How to Build a Claude Cowork Multi-Agent Orchestration Loop

Multi-agent orchestration in Claude Cowork involves coordinating several Claude agents to work concurrently on shared files and tasks without conflicts. While individual AI agents handle bounded tasks well, building real-world software and processing complex data pipelines requires a team approach. An orchestrated system routes specific subtasks to specialized agents, manages their execution cycles, and combines their output into a final product.

In a traditional prompt-response interaction, an AI model receives a single instruction, generates text, and stops. Agentic systems operate differently. They run in continuous loops, executing a "plan, act, verify, adapt" cycle. During this cycle, an agent can use tools to read files, run code, search the web, and modify directories.

When you scale to multiple agents, you need an orchestration layer to manage the team. It decides which agent handles which file, prevents two agents from editing the same script simultaneously, and tracks overall progress.

A concrete example: you ask an orchestrated system to build a web application. The orchestrator spawns a Frontend Agent to write React components and a Backend Agent to write the database API. Both run in parallel, reading from the same requirements document but writing to separate directories. When they finish, a Review Agent inspects the combined codebase for integration errors.

The benefit of this approach is focus. Narrow worker prompts produce fewer hallucinations and more reliable output than one agent trying to do everything. The cost is complexity — shared state, error propagation, and observability all become harder when multiple autonomous processes are running concurrently.

Before building complex orchestration, it helps to understand why single-agent workflows eventually break down. Many developers start by giving one Claude instance a large system prompt containing dozens of tools and instructions.

The first constraint is the context window. Claude 3.5 Sonnet has a 200,000 token context window. While large, an autonomous agent working through a complex coding task will rapidly fill this window with tool execution outputs, terminal errors, and conversational history. Once the context is saturated, the agent begins to forget its initial instructions or loses track of the main goal.

The second constraint is prompt drift. When a single agent handles research, planning, coding, and testing, its focus splits. It may focus on a minor testing error and forget that it still needs to implement a core feature. The broader the job, the less reliable the execution.

Finally, a single agent suffers from tool overload. Giving an agent fifty different tools hurts its decision-making capability. The agent spends more tokens reasoning about which tool to use than actually solving the problem, leading to inefficient loops and execution failures.

Multi-agent orchestration solves all three problems. It distributes the token burden, maintains strict role boundaries, and restricts tool access to only what is necessary for a specific job.

The right architecture depends on your task. Here are the patterns that come up most often in Claude orchestration setups.

The Supervisor (Leader-Worker) Pattern

A central Supervisor receives the user's request and routes it. The Supervisor doesn't execute code or write files — it maintains a list of Worker agents, delegates tasks, and reviews output. If a Worker fails, the Supervisor re-prompts or assigns the task elsewhere. This is the easiest architecture to debug, which is why most teams start here.

The Pipeline Pattern

For predictable, linear workflows, the Pipeline pattern is efficient. In this setup, the output of Agent A becomes the input for Agent B. For instance, a Data Extraction Agent reads invoices and outputs JSON. A Validation Agent checks the JSON against a schema. Finally, a Database Agent writes the validated data to PostgreSQL. This pattern requires little active orchestration because the handoffs are predetermined.

The Evaluator-Optimizer Pattern

When quality is more important than speed, this pattern works well. A Generator agent produces a draft or a code snippet. An Evaluator agent reviews the output against strict criteria and provides targeted feedback. The Generator then revises its work based on the feedback. This loop continues until the Evaluator approves the output.

The Swarm Pattern

In a true swarm, there is no central leader. Agents broadcast their availability and capabilities to a shared message bus. When a task appears, the agents negotiate among themselves to determine who is best suited to handle it. While interesting from a research perspective, swarms are difficult to debug and are generally not recommended for production workflows.

The most common way multi-agent systems break down in practice is file conflicts. Several agents working on a shared repository can overwrite each other, read half-written files, or trigger race conditions that corrupt the project state. This isn't hypothetical — it happens fast, and it's hard to debug after the fact.

Five practices that prevent it:

1. Explicit file locks. Before an agent modifies a file, it acquires a lock from the orchestration layer. If another agent holds the lock, the requester queues or waits. No simultaneous edits.

2. Directory ownership. Designate specific agents as sole owners of particular directories. Only the Backend Agent writes to /api. Others can read, not write. This is the simplest coordination rule and the one most teams skip — until they regret it.

3. Private scratchpad directories. Give each agent a private directory for temporary files, terminal output, and test runs. Only verified output moves to the shared directory.

4. Version control as a safety net. Every significant write creates a new commit or file version instead of overwriting. If a worker hallucinates and corrupts data, the supervisor can roll back.

5. Atomic writes. Agents write to a temporary file first, then rename it to the target. This prevents other agents from reading a partially written file mid-operation.

Coordinating multiple agents requires a storage environment that handles concurrent, programmatic access. Most cloud storage was designed for human users clicking through folders, which creates friction when agents need rapid read-write cycles.

The main options developers use:

• S3-compatible object storage (AWS S3, Cloudflare R2, Backblaze B2): Straightforward and cheap at scale, but you manage permissions, versioning, and locking yourself. No built-in agent tooling.
• Git repositories: Version control built in, and well-supported by agent tooling. Works well for code workflows, less well for binary files or large datasets.
• Fast.io: An API-first workspace with MCP support, built-in file locking, and ownership transfer. Agents and human reviewers share the same workspace without separate infrastructure.

For teams building on Claude specifically, Fast.io's native MCP server is the most direct integration. Every capability available in the web UI has a corresponding agent tool, so Claude can read, write, and organize files via Streamable HTTP and SSE transport without custom API work. Built-in file locking means agents can acquire a lock on a shared document, complete their edit, and release it without a race condition. Intelligence Mode indexes workspace files automatically, so agents can run semantic search without setting up a separate vector database. A free tier for AI agent accounts covers initial development.

Give your Claude agents a shared, lock-safe workspace

Fast.io provides concurrent file access, built-in file locking, and MCP tools so your Claude agents can work in parallel without overwriting each other's output.

Try the MCP Server

Start smaller than you think you need to. A working two-agent loop teaches you more than a perfectly architected five-agent system that breaks on the first run.

Step 1: Set up the shared environment. Create a Fast.io workspace and generate API credentials for your agents. Connect the Fast.io MCP server to Claude and enable Intelligence Mode so agents can query project documents directly.

Step 2: Write distinct system prompts for each agent. If you're building a research team, define a Web Researcher with browser tools, a Data Analyst with Python tools, and a Writer with file creation tools. Keep capabilities siloed. The moment an agent can do anything, it starts doing the wrong thing.

Step 3: Build the Supervisor last. The Supervisor's system prompt must explicitly say it doesn't write code or output files. It reads project_status.md, delegates subtasks to workers, and reviews their output. Give it a task delegation tool that triggers workers. That's it.

Step 4: Build error handling before you need it. When a worker times out or hits a terminal error, the orchestration script catches it, appends the error log to context, and re-prompts the Supervisor for a recovery plan. Do this before your first real run, not after.

Step 5: Start with exactly two handoffs. Run it, watch the logs, and track token usage and tool failure rates. Audit logs are the main debugging tool here. Once the two-handoff version works reliably, add complexity.