass the smallest context that supports the task, then keep references to anything the next stage may need.
Tools and permissions
Tool access belongs in the role contract. An informal system prompt cannot be validated, so list allowed tools, parameter limits, data scopes, and actions requiring approval.
A read-only research agent should not inherit write access because another agent needs it. A deployment agent should not decide product policy because it can call the deployment tool.
Permissions also define identity. Record which agent initiated an action, which credentials it used, and which coordinator approved the call.
If your API surface is difficult to expose safely, AgentBridge's approach to semantic API representation shows one way to do it. Agents need operations described by intent, inputs, constraints, and side effects.
Success, failure, and termination rules
An agent needs a stopping rule. Without one, it can continue gathering context, retrying tools, or asking for clarification after the workflow has already lost its budget.
Set observable success conditions, failure categories that guide recovery, and termination limits for attempts, time, cost, and dependency depth.
A failure contract should distinguish between:
- Missing information that another agent can provide.
- Invalid input that requires correction.
- Tool failure that may support a bounded retry.
- Policy or permission failure that requires escalation.
- Output failure that requires rework.
The coordinator should receive these states explicitly. "I could not complete the task" is not enough to choose the next action.
A specialized agent becomes operationally sound when its boundaries can be validated by software.
Which orchestration topology fits the workflow?
Topology determines how work moves and where control lives. Choose it from task dependencies and failure recovery, since framework familiarity is the wrong input.
LangChain's multi-agent documentation describes routing, supervisor coordination, handoffs, and subagents composed as tools. Those patterns map to different control requirements.
Flow
Use it when
Main engineering concern
Sequential
Each stage depends on the previous output
A slow or failed stage blocks downstream work
Parallel
Tasks can run independently
The merge stage must resolve disagreement
Supervisor
One coordinator can route dynamically
The supervisor can become a bottleneck
Graph-based
Branches, loops, and recovery paths matter
State transitions require strict controls
Sequential and parallel flows
Sequential flows fit dependent work. The next stage needs the previous stage's output, so the coordinator moves one handoff at a time.
ChatDev uses sequential role specialization across design, coding, and testing. Its paper further divides coding and testing into subtasks handled through specialized instructor and assistant agents, as described in ChatDev's role design.
Parallel flows fit independent work. Multiple specialists can investigate separate sources or produce separate judgments before a merge stage reconciles them.
Parallelism does not remove coordination. It moves the problem to result merging, conflict handling, and shared resource limits.
Supervisor and selector patterns
A supervisor keeps the workflow under centralized control. It selects a specialist, supplies context, evaluates the response, and decides what happens next.
A selector is narrower. It routes a task to one specialist based on intent, data type, permission, or workflow state.
Supervisor patterns work when the route depends on intermediate findings. Selector patterns work when the route can be determined from the initial request.
Keep the supervisor's authority narrow. It should choose and validate work, then stay out of the specialists' reasoning.
Handoffs can also move control between agents. That fits workflows where the current specialist knows most about the next step, though it requires clear transfer rules.
Hybrid and graph-based workflows
Most production workflows need more than one topology. A graph can begin with a selector, run independent research in parallel, merge findings, and route failed evidence back to a specialist.
Represent each node as a contract and each edge as an allowed transition. A retry then becomes a graph decision with a recorded path.
Graph-based designs also make termination visible. You can identify cycles, cap repeated transitions, and preserve the state that caused a failure.
The cost is operational complexity. You need durable state, trace identifiers, transition logs, and a way to replay a failed path.
Choose the smallest topology that preserves the workflow's real dependencies and recovery paths.
How to verify a multi-agent system in production
A multi-agent workflow can produce convincing final answers while hiding broken handoffs underneath. Verification has to inspect the path as well as the final response.
Treat every handoff as an event with an input, decision, output, and status. That record gives your team a diagnosis when the final result looks plausible but is wrong.
Handoff evidence and traceability
Each handoff should answer five questions:
- Which agent produced this output?
- Which task and workflow does it belong to?
- What evidence supports the result?
- Which tools and data sources were used?
- Why did the coordinator select the next step?
Store the contract version alongside the handoff. A changed prompt or schema can alter behavior even when the workflow code stays the same.
Trace the original request through every delegated task. Include parent and child identifiers, timestamps, tool results, retries, and termination reasons.
Evidence from real traffic matters here. A workflow can pass controlled tests while failing under long context, partial tool outages, or unexpected routing.
Reliability, cost, and latency checks
Reliability is not automatic because a workflow has more agents. According to the October 26, 2025 revision of Why Do Multi-Agent LLM Systems Fail?, failure rates ranged from 41% to 86.7% across seven open-source MAS.
The study annotated 1,642 execution traces. One workflow change raised ChatDev's overall task success rate by 9.4%.
Those findings make trace review part of system design. Track each agent's failure rate, retry rate, handoff rejection rate, and contribution to failed runs.
Cost and latency need the same treatment. Measure total model calls, tool calls, serialized context, queue time, and the critical-path duration.
Set budgets at both agent and workflow levels. A specialist may have a small retry allowance, while the coordinator owns the total time and cost limit.
The trace is where the root cause lives. Treat it as a test artifact.
Production verification should tell you which handoff failed, why it failed, and what the next run will change.
Where to start with separate agents
Separate agents fit workflows with distinct context, permissions, tools, or evaluation rules. Keep one agent when those boundaries are weak, then add specialization only where a handoff preserves control or independent progress.
If you need help designing durable, observable AI agent workflows, Blazity can help you turn the workflow into contracts, routing rules, and production traces.
FAQ on multi-agent systems
These questions come up most often once a team has decided that one agent is not enough.
How many specialized agents should a workflow contain?
Add an agent only when it owns a boundary no existing agent owns. Each one should be able to work independently, fail independently, or protect a distinct permission scope.
What state should a coordinator pass between agents?
Pass the task identifier, validated input, required context, evidence, and current status. Avoid forwarding the full conversation unless the next agent genuinely needs it.
Does a multi-agent system cost more to run than a single agent?
Usually yes, because coordination adds model calls, serialized context, and retries that one reasoning loop never pays for. The offset arrives on long workflows, where a failed stage can be retried alone instead of rerunning the task from the start.
Which framework should you choose for a multi-agent system?
Let the control your workflow needs pick the framework. AutoGen suits conversational delegation, AutoAgents suits generated role hierarchies, and graph-based orchestration suits branching with explicit recovery paths.
How do you move from one agent to several without a rewrite?
Write the contracts first, while the work still runs inside one agent. Once each step has a validated input, an output schema, and a termination rule, splitting it out becomes a routing change rather than a redesign.
Sources
- AutoAgents: A Framework for Automatic Agent Generation, section 3 (April 29, 2024)
- AutoAgents: A Framework for Automatic Agent Generation, section 4 (April 29, 2024)
- AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation (October 3, 2023)
- ChatDev: Communicative Agents for Software Development (June 5, 2024)
- Multi-agent, LangChain documentation
- Why Do Multi-Agent LLM Systems Fail? (October 26, 2025)
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