LESSON 64 · AI AGENTS

Multi-Agent Systems

Learn how multiple AI agents work together, divide responsibilities, share information, coordinate actions, and solve complex tasks.

35 min read•Intermediate•Generative AI

By the end of this lesson, you will be able to:

  • Explain what a multi-agent system is.
  • Understand why specialized agents can be useful.
  • Design clear roles and responsibilities.
  • Compare sequential, parallel, and hierarchical patterns.
  • Understand agent communication and shared state.
  • Identify cost, reliability, and safety challenges.
1

What Is a Multi-Agent System?

A multi-agent system is an AI application in which multiple agents cooperate to complete a task or shared goal.

Key idea:Instead of one agent performing every responsibility, specialized agents can handle different responsibilities and exchange results.
2

Why Use Multiple Agents?

Multiple agents can help when a task has clearly different responsibilities or independent work that can be safely coordinated.

Key idea:Specialization, parallelism, separation of responsibilities, and synthesis are common reasons to consider this architecture.
3

Single Agent vs Multi-Agent

A single agent can plan, use tools, observe results, and respond in one loop. A multi-agent system divides that work among cooperating roles.

Key idea:The trade-off is additional coordination, latency, token usage, and failure points.
4

Agent Roles

Useful roles include coordinator, researcher, analyst, writer, reviewer, and action specialist.

Key idea:Define a role by a real responsibility rather than simply creating many differently named agents.
5

Sequential Pattern

In a sequential pattern, one agent completes its responsibility and passes the result to the next agent.

Key idea:Example: Researcher → Analyst → Writer. Dependencies are explicit and the flow is easy to reason about.
6

Parallel Pattern

Independent tasks can sometimes be delegated to multiple agents at the same time.

Key idea:Example: Research A + Research B + Research C → Synthesizer. Parallelism is useful only when tasks are sufficiently independent.
7

Hierarchical Pattern

A manager or coordinator delegates work to specialist agents and combines their outputs.

Key idea:This can be useful when a complex task needs several levels of decomposition.
8

Agent Communication

Agents need a controlled way to exchange information.

Key idea:Pass a clear task, structured result, relevant context, and status instead of unnecessary conversation history.
9

Python: Structured Agent Result

Structured messages make coordination easier than passing unstructured text everywhere.

</> Python
research_result = {
    "agent": "researcher",
    "status": "success",
    "findings": ["Finding A", "Finding B"],
    "sources": 3,
}

if research_result["status"] == "success":
    print("Pass findings to the analyst")
else:
    print("Handle the failed research task")
10

Shared State vs Messages

Messages pass selected information between agents. Shared state lets components read controlled workflow state.

Key idea:Choose the smallest information boundary that lets the next agent perform its job.
11

Coordinator Agent

A coordinator routes work instead of doing every specialist task itself.

Key idea:For example, a coordinator can assign one research task per product, collect results, and send them to a comparison agent.
12

Python: Simple Coordinator

A small Python model can represent assignments before execution.

</> Python
tasks = [
    {"agent": "researcher_a", "topic": "product A"},
    {"agent": "researcher_b", "topic": "product B"},
    {"agent": "researcher_c", "topic": "product C"},
]

for task in tasks:
    print("Assign:", task["agent"], "->", task["topic"])
13

Multi-Agent Systems + Tools

Different agents can have different tool permissions.

Key idea:A research agent may have search tools, while an action agent may have narrowly scoped business tools.
14

Multi-Agent Systems + RAG

RAG can provide external knowledge to one or more specialist agents.

Key idea:A RAG agent can retrieve evidence, an analyst can evaluate it, and a writer can use validated findings.
15

Agent Review

A separate reviewer can inspect a candidate answer before it is returned.

Key idea:Generator → Reviewer → Revision → Final is useful when an independent checking stage provides measurable value.
16

Failure Handling

More agents create more places where something can fail.

Key idea:Detect the failing step, retry only when appropriate, route alternatives when safe, and stop when continuing could create an incorrect or unsafe action.
17

Cost and Latency

Every additional agent can add model calls, tokens, communication, and latency.

Key idea:Parallel independent work can reduce wall-clock time, but only when the coordination overhead is justified.
18

Safety and Permissions

Multi-agent systems should not give every agent unrestricted access.

Key idea:Use least privilege, validate messages and tool arguments, set step/retry/time limits, and require approval for high-impact actions when appropriate.
19

Common Multi-Agent Mistakes

Common mistakes include adding agents unnecessarily, sharing too much context, giving every agent every tool, and creating circular loops.

Key idea:A simple deterministic workflow is often better when the path is already known.
20

Mini Project: Research Team

Design a research team with a coordinator, researcher, fact checker, and writer.

Key idea:Challenge: require validated findings, set a retry limit, and stop when evidence is insufficient.
21

When Should You Use Multi-Agent Systems?

Use them when specialization, coordination, or safe parallelism provides real value.

Key idea:Avoid them for simple predictable workflows where additional agents only add overhead.
22

Quick Quiz

1. What is a multi-agent system?

A) Multiple cooperating AI agents   B) Multiple databases   C) One prompt

2. Why specialize agents?

A) To create clear responsibilities   B) To remove validation   C) To guarantee perfect answers

3. What is a benefit of parallel agents?

A) Independent work can run concurrently   B) All tasks become correct   C) Tokens become free

4. What should limit agent actions?

A) Application-side permissions and controls   B) The model alone   C) No limits

23

30-Second Recap

1Multiple agents cooperate toward a shared goal.
2Specialized roles can simplify complex tasks.
3Sequential, parallel, and hierarchical patterns solve different coordination problems.
4Messages, state, tools, and permissions should be controlled.
5More agents also mean more cost, latency, and failure points.
24

Key Takeaways

Remember:Use multiple agents when specialization or coordination provides real value. Start simple, define clear roles, limit permissions, validate communication, and add agents only when the architecture benefits from them.

The best multi-agent system is the simplest coordination model that reliably solves the task.