Multi-Agent Systems

A single AI agent is powerful. But many tasks benefit from multiple agents working in parallel, specializing in different aspects of a problem, checking each other’s work, or handling subtasks simultaneously. Multi-agent systems are the architecture for that.

Why Multiple Agents?

Three compelling reasons to use multiple agents instead of one:

1. Tasks Too Long for One Context Window

Research that requires reading 50 documents, synthesizing findings, and producing a report can’t fit in a single context. Split it: 5 agents each read 10 documents, then a synthesizer agent combines summaries.

2. Specialization and Quality

A generalist agent does everything adequately. A specialist agent does one thing well.

Single agent approach:
  "Research, write, and review this blog post" → mediocre result

Multi-agent approach:
  Researcher agent    → gathers facts, statistics, citations
  Writer agent        → drafts from research + outline
  Critic agent        → identifies weak arguments, factual errors
  Editor agent        → improves prose, structure, SEO

3. Independent Verification

One agent completes a task. A second agent verifies the result. This “checker” pattern dramatically reduces errors in high-stakes tasks.

Core Multi-Agent Patterns

Orchestrator → Subagents

A coordinator agent plans and delegates, specialized subagents execute.

                    ┌─────────────────┐
                    │  Orchestrator   │
                    │  (planner)      │
                    └────────┬────────┘
                             │ delegates tasks
            ┌────────────────┼────────────────┐
            ▼                ▼                ▼
     ┌────────────┐  ┌────────────┐  ┌────────────┐
     │  Research  │  │   Code     │  │   Write    │
     │   Agent    │  │   Agent    │  │   Agent    │
     └────────────┘  └────────────┘  └────────────┘
            │                ▼                │
            └────────────────┬────────────────┘
                             ▼
                     Results → Orchestrator → Final output

Pipeline / Sequential

Each agent’s output becomes the next agent’s input. Good for transformation workflows.

Raw Data → [Cleaner Agent] → Clean Data → [Analyzer Agent] → Insights → [Report Agent] → Report

Parallel / Scatter-Gather

Multiple agents work on subtasks simultaneously; a final agent combines results.

Task: "Compare Python, Go, and Rust for our new microservice"

              ┌──────────────────┐
              │  Scatter Agent   │ (splits task)
              └─────────┬────────┘
           ┌────────────┼────────────┐
           ▼            ▼            ▼
     [Python Agent]  [Go Agent]  [Rust Agent]
           │            │            │
           └────────────┼────────────┘
                        ▼
              ┌──────────────────┐
              │  Gather Agent    │ (synthesizes)
              └──────────────────┘

Debate / Reflection

Multiple agents argue different positions; a judge evaluates. Used for research synthesis, decision analysis.

Proposal → [Proponent Agent] argues for it
         → [Critic Agent] argues against it
         → [Judge Agent] evaluates both and produces balanced assessment

Agent Communication

Agents communicate in one of a few ways:

Direct messaging: Agent A calls Agent B’s function with a message and receives a response. Clean for synchronous workflows.

Shared state: Agents read/write to a shared data store (database, message queue). Enables async, loosely coupled workflows.

Message passing with a broker: Agents publish to queues; other agents subscribe. Enables fan-out patterns. Used in distributed agent systems.

# Simple orchestrator-subagent with LangGraph
from langgraph.prebuilt import create_react_agent
from langchain_anthropic import ChatAnthropic

model = ChatAnthropic(model="claude-3-5-sonnet-20241022")

researcher = create_react_agent(model, tools=[search_web, read_document])
writer = create_react_agent(model, tools=[create_outline, write_section])
reviewer = create_react_agent(model, tools=[check_facts, rate_quality])

async def run_pipeline(topic: str) -> str:
    research = await researcher.ainvoke({"messages": [{"role": "user",
                                         "content": f"Research: {topic}"}]})
    draft = await writer.ainvoke({"messages": [{"role": "user",
                                    "content": f"Write based on: {research['output']}"}]})
    final = await reviewer.ainvoke({"messages": [{"role": "user",
                                       "content": f"Review and improve: {draft['output']}"}]})
    return final["output"]

Real-World Multi-Agent Examples

Automated Code Review Pipeline

PR diff →
  [Security Agent] scans for vulnerabilities
  [Performance Agent] identifies slow patterns
  [Style Agent] checks conventions and naming
  [Test Coverage Agent] identifies untested paths
  ↓ all run in parallel ↓
  [Summarizer Agent] combines findings into PR comment

Research Report Generation (OpenAI Deep Research style)

Research question →
  [Query Agent] generates 10 search queries
  [Fetch Agent] retrieves 50 web pages in parallel
  [Extraction Agents] (×5, each processes 10 pages)
  [Cross-Reference Agent] finds contradictions and consensus
  [Citation Agent] formats references
  [Writer Agent] composes final report

Automated Data Pipeline Monitoring

Alert fires →
  [Log Analysis Agent] reads recent application logs
  [Metrics Agent] pulls Grafana/Datadog metrics
  [Code Agent] looks up relevant recent code changes
  [Diagnosis Agent] synthesizes all findings
  [Action Agent] creates JIRA ticket + pings Slack channel

Challenges and Failure Modes

Multi-agent systems introduce unique failure modes:

Error propagation: A mistake in step 1 compounds through steps 2, 3, 4. Intermediate validation checkpoints are essential.

Coordination overhead: Adding more agents doesn’t always improve results. Each handoff introduces latency and potential for miscommunication.

Context loss between agents: Agent A has rich context; Agent B starts fresh. Passing context between agents is a design challenge — too little and B lacks context, too much and tokens are wasted.

Blame assignment: When a multi-agent pipeline fails, which agent failed? Good logging is non-negotiable.

Cost multiplication: 5 agents with 10K tokens each is 50K tokens total. Budget carefully.

Frameworks Worth Knowing

Framework	Strength	Best For
LangGraph	Graph-based workflow, stateful	Complex branching pipelines
AutoGen (Microsoft)	Agent conversations, code execution	Research + coding tasks
CrewAI	Role-based, high-level API	Business process automation
Agno	High-performance, minimalist	Production at scale
Anthropic Claude Agents	Native tool use, raw API	Simple reliable agents
OpenAI Swarm	Lightweight handoffs	Simple agent routing

For most production use cases in 2026: LangGraph for complex workflows, raw API calls with well-designed tools for simpler orchestration. Don’t add framework overhead unless you need the feature.

The 2026 Horizon

Multi-agent systems are moving from research demos to production infrastructure. Patterns that are stabilizing:

Specialized agents as microservices: Each agent has a single responsibility and exposes a tool interface
Async / event-driven agents: Agents triggered by events (new data, user action), not just synchronous calls
Human-in-the-loop checkpoints: Agents pause for human approval before irreversible actions
Agent observability: Full traces of every agent decision, tool call, and message — essential for debugging

The mental model shift: think of agents not as “AI doing everything” but as “AI collaborating with humans and other AI in structured workflows.”