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Start with one capable agent, not a swarm. Build the workflow as an ordinary program, add one tool-using agent, measure where it fails, and split out a specialist only when specialization, parallel work, permissions, or workflow control solves a demonstrated problem.
Your first useful multi-agent project can be a small research assistant: a supervisor plans the task, a researcher gathers evidence, a reviewer checks it, and the supervisor produces the answer. This teaches delegation, shared state, tool use, review, and termination without hiding the fundamentals behind an elaborate framework.
What a multi-agent system actually is
A multi-agent system is an application in which multiple software agents communicate, maintain state, and take actions toward a shared or coordinated goal. The agents may run in the same process, on different machines, or across organizational boundaries. They may use different models, instructions, tools, programming languages, and permissions.
In an LLM application, an agent usually combines four things:
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- A model that interprets instructions and produces decisions or text.
- Instructions that define its role, boundaries, output format, and escalation rules.
- Tools that let it retrieve information, call APIs, transform files, or perform other controlled actions.
- State and communication that preserve the relevant task information and pass work to another agent or program step.
The important distinction is not simply the number of model calls. A workflow with three independent prompts is not automatically a good multi-agent system. The components need clear responsibilities, a way to exchange results, and a rule for deciding what happens next.
Single agent versus multiple agents
A single agent with many tools has one model controlling the loop. It decides which tool to call, interprets the result, and determines the next step. This is often the best design for a task that has one coherent objective and a manageable tool set.
A multi-agent system divides control or responsibility. One component may plan, another may retrieve information, a third may validate it, and ordinary application code may enforce the workflow between them.
OpenAI’s practical guide to building agents, AutoGen’s team guidance, and LangChain’s multi-agent documentation all support the same beginner-friendly progression: keep a single agent manageable first, then introduce a team when the single agent is inadequate.
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Use multiple agents when the task has a specific architectural reason for being split. The strongest reasons are:
- Specialized context: different subtasks need substantially different instructions, source material, tools, or domain knowledge.
- Tool overload: one agent has so many tools that it regularly selects the wrong one or produces unreliable arguments.
- Parallel work: independent research, extraction, comparison, or classification tasks can run concurrently.
- Separate ownership: different teams need to develop, deploy, test, or maintain components independently.
- Workflow boundaries: the process contains clear stages, approvals, review gates, or handoffs.
- Different interaction modes: one agent interacts with the user while other agents work in the background.
- Isolation and permissions: sensitive data or high-impact tools should be exposed only to a narrowly scoped specialist.
Do not split a system merely because “multi-agent” sounds more advanced. Every additional agent can introduce another model call, another state boundary, another opportunity for a contradictory answer, and another failure mode. It may also increase latency and cost.
A quick decision test
| Question | If the answer is yes | If the answer is no |
|---|---|---|
| Does one role need very different instructions or tools from another? | Consider a specialist. | Keep one agent. |
| Can subtasks run independently? | Consider parallel workers. | Use sequential steps. |
| Must a person approve a stage before the next one? | Use an explicit workflow or approval gate. | A simpler loop may be enough. |
| Are permissions or sensitive data different at each stage? | Split access by agent or service. | Keep the permission model simpler. |
| Can you measure a failure in the current design that splitting might fix? | Run a controlled multi-agent experiment. | Do not add agents yet. |
The best first project: a research assistant with review
A research assistant is a useful teaching project because the stages are easy to explain and the quality checks are visible. The design below is a manager or supervisor architecture:
- Supervisor: receives the question, creates a short plan, and coordinates the run.
- Researcher: searches approved sources or a retrieval index and returns evidence in a structured format.
- Reviewer: checks whether the evidence is relevant, complete, contradictory, or unsupported.
- Supervisor: asks for focused follow-up research if necessary, then synthesizes the final answer with links or citations.
- Termination rule: ends the run after approval, an unrecoverable error, or a fixed maximum number of review cycles.
The supervisor should not pass the entire conversation and every available credential to every specialist. Give each component only the state and permissions it needs. For example, the researcher may search but not send email, while the reviewer may inspect evidence but not change an external document.
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Example state passed between agents
A small typed state object is easier to debug than a conversation in which every agent sees everything:
{
"task_id": "research-1042",
"question": "What are the main approaches to ...?",
"plan": ["find primary sources", "compare approaches", "check disagreements"],
"evidence": [
{
"claim": "...",
"source_url": "https://example.invalid/source",
"support": "direct quote or concise evidence"
}
],
"review": {
"approved": false,
"gaps": ["verify the date of the second claim"],
"contradictions": []
},
"turn": 1
}The URL above is only a placeholder showing the shape of the data; a real application should store URLs returned by its approved search or retrieval tools. Structured fields make it possible to validate results before they reach the next agent.
Common multi-agent architectures
There is no single correct topology. The right pattern depends on who controls the next transition and how much of the workflow should be explicit application code.
| Pattern | Good first use | Main strength | Main risk |
|---|---|---|---|
| Supervisor plus specialists | Research, support, document analysis | Central control and straightforward synthesis | The supervisor can become a bottleneck or single point of failure. |
| Router | Clearly separated request categories | Predictable entry routing to a specialist | Misclassification can send work to the wrong specialist. |
| Handoff | Customer-support escalation or domain transitions | Natural transfer of responsibility and conversation control | State, permissions, and auditability become harder to manage. |
| Parallel workers | Independent research, extraction, or comparison | Specialist focus and potentially lower wall-clock time | Results may conflict and the number of model calls can increase. |
| Shared graph workflow | Multi-step business processes | Explicit state, branching, looping, and checkpoints | More implementation and state-management overhead. |
| Round-robin team | Demonstrations and controlled discussion | Easy-to-understand coordination | Agents may repeat one another or waste turns. |
| Fully decentralized swarm | Advanced experimentation | Flexible peer-to-peer delegation | Debugging, termination, permissions, and evaluation are difficult. |
Supervisor or manager
A manager remains the user-facing controller and invokes specialists as tools or subroutines. It is usually the most approachable multi-agent pattern because there is one obvious place to inspect routing and synthesis. The supervisor can decide whether to call a researcher, reviewer, calculator, or domain specialist.
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Router
A router classifies the request before work begins. For example, billing questions go to a billing agent, technical questions go to a technical agent, and account changes go to a human-approved workflow. A router is valuable when categories are relatively clear. It is less suitable when the request must be explored before its correct specialist is known.
Handoff or decentralized control
With a handoff, the current agent transfers control and relevant conversation state to another agent. This can feel natural in customer support: a general triage agent transfers a complex coding issue to a technical specialist. The design must specify what state is transferred, which agent becomes responsible, and whether control can return.
Graph or workflow architecture
A graph makes the workflow explicit. Nodes can contain LLM agents, ordinary functions, or both. Edges determine what runs next, and conditional edges can route based on state. This is useful when the process has branching, loops, retries, checkpoints, or irreversible actions that should not be left entirely to a model.
Build your first system in ten steps
1. Write the task as a normal function
Before adding an LLM, describe the input, output, errors, and external actions. If the process cannot be explained as a sequence of testable steps, adding more agents will make the ambiguity worse.
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2. Add one agent with one or two tools
Give the agent a narrow objective and a small allowlist of tools. Define each tool’s typed inputs, expected output, failure behavior, and permissions. A tool should do one observable thing rather than expose an unrestricted shell or database connection.
3. Record representative failures
Collect real examples of wrong tool selection, missing information, unsupported claims, malformed output, excessive retries, and confusing instructions. These examples become your baseline test set.
4. Split only one responsibility
Choose the most clearly isolated failure. In the research project, that might be evidence review. Do not split planning, searching, writing, and citation checking all at once; otherwise you will not know which change helped or hurt.
5. Add explicit routing
Use a supervisor, classifier, conditional edge, or handoff function. Make the transition visible in logs. A useful routing decision should answer: who is called, why, with what state, and what result is expected?
6. Define a typed message or state schema
Do not rely on every agent to infer the meaning of free-form prose. Define required fields such as task_id, status, claims, sources, uncertainties, and next_action. Reject or repair malformed results before passing them onward.
7. Add termination, timeouts, and retry limits
Set a maximum number of turns, a per-tool timeout, and a retry budget. A reviewer that always requests “one more search” is a loop, not quality assurance. Terminate with a clear status such as approved, needs_human_review, or failed.
8. Trace every important event
Record model calls, tool calls, routing decisions, handoffs, state changes, latency, errors, and the final output. Include a run ID and agent name in every event. Without traces, a multi-agent failure often looks like a vague final answer rather than a diagnosable sequence of decisions.
9. Evaluate both designs on the same test set
Run the original single-agent version and the multi-agent version against identical representative tasks. Compare answer quality, completeness, citation correctness, tool-use accuracy, latency, cost, recovery from failures, and the frequency of human intervention. Keep the simpler design unless the extra coordination produces a measurable benefit that matters to your application.
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10. Expand only after the first split works
Once the supervisor-and-specialist boundary is stable, consider parallel workers, additional permissions, checkpoints, or a second specialist. Each new component should have a reason, an owner, a contract, and a test.
A framework-neutral starter implementation
You can learn the orchestration ideas with ordinary Python functions before choosing a framework. The following example intentionally leaves llm and search_tool abstract. It demonstrates the control flow without pretending that one vendor’s API signature is permanent.
from typing import Any, TypedDict
MAX_REVIEW_TURNS = 2
class State(TypedDict, total=False):
question: str
plan: list[str]
evidence: list[dict[str, str]]
review: dict[str, Any]
turn: int
def run_research_system(question: str) -> str:
state: State = {
'question': question,
'turn': 0,
}
# Supervisor: create a small, testable plan.
state['plan'] = supervisor_plan(question)
while state['turn'] < MAX_REVIEW_TURNS:
state['turn'] += 1
# Researcher: search only approved sources and return structured evidence.
state['evidence'] = researcher_search(
question=state['question'],
plan=state['plan'],
followups=state.get('review', {}).get('gaps', []),
)
# Reviewer: check support, completeness, and contradictions.
state['review'] = reviewer_check(
question=state['question'],
evidence=state['evidence'],
)
if state['review'].get('approved') is True:
break
# Supervisor: synthesize, or clearly disclose that review did not pass.
return supervisor_write(state)
def supervisor_plan(question: str) -> list[str]:
return llm_structured('Create a short research plan', question)
def researcher_search(question: str, plan: list[str], followups: list[str]):
return search_tool(question, plan, followups)
def reviewer_check(question: str, evidence: list[dict[str, str]]):
return llm_structured('Review evidence and return approved, gaps, contradictions', {
'question': question,
'evidence': evidence,
})
def supervisor_write(state: State) -> str:
return llm_structured('Write the answer using only supported evidence', state)This is already a coordinated multi-agent workflow if the three role functions use separate instructions, state, and tool permissions. It is also easy to test. You can replace one function at a time with an SDK agent, a graph node, or a message-driven component.
In production code, add schema validation, redaction of secrets, structured error results, timeouts around every external call, and a clear fallback when the reviewer does not approve the evidence.
Choosing a framework
Framework selection should follow the workflow you need to control, not the popularity of a particular library. The main beginner paths are:
OpenAI Agents SDK
The OpenAI Agents SDK is designed to orchestrate both single-agent and multi-agent applications. Its documented primitives include agents, tools, handoffs, guardrails, tracing, and observability. The API quickstart’s triage example shows one agent handing requests to language-specific agents, which makes it a clear illustration of decentralized routing.
The SDK has Python and TypeScript documentation, but platform capabilities change quickly. Check the current documentation for language support, model compatibility, tracing behavior, and production requirements before copying an example. The SDK is a practical option if you want managed agent abstractions, handoffs, guardrails, and tracing without designing every orchestration primitive yourself; it is not the only way to build the system.
Recent OpenAI Agents SDK material also describes sandbox-aware orchestration, file and shell operations, memory, checkpointing, and integrations with several sandbox providers. Availability can differ by capability and language. In particular, do not assume that a sandbox feature described in an announcement is already available in every SDK or TypeScript release. Any model-generated code execution should be isolated, constrained, and prevented from accessing application credentials.
LangGraph
LangGraph represents an agent workflow as a graph with three core concepts:
- State: the current application snapshot.
- Nodes: functions that perform computation, call a model, or execute a side effect.
- Edges: rules that determine which node runs next.
Graphs are compiled before use. Conditional edges or command-based routing can implement dynamic transitions, while explicit state makes branching, loops, checkpoints, and recovery easier to reason about. LangGraph also documents parallel execution within graph super-steps and handoffs through subgraphs and navigation to a parent graph.
LangGraph is a strong teaching choice when you want to see the workflow rather than leave every transition to a model. Remember that a graph node does not have to be an agent: it can contain ordinary deterministic code, an LLM call, a tool, or a combination of them. The graph representation and the broader concept of a multi-agent system are related but not identical.
Microsoft AutoGen
Microsoft AutoGen describes agents as self-contained units that communicate through messages, maintain state, and perform actions. Its current documentation separates:
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- AgentChat: a higher-level framework for conversational single-agent and multi-agent applications.
- Core: an event-driven framework for more scalable and potentially distributed systems.
AutoGen documents teams including round-robin, selector-based, handoff-based, and Magentic-One-style configurations. Its guidance recommends teams for complex tasks requiring collaboration and diverse expertise, while noting that teams require more scaffolding than a single agent.
The Core quickstart illustrates separate modifier and checker agents communicating through a runtime. AutoGen’s distributed-runtime documentation describes communication across process boundaries as experimental and subject to breaking changes, so treat distributed deployment as an advanced step rather than a beginner prerequisite.
Framework comparison at a glance
| Choose this direction when you want… | Likely fit | What to learn first |
|---|---|---|
| Agent abstractions, tools, handoffs, guardrails, and tracing | OpenAI Agents SDK | Role instructions, handoff contracts, tool permissions, and traces |
| Explicit state, branching, loops, checkpoints, and deterministic nodes | LangGraph | State schemas, nodes, edges, conditional routing, and graph compilation |
| Message-driven teams or event-based runtime concepts | AutoGen AgentChat and Core | Message contracts, team termination, runtimes, and the difference between local and distributed execution |
| Maximum portability or a small learning prototype | Plain application code first | Typed state, model adapters, tool wrappers, tests, and observability |
For additional depth after you understand the basics, a practical multi-agent systems book can help you work through architectures, orchestration, evaluation, MCP, A2A, and production concerns in a longer sequence. It is optional supplemental material, not a prerequisite and not a substitute for testing your own workflow. Publisher listings currently include titles such as Design Multi-Agent AI Systems Using MCP and A2A, Build a Multi-Agent System (from Scratch), AI Agents in Action, Second Edition, and Multi-Agent AI Engineering; check the edition and publication status before buying because this field changes quickly.
Designing reliable agent boundaries
Give each agent one job
A role such as “help with research” is too broad. A better researcher instruction says what sources it may use, what counts as evidence, what fields it must return, and when it should report uncertainty. A reviewer should not rewrite the entire answer; it should identify unsupported claims, missing evidence, contradictions, and approval status.
Use narrow tools and explicit permissions
Tools should be allowlisted. Validate arguments before execution, enforce limits in code, and return errors as structured data. A research tool might permit read-only searches with a maximum result count. A publishing tool might be available only after human approval. Never give an agent an unrestricted credential simply because it might be convenient.
Pass minimum necessary context
More context is not automatically better. Large shared histories raise token costs, increase latency, and make it harder to identify which instruction influenced a decision. Pass the question, relevant evidence, current status, and narrowly scoped instructions. Store large documents in a retrieval system or state store and provide only the relevant excerpts.
Make uncertainty first-class
Require fields such as confidence, missing_evidence, conflicts, and needs_human_review. A reviewer that can only return “good” or “bad” cannot explain what should happen next.
Reliability, security, and failure recovery
Adding agents does not guarantee more accurate answers. Current agent-system failure modes include hallucinations, unsafe or incorrect tool use, infinite loops, coordination breakdowns, excessive latency, inconsistent state, and permission or security failures.
Minimum safeguards
- Allowlist tools: expose only the functions each role needs.
- Validate arguments: check types, ranges, paths, URLs, record IDs, and authorization in application code.
- Separate credentials: keep secrets outside model-generated prompts and code-execution environments.
- Use boundaries: apply input and output guardrails at system edges, especially around external content and side effects.
- Limit execution: set maximum turns, request timeouts, retry budgets, token limits, and concurrency limits.
- Require structured inter-agent outputs: reject malformed messages rather than silently guessing what they mean.
- Trace delegation: preserve who called whom, with what state, which tools ran, and what each returned.
- Add human approval: require confirmation before sending messages, changing records, spending money, publishing content, or performing another irreversible action.
- Test hostile inputs: include prompt injection, malicious retrieved documents, credential-exfiltration attempts, and instructions that conflict with system policy.
- Give least privilege: each specialist should see only the context and permissions required for its responsibility.
Failure-handling branches for the research project
| Failure | Safe response |
|---|---|
| Search tool times out | Retry within the budget, record the timeout, then return a partial or human-review status rather than inventing sources. |
| Sources contradict one another | Send the conflict to the reviewer, preserve both sources, and state the disagreement in the final answer. |
| Researcher returns malformed JSON | Reject it, request one constrained repair, and terminate if the repair fails. |
| Reviewer keeps requesting more work | Stop at the maximum turn count and mark the result as needing review. |
| Retrieved content contains instructions | Treat retrieved text as untrusted data, not as authority to change agent instructions or permissions. |
| Agent requests a high-impact tool | Pause for an authorization check or human approval; do not let another model agent grant permission implicitly. |
Prompt injection deserves special attention. A web page, uploaded document, or search result may contain text designed to manipulate the agent. Keep untrusted content separate from system instructions, label its origin, restrict tool access, and inspect proposed actions before execution. Sandbox execution can reduce blast radius, but it does not replace permission checks, credential isolation, or review.
Observability and evaluation
Logging only the final answer is not enough. A useful trace records:
- Run ID, user request, and application version.
- Agent name, model call, instruction version, and routing decision.
- Tool name, validated arguments, result status, and elapsed time.
- State before and after each transition, with secrets redacted.
- Retries, timeouts, rejected outputs, handoffs, and termination reason.
- Final answer, citations, approval status, latency, and estimated cost.
Build a small evaluation set containing normal tasks and difficult cases. For the research assistant, check whether it found relevant evidence, represented disagreements, supported claims with citations, obeyed the tool policy, stopped when required, and recovered from a failed search. Compare the multi-agent version with the original single-agent version on the same inputs.
Useful measurements include answer quality, completeness, citation accuracy, tool-selection accuracy, malformed-output rate, average and worst-case latency, model-call count, cost, loop frequency, and human-review rate. A multi-agent design has earned its complexity only when it improves the outcome that your application actually cares about.
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Moving from a prototype to production
You do not need a cloud agent platform to learn or prototype. Plain Python functions, a model API, mocked tools, and local traces are enough to validate the architecture. Production infrastructure becomes relevant when you need managed deployment, scaling, centralized observability, durable state, secure execution, team ownership, or integration with existing services.
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Google Cloud’s Vertex AI Agent Engine documentation describes managed deployment and scaling, with integrations for the Agent Development Kit, LangChain, and LangGraph, plus custom templates for CrewAI and other frameworks. It is a possible production path, not a requirement for beginners. Verify current regions, supported frameworks, pricing, quotas, and observability features before committing to a deployment design.
The broader ecosystem is also moving toward portable capabilities. Recent OpenAI Agents SDK material discusses MCP, skills, sandbox execution, memory, checkpointing, and portable workspace abstractions. MCP is commonly used to describe a standardized way to expose tools or context to models, while A2A refers to agent-to-agent communication concepts. These terms do not eliminate the need for authentication, authorization, schemas, observability, or termination rules. Treat interoperability as an implementation aid, not as a complete safety architecture.
A practical learning plan
- Build a deterministic version of the workflow.
- Add one model-powered agent with a single narrow tool.
- Create a test set from representative successes and failures.
- Introduce one specialist for one measured weakness.
- Choose supervisor, router, handoff, or graph routing based on the workflow.
- Define typed state and message contracts.
- Add timeouts, retries, maximum turns, and human approval gates.
- Add traces and inspect complete runs, not just final text.
- Run the same evaluation set against both architectures.
- Keep the simpler system if the team does not produce a measurable benefit.
The goal is not to maximize the number of agents. The goal is to make responsibility, state, permissions, and failure handling clearer while improving the result.
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Frequently overlooked design questions
Should every agent use a different model?
No. Different models can be useful when speed, cost, context length, or specialized capability differs, but multiple roles can also use the same model with different instructions and tools. Decide from measured requirements rather than assuming model diversity improves quality.
Should agents talk directly to one another?
Only when direct communication makes the workflow clearer. A supervisor or explicit graph often provides better auditability for a beginner system. Direct peer-to-peer messaging can be appropriate for event-driven or distributed applications, but it requires stronger message contracts and termination controls.
Is a graph framework the same thing as a multi-agent system?
No. A graph is a way to represent state transitions. A node may contain an agent, ordinary code, a tool call, or several of these. Graphs can orchestrate multi-agent systems, but not every graph is multi-agent.
What should happen when the reviewer disagrees with the researcher?
Preserve both outputs, identify the exact conflict, and request targeted follow-up evidence. If the conflict remains unresolved after the turn budget, disclose the uncertainty or route the task to a person. Never resolve a factual disagreement by silently choosing the more confident-sounding output.
Frequently Asked Questions
Is a multi-agent system better than a single AI agent?
Not by default. Multiple agents add coordination, model calls, state management, latency, cost, and failure modes. Start with one agent and split the workflow only when measured specialization, parallelization, permissions, or review requirements justify it.
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A supervisor with two specialists is usually the clearest starting point. For example, a supervisor can delegate evidence gathering to a researcher and quality checks to a reviewer, then synthesize the result.
Which framework should I use first: OpenAI Agents SDK, LangGraph, or AutoGen?
Choose based on the workflow. OpenAI Agents SDK emphasizes agents, tools, handoffs, guardrails, and tracing; LangGraph emphasizes explicit state, nodes, edges, branching, and checkpoints; AutoGen emphasizes message-driven agents and teams. Plain application code is also a valid first step.
Do I need a cloud platform to build a multi-agent system?
No. A local prototype using ordinary application code, mocked tools, and a model API is sufficient for learning. Managed services such as Vertex AI Agent Engine become relevant when you need production deployment, scaling, observability, durable state, or organizational controls.
The Bottom Line
Build the first version as a single-agent or ordinary-code workflow, measure its failures, and then split out one narrowly defined specialist. Use typed state, restricted tools, explicit routing, hard termination limits, traces, and the same evaluation set for both versions. A smaller system that is observable and reliable is more valuable than a larger system that merely has more agents.
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