Multi-Agent Orchestration with Claude: Running Parallel AI Workflows

A single Claude agent is powerful. A coordinated team of Claude agents is transformative.

Multi-agent systems let you break complex tasks into parallel workstreams, run specialized agents simultaneously, and synthesize results that no single agent could produce alone. By early 2026, Anthropic reports that the most capable Claude deployments in enterprise use multi-agent patterns — not because single agents are limited, but because parallelism, specialization, and isolation make systems faster, more reliable, and easier to reason about.

This post covers the core patterns for building multi-agent systems with Claude: from simple subagent delegation to full swarm orchestration.

Why Multi-Agent?

Three fundamental reasons push teams toward multi-agent architectures:

1. Context window limits — Claude's context window, while large, is finite. A codebase with 500 files can't all fit in one prompt. Split it across agents that each own a module. 2. Parallelism — Tasks that are independent can run simultaneously. Analyzing 20 customer interviews sequentially takes 20x longer than running 20 agents in parallel. 3. Specialization — A security-focused agent with targeted tools and a strict system prompt outperforms a general agent given both security and feature tasks.

The Orchestrator-Worker Pattern

The most common multi-agent pattern is orchestrator + workers:

• The orchestrator receives the high-level task, breaks it into subtasks, dispatches workers, and synthesizes results
• Workers are specialized agents that execute one subtask and return a result

import anthropic
import asyncio
from typing import Any

client = anthropic.Anthropic()

async def run_worker_agent(
    task: str,
    system_prompt: str,
    tools: list
) -> str:
    """Run a specialized worker agent and return its output."""
    response = client.messages.create(
        model="claude-opus-4-6",
        max_tokens=4096,
        system=system_prompt,
        tools=tools,
        messages=[{"role": "user", "content": task}]
    )
    # Simplified — in production, handle tool loops here
    for block in response.content:
        if hasattr(block, "text"):
            return block.text
    return ""

async def orchestrate(high_level_task: str) -> str:
    # Step 1: Orchestrator plans the work
    plan_response = client.messages.create(
        model="claude-opus-4-6",
        max_tokens=2048,
        system="You are a project orchestrator. Break the task into 3-5 independent subtasks that can run in parallel. Output a JSON list of subtask descriptions.",
        messages=[{"role": "user", "content": high_level_task}]
    )
    # Parse subtasks from plan_response (simplified)
    subtasks = parse_subtasks(plan_response)

    # Step 2: Run workers in parallel
    worker_tasks = [
        run_worker_agent(
            task=subtask,
            system_prompt="You are a specialist. Complete your assigned task thoroughly.",
            tools=[]
        )
        for subtask in subtasks
    ]
    results = await asyncio.gather(*worker_tasks)

    # Step 3: Orchestrator synthesizes results
    synthesis_prompt = f"""Original task: {high_level_task}

Worker results:
{chr(10).join(f'Worker {i+1}: {r}' for i, r in enumerate(results))}

Synthesize these results into a comprehensive final answer."""

    final = client.messages.create(
        model="claude-opus-4-6",
        max_tokens=4096,
        messages=[{"role": "user", "content": synthesis_prompt}]
    )
    return final.content[0].text

Subagents in Claude Code

If you use Claude Code, subagents are a first-class feature. You can define specialized agents in your project's .claude/agents/ directory:

<!-- .claude/agents/security-reviewer.md -->
---
name: security-reviewer
description: Specialized agent for security code review
tools: Read, Grep, WebFetch
---

You are a security-focused code reviewer. For every file you analyze:
1. Check for SQL injection vulnerabilities
2. Identify XSS risks in frontend code
3. Review authentication and authorization logic
4. Flag hardcoded secrets or credentials
5. Check for insecure dependency versions

Always provide a severity rating (Critical/High/Medium/Low) for each finding.

Now the main Claude Code session can delegate to this agent:

> Use the security-reviewer agent to analyze all files in src/api/

Claude Code spins up the specialized agent, which runs with its own context, tools, and permissions — completely isolated from the main session.

The Pipeline Pattern

Not all workflows are parallel. Sometimes Agent B needs Agent A's output. This is the pipeline pattern:

async def content_pipeline(topic: str) -> dict:
    # Stage 1: Research agent
    research = await run_worker_agent(
        task=f"Research the latest developments in {topic}. Find 5 key facts with sources.",
        system_prompt="You are a research specialist. Be thorough and cite sources.",
        tools=[web_search_tool, fetch_url_tool]
    )

    # Stage 2: Writer agent (uses research output)
    draft = await run_worker_agent(
        task=f"Write a 800-word blog post about {topic} using this research:\n\n{research}",
        system_prompt="You are a technical writer. Write clearly for a developer audience.",
        tools=[]
    )

    # Stage 3: Editor agent (uses draft)
    final = await run_worker_agent(
        task=f"Edit this draft for clarity, accuracy, and SEO:\n\n{draft}",
        system_prompt="You are a senior editor. Improve without changing the author's voice.",
        tools=[]
    )

    return {"research": research, "draft": draft, "final": final}

The output of each agent feeds the next, creating a production-quality content pipeline.

Managing State Across Agents

Agents need a way to share information without polluting each other's context windows. Common patterns:

Shared file system — Agents read/write to agreed file paths. Simple and works well for Claude Code:

# Agent A writes results
with open("/tmp/agent_workspace/analysis.json", "w") as f:
    json.dump(results, f)

# Agent B reads them
with open("/tmp/agent_workspace/analysis.json") as f:
    prior_results = json.load(f)

Message passing — The orchestrator holds state and passes relevant context to each worker. No shared filesystem needed. Vector database — For large-scale information sharing, store agent outputs as embeddings and retrieve relevant chunks per agent.

Controlling Agent Permissions

In multi-agent systems, principle of least privilege is critical. Give each agent only the tools it needs:

SECURITY_TOOLS = [grep_tool, read_file_tool]  # Read-only
DEPLOYMENT_TOOLS = [run_command_tool, write_file_tool]  # Destructive

# Security agent gets read-only tools
security_agent = Agent(tools=SECURITY_TOOLS)

# Deployment agent gets write tools, but only in staging
deployment_agent = Agent(
    tools=DEPLOYMENT_TOOLS,
    system_prompt="You may only deploy to staging. Never touch production."
)

Real-World Example: Automated Code Review Pipeline

Here's a complete multi-agent code review system that enterprises are running in CI/CD pipelines:

PR Opened
    │
    ▼
Orchestrator Agent
    │  Reads PR diff, assigns review tasks
    ├──────────────────────────────────┐
    ▼                                  ▼
Security Agent              Performance Agent
(grep for vulns,            (analyze complexity,
 check OWASP top 10)        flag N+1 queries)
    │                                  │
    └──────────────┬───────────────────┘
                   ▼
            Style Agent
       (check naming, docs,
        test coverage gaps)
                   │
                   ▼
            Synthesis Agent
       (combine findings into
        PR comment with severity)
                   │
                   ▼
            GitHub API
       (post review comment)

This pipeline runs in 2-3 minutes vs. 30+ minutes for a human review, catches 80%+ of common issues, and runs on every PR automatically.

Performance Considerations

Token costs scale linearly with agents — 10 parallel agents use 10x the tokens. Profile before scaling. Use smaller models for simpler tasks — Route straightforward classification tasks to claude-haiku-4-5 and complex reasoning to claude-opus-4-6. This can cut costs by 10x. Add timeouts — Parallel agents can hang indefinitely. Always set asyncio.wait_for() with sensible timeouts. Monitor with traces — Use a tracing library (LangSmith, Langfuse, or custom logging) to visualize which agents ran, what they called, and where time was spent.

Conclusion

Multi-agent orchestration isn't just about handling larger tasks — it's about building AI systems that are modular, maintainable, and production-ready. The orchestrator-worker pattern, subagent isolation, and pipeline composition are the building blocks of serious AI applications in 2026.

Start small: take one workflow you currently run with a single agent and split it into two specialized agents. Measure the quality improvement. Then scale from there. The most impressive AI systems aren't built with a single genius agent — they're built with well-coordinated teams of focused ones.