Multi-Agent Systems: AI Teams That Work Together

In Lesson 23 you met a single AI agent — one LLM with tools and a loop. It's powerful, but for big, messy tasks a lone agent gets overwhelmed, loses track, or tries to be a jack-of-all-trades. The fix mirrors how humans handle complex work: build a team. Multi-agent systems use several specialized agents that collaborate — each expert at one job — to accomplish far more than any single agent could alone. This is one of the most active frontiers in applied AI right now.

Why one agent isn't enough

Ask a single agent to "research this topic, write a detailed report, and fact-check it," and it juggles three very different jobs in one context — quality slips and it forgets earlier steps. Splitting the work lets each agent stay focused, keep a cleaner context, and do its one job well. Same reason companies have separate researchers, writers and editors.

Specialized agents: division of labour

Each agent gets a narrow role, its own instructions, and sometimes its own tools. A classic content-writing team might be:

• Researcher — gathers facts (has a search/RAG tool).
• Writer — turns research into a draft.
• Critic/Reviewer — checks quality and sends it back for fixes.

Because each has a single, clear job, each performs better than one agent wearing all three hats.

Orchestration patterns

How do the agents coordinate? A few common patterns:

• Manager / orchestrator: a lead agent breaks the goal into subtasks and delegates to specialists.
• Sequential pipeline: output flows from one agent to the next (research → write → review).
• Debate / critique loop: agents review and improve each other's work over rounds.

How agents communicate

Agents coordinate by passing messages — one agent's output becomes the next's input (a handoff) — and often through a shared memory or scratchpad they can all read and write, so the team keeps a common picture of progress. Clear handoffs and shared state are what keep a team from descending into chaos.

See a multi-agent workflow in code (runnable)

Here's the researcher → writer → critic pipeline with a revision loop. The "agents" are simple functions here (a real system swaps each for an LLM call), but the orchestration is exactly the real pattern.

# Each agent = a role. Real version: each calls an LLM.
def researcher(topic):
    return [f"fact about {topic}", f"another point on {topic}"]

def writer(facts, feedback=""):
    draft = "Draft: " + "; ".join(facts)
    if feedback:
        draft += f" (revised for: {feedback})"
    return draft

def critic(draft):
    # Approve only once the draft has been revised
    if "revised" in draft:
        return {"ok": True, "feedback": ""}
    return {"ok": False, "feedback": "add more detail"}

# Orchestrator: run the team with a critique loop
def run_team(topic, max_rounds=3):
    facts = researcher(topic)                 # 1. research
    draft, feedback = writer(facts), ""
    for round in range(max_rounds):
        review = critic(draft)                # 2. review
        print(f"Round {round+1}: approved={review['ok']}")
        if review["ok"]:
            return draft
        draft = writer(facts, review["feedback"])  # 3. revise
    return draft

print(run_team("neural networks"))

Run it: the critic rejects the first draft, the writer revises, and the second passes — a tiny but complete multi-agent loop.

Frameworks that do the plumbing

You rarely wire all this by hand. Libraries handle roles, messaging and orchestration for you — you'll hear names like LangGraph, CrewAI and AutoGen. They let you define agents and how they connect, and manage the loop, memory and handoffs.

Where multi-agent systems shine

• Coding teams: a planner, a coder and a tester agent building software together.
• Research pipelines: search, read, synthesise and write a report across agents.
• Complex workflows: customer requests routed through specialist agents (billing, tech, escalation).

The challenges

• Cost & latency: many agents = many LLM calls = more money and time (next lesson's topic).
• Error compounding: one agent's mistake can cascade through the team.
• Coordination & loops: poorly designed teams argue in circles or get stuck — cap the rounds.
• Harder to evaluate & secure: more moving parts (recall Lesson 32 and Lesson 33).

The honest takeaway: multi-agent systems are powerful but add real complexity. Reach for them when a task genuinely needs distinct skills — not just because they're exciting.

The core idea: break a hard problem into roles, give each role its own focused agent, and orchestrate the hand-offs — an AI team, working like a human one.

The first time I split a stubborn task into a "writer" and a separate "critic" agent, the jump in quality genuinely surprised me — the same model that produced mediocre one-shot output became sharp once one agent's only job was to critique the other's. That's when division of labour stopped being theory for me. My tip: start with just two roles (a doer and a reviewer) before building elaborate teams — you get most of the benefit with far less complexity.

Key takeaways

• Multi-agent systems use several specialized agents collaborating on a task.
• Splitting work into focused roles (e.g. researcher, writer, critic) beats one do-everything agent.
• Common patterns: orchestrator/manager, sequential pipeline, and critique loops.
• Agents coordinate via handoffs and shared memory; frameworks (LangGraph, CrewAI, AutoGen) handle the plumbing.
• Watch the trade-offs: cost, latency, error compounding and coordination — use a team only when the task needs it.

Continue the series: ← Lesson 34: Multimodal AI  ·  Next: Lesson 36 — Making AI Faster and Cheaper: Cost & Latency Optimization →