What Are Multi-Agent Systems?

A team at the University of Pennsylvania published a finding that most multi-agent tutorials gloss over: in a two-agent setup where one model generated answers and a second model critiqued them, the pair outperformed the single-model baseline by 31% on multi-step reasoning tasks. Same base model for both agents. Same weights. Different role. The improvement came entirely from structure, not from a smarter model.

That asymmetry is what makes multi agent systems explained properly so valuable. You're not building a bigger brain. You're building a structure where AI instances check each other in ways a single instance never can.

What is a Multi-Agent System?

A multi-agent system (MAS) is an architecture where two or more AI agents coordinate to complete tasks that benefit from specialization, parallel execution, or adversarial validation. Each agent is an independent LLM call with its own system prompt, context window, and optionally its own tools and memory.

The critical distinction from "running several chatbot sessions" is coordination. In a MAS, agents share state, their outputs influence each other's inputs, and an orchestration layer decides what runs when. Without that coordination, you don't have a system — you have parallel experiments.

Here's the minimal working version: a generator-reviewer pair.

[object Object], anthropic ,[object Object], Anthropic

client = Anthropic()

,[object Object], ,[object Object],(,[object Object],) -> ,[object Object],:
    response = client.messages.create(
        model=,[object Object],,
        max_tokens=,[object Object],,
        system=,[object Object],,
        messages=[{,[object Object],: ,[object Object],, ,[object Object],: task}]
    )
    ,[object Object], response.content[,[object Object],].text

,[object Object], ,[object Object],(,[object Object],) -> ,[object Object],:
    response = client.messages.create(
        model=,[object Object],,
        max_tokens=,[object Object],,
        system=,[object Object],,
        messages=[{
            ,[object Object],: ,[object Object],,
            ,[object Object],: ,[object Object],
        }]
    )
    ,[object Object], response.content[,[object Object],].text

task = ,[object Object],
draft = generator_agent(task)
feedback = critic_agent(draft, task)
,[object Object],(,[object Object],)

What this does: The generator writes without self-censorship. The critic starts with a completely fresh context — no reasoning history, no anchoring to the draft's logic — and evaluates the output against the original task. Two independent perspectives from the same model family.

Why Multi-Agent Systems Outperform Single Agents

This is worth understanding mechanically, not just conceptually. Single agents fail in three specific ways that multi-agent systems address directly.

Attention drift on long tasks: When a single agent handles a 20-step task, its representation of constraints set early in the conversation weakens as the context grows. By step 15, the agent has functionally forgotten what it decided at step 3. This isn't a bug — it's how transformer attention works. Multi-agent systems solve this by giving each agent a shorter, focused context. Each agent only processes the information relevant to its specific piece of the problem.

Specialization ceiling: A system prompt that says "You are a security researcher who reviews code exclusively for authentication vulnerabilities" shapes model behavior differently than "You are a helpful assistant who can also review code." Focused identity changes what the model attends to and what it chooses to say. Running five specialized agents on the same document produces five genuinely different perspectives — not five variations on a single response.

Validation blind spots: A single agent asked to self-critique its own output is anchored to its own reasoning chain. It can find surface errors, but it tends to rationalize the deeper logical choices it already made. A separate critic agent starting from a blank context has no such anchor. That's where the 31% improvement came from.

⚡ Pro tip: If you can only add one agent to an existing workflow, make it a critic. The generation-validation pair is the highest-return multi-agent pattern and requires no complex orchestration — just two sequential API calls with different system prompts.

Core Components of a Multi-Agent System

Four elements appear in every functioning MAS regardless of framework choice.

Agents: Individual LLM instances configured with system prompts. Each agent has a role, a focus, and optionally tools and memory. Importantly, the agent doesn't inherently know it's part of a larger system — you tell it what it needs to know through its system prompt and the inputs it receives.

Orchestrator: The coordination logic that decides which agent runs when and what data flows between them. This can be a dedicated "planner" agent that reasons about sequencing, or plain Python code with conditional logic. For production systems, code-based orchestrators almost always win over LLM-based ones — they're deterministic, free of hallucination risk, and don't burn tokens on routing decisions that a simple if-else handles instantly.

Communication channels: How agents exchange information. The simplest channel is a function return value — one agent's output string becomes the next agent's input string. More complex systems use structured JSON payloads, message queues, or databases with versioned state.

Shared state: A persistent data structure that spans multiple agent calls. Without shared state, each agent starts completely blind. With it, downstream agents can read the original user request, intermediate results from earlier agents, and constraints established early in the pipeline.

[object Object],
task_state = {
    ,[object Object],: ,[object Object],,
    ,[object Object],: ,[object Object],,
    ,[object Object],: {},
    ,[object Object],: {,[object Object],: ,[object Object],, ,[object Object],: ,[object Object],}
}

,[object Object], ,[object Object],(,[object Object],) -> ,[object Object],:
    response = client.messages.create(
        model=,[object Object],,
        max_tokens=,[object Object],,
        system=,[object Object],,
        messages=[{
            ,[object Object],: ,[object Object],,
            ,[object Object],: ,[object Object],
        }]
    )
    state[,[object Object],][,[object Object],] = response.content[,[object Object],].text
    state[,[object Object],] = ,[object Object],
    ,[object Object], state

What this does: The agent reads both the original request and constraints from shared state, then writes its output back to the same structure. Any downstream agent can read state["agent_results"]["analysis"] without knowing anything about how the analysis agent works internally — loose coupling through shared state.

How Multi-Agent Systems Are Structured in Practice

Four topologies cover the vast majority of real-world use cases.

Pipeline: Agents execute in a fixed sequence. Each agent's output feeds the next. Simple to reason about, easy to test in isolation, limited to one agent executing at a time. Best for document transformation, content production workflows, and step-by-step processes with clear dependencies.

Fan-out: An orchestrator dispatches the same or similar task to multiple agents simultaneously, then aggregates results. Ideal for research, risk analysis, and competitive intelligence — anywhere independent perspectives on identical input add value.

Supervisor-worker: A supervisor agent decomposes complex tasks, assigns subtasks to specialized workers, evaluates results, and retries when workers underperform. Most production systems evolve toward this pattern because it handles failure gracefully and scales naturally.

Peer debate: Two agents with opposing instructions — or different areas of expertise — reason about the same problem. The orchestrator synthesizes their outputs or lets them iterate on each other's reasoning. Useful for high-stakes decisions where adversarial quality control needs to be baked into the architecture itself.

One thing most tutorials don't mention: multi-agent systems are trust hierarchy problems as much as they are coordination problems. Your supervisor needs explicit criteria for evaluating worker output. Your orchestrator needs policies for when agents disagree. Getting these right matters as much as choosing the right topology.

⚡ Pro tip: Define your trust hierarchy explicitly in your supervisor's system prompt. Write specific evaluation criteria — not just "check the quality" but "verify that the response cites at least two data sources, stays under 300 words, and doesn't make claims about future performance." Vague supervision produces vague results.

Common Mistakes When Starting With Multi-Agent Systems

⚠️ Common mistake: Designing the full architecture before validating the concept. The most common failure pattern is sketching an eight-agent system on a whiteboard, spending three weeks building it, and discovering that the first two agents already solve 90% of the original problem. Start with one agent. Find where it actually fails. Add a second agent to address that specific failure. Then stop and measure before adding a third.

Three other patterns come up repeatedly in production systems:

Orphaned original intent: An agent at step 5 that only receives step 4's output — without the user's original request — will drift from what the user actually wanted. Always pass the original task through the full chain, even when it feels redundant. The downstream agents need that context.

Missing inter-agent error handling: What happens when Agent B receives malformed output from Agent A? Systems that don't answer this question fail in production in ways that are genuinely difficult to diagnose. Define retry policies, fallback behaviors, and escalation paths before you ship.

Over-specialization at the wrong granularity: Agents with hyper-narrow responsibilities — "paragraph formatting agent," "comma placement agent," "tone adjustment agent" — create coordination overhead that costs more than it saves. Specialization makes sense at the task level: code review, legal analysis, customer communication. Not at the sentence level.

The Starting Point

Multi agent systems explained at their core are coordination patterns. You're not making the model smarter. You're giving it a more focused context, a more rigorous validation pipeline, and the ability to run in parallel where the task allows.

Start with two agents. Measure the quality difference. Add a third only when you can name the specific problem it would solve.

Patterns Worth Knowing Before You Build

Three patterns appear in almost every successful multi-agent system, regardless of domain or complexity.

The generation-validation pair is the simplest and highest-return pattern. One agent generates output; a second agent reviews it against explicit criteria. The generator doesn't self-review — it produces freely. The validator doesn't generate — it evaluates precisely. This separation produces better output than a single agent asked to "generate and check your work," because the two roles pull reasoning in different directions: generation favors completeness, validation favors accuracy.

The pipeline with handoffs chains agents sequentially where each agent receives the previous agent's output as its primary input. Research → synthesis → formatting is a classic example. Each agent in the chain has a narrow, well-defined job. The pipeline design makes failures easy to localize: if the final output is wrong, you inspect each agent's output in sequence until you find where the error was introduced.

The supervisor-workers model uses a routing agent to direct tasks to the appropriate specialist and then aggregate their outputs. The supervisor handles task distribution; workers handle domain-specific execution. This pattern scales well to large teams of specialists without increasing any individual agent's complexity.

⚡ Pro tip: Name your agents for their function, not their technology. "content_researcher," "argument_validator," and "report_writer" are better agent names than "agent_1," "gpt4_agent," and "claude_agent." Functional names make orchestration code self-documenting and make system behavior easier to reason about when debugging production issues.

If you're building out your agent system prompt library — the specialized instructions that define each agent's role and behavior — PromptABCD makes it easy to store, version, and reuse them across projects without starting from scratch every session.