How to Run Devin AI in a Fast.io Coordination Room

Two coding agents pointed at the same repository will write conflicting changes and overwrite files without a shared coordination layer. The local editor session needs a mechanism to preserve file states, test logs, and configurations in a persistent cloud workspace that human developers and cloud-based agents can access in real-time. When an autonomous agent like Devin AI operates, it usually runs inside a sandboxed, ephemeral virtual machine. This isolated environment protects host machines from untested code execution, but it also creates a collaboration barrier. Human developers cannot inspect the agent's intermediate outputs in real time, and other agents cannot easily collaborate on the same files. Standard version control systems like Git are designed for source code, but they are not suitable for real-time state sharing of active execution logs, temporary build outputs, or intermediate design drafts.

Devin AI operates in a containerized environment to safely compile code, download packages, and run local servers. However, when the agent is finished or encounters an error, the workspace state is locked inside that environment. If the developer wants to see what the agent generated, they must wait for a Git commit or manually extract files from the container. Standard version control is too slow for the high-frequency reads and writes that happen during debugging. It does not handle temporary files, compiled binary assets, or detailed testing logs. If multiple agents or human teammates are working together, they quickly drift out of synchronization.

To resolve this issue, teams often try copying files to a standard Amazon S3 bucket or sharing a generic Google Drive folder. However, these tools are built for human synchronization or raw storage; they lack real-time agent APIs, structured metadata extraction, or integrated Model Context Protocol (MCP) endpoints. This is where Fastio is positioned. A Fastio coordination room provides a shared, persistent workspace where human developers and agents like Devin AI can collaborate, exchange files, and run joint tools via MCP.

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A Fastio coordination room is a specialized workspace folder that carries a server-owned room flag, distinguishing it from standard file storage. This room acts as a persistent communication and sharing substrate. Instead of agents blindly editing files and overwriting each other's changes, the coordination room enables a structured, chronological workflow.

When Devin AI or a human developer joins a room, they are registered as active participants. The coordination room supports real-time presence tracking, message sharing, and file handoffs. Communication is handled by the room tool within the Fastio MCP server. This tool provides actions like create, status, post, messages, and wait. An agent joins the room using room create (which is idempotent per workspace and topic slug), updates its status using room status, and posts updates with room post.

Traditional cloud drives are built for human-to-human sharing, relying on slow desktop synchronization clients. They do not notify other participants when a file is modified, and they do not have a built-in message stream. Fastio Coordination Rooms solve this by combining file storage and communication in a single, API-accessible environment.

The Coordination Room functions as a neutral ground. A server-owned flag indicates that this folder is monitored and configured for agent interactions. Both humans and agents can write to this directory. When a file is created or updated, the room records the activity. Instead of polling the directory constantly, agents can call room wait to receive notification of events immediately. This is highly efficient, reducing network overhead and preventing rate-limiting issues on large projects.

This setup creates a tangible handoff process. When Devin AI finishes a phase of code generation or analysis, it writes the resulting files to the room's storage folder and posts a status message to the room. The room wait action allows other participants to long-poll for updates, waiting for a specific event or status change without constantly querying the database.

These rooms support event-driven external workflows. By configuring webhooks for Coordination Rooms, teams can receive real-time notifications for room.message.created and room.participant.status_changed events. This allows external servers to react immediately when an agent posts a status update or completes a task.

Doing real work in these rooms requires a paid organization subscription. Every organization starts with a 14-day free trial, which requires a credit card. Plans are Starter at $29/mo, Business at $99/mo, and Growth at $299/mo. Creating an account is free; doing real work requires an organization on a paid subscription. Learn more on our pricing page.

To run Devin AI in a Fastio coordination room, you must connect Devin to the remote Fastio MCP server. Because this is a remote service rather than a local module, you do not run local setup commands. Instead, you register the HTTP endpoint with Devin's configuration.

The Fastio MCP server is available at https://mcp.fast.io/mcp/key for authenticated access. This endpoint accepts standard Bearer token authorization.

To configure the connection, you can add the server block to Devin's config file. Depending on your needs, you can store this in the global scope (~/.config/devin/mcp_config.json on macOS and Linux, or %APPDATA%\devin\mcp_config.json on Windows) or the project scope (.devin/mcp_config.json in your repository root).

Devin AI supports three levels of configuration to help teams organize their MCP integrations. First, the global scope is stored in your user configuration file. This is best for tools you want available across all coding projects, such as a personal documentation index. Second, the project scope is stored in your project's repository. Because this file is checked into Git, every member of your team automatically receives the same MCP server configurations. This ensures that when another developer launches Devin, the agent has the exact same tools available. Third, the local override scope is used for private settings and API keys. By keeping your Fastio API key in the local override file, you ensure it is never committed to your repository. This scope separation protects your credentials while allowing the team to share the core tool definitions.

Here is the JSON configuration block to connect Devin to the Fastio MCP server:

{
  "mcpServers": {
    "fastio-workspace": {
      "url": "https://mcp.fast.io/mcp/key",
      "transport": "http",
      "headers": {
        "Authorization": "Bearer YOUR_FASTIO_API_KEY"
      }
    }
  }
}

In the configuration above, the Authorization header passes your secure Fastio API key. Specifying "http" as the transport configures the connection over Streamable HTTP.

You can set up this connection in 4 steps:

1. Create the Coordination Room: Create a Coordination Room within a Fastio workspace. This room provides the shared space where Devin and human developers can collaborate.

2. Get API Keys: Open your Fastio organization settings, go to the API keys panel, and generate a new read-write API key.

3. Add Custom MCP in Devin: Open the Devin UI, go to the settings panel, select connections, then choose MCP servers, and click the Add a custom MCP button. Select HTTP as the transport, enter https://mcp.fast.io/mcp/key as the URL, and paste the API key as a header.

4. Run Sync Command: Join the coordination room by invoking Devin with instructions to write to the workspace or read from the room.

If you prefer using the CLI, you can add the remote MCP server by running the following command:

devin mcp add fastio-workspace https://mcp.fast.io/mcp/key

Devin's CLI detects the HTTP prefix and automatically registers the connection. By default, this command writes the configuration to the local override file (.devin/mcp_config.local.json), which is ignored by Git, preventing API keys from being committed to public repositories.

Coordinate Devin AI in your Fastio workspace

Set up a shared coordination room with a remote MCP endpoint to track agent progress, edit notes, and hand off organization ownership. Starts with a 14-day free trial.

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Once Devin AI is connected, human developers and the agent share a unified workspace environment. To prevent concurrent edits from corrupting files, Fastio provides per-file version history. When Devin writes files to the coordination room, the platform tracks changes with a complete version history. If the agent writes faulty code or saves an invalid configuration, you can view the version history and restore any prior state, ensuring that concurrent edits are auditable and easily recoverable.

In a collaborative room, multiple agents or humans might edit the same file. Standard storage platforms overwrite files silently, making it difficult to trace who made a change or why. Fastio resolves this by maintaining a complete history for every file. When Devin AI uploads a new version of a script, the old version is preserved. You can access the history through the web interface to compare files side-by-side or restore a previous version.

The append-only audit log records all activity. Every file read, write, rename, or deletion is logged with a timestamp and user ID. Because this log is immutable, it provides a reliable trail for security audits. If an agent behaves unexpectedly, you can review the log to see exactly what files it accessed and what modifications it made, making debugging straightforward.

An append-only audit log records every read, write, and permission change. This provides an immutable record of both agent and human actions, ensuring transparency throughout the project lifecycle.

Teammates can also collaborate in real-time using Collaborative Notes. Fastio Notes brings co-editing to every workspace with live multiplayer cursors. Human developers and AI agents can edit notes simultaneously with visible cursors. You can use this space to draft sprint plans, record agent prompts, or build checklists.

When Devin AI completes a project or builds out the required workspaces, the work can be handed off to a human sponsor. This is managed via ownership transfer. The agent account (which must be created as an agent account during signup) can create the organization, configure the workspaces, and then generate an ownership claim link.

The human sponsor receives the claim link, enters their payment details, and starts the 14-day free trial (credit card required). This transfers the organization and workspaces to the human. The agent account retains developer access, ensuring that Devin can continue to support the team while putting billing and admin controls in human hands. Every organization subscription runs on a paid plan: Starter at $29/mo, Business at $99/mo, or Growth at $299/mo. Creating an account is free; doing real work requires an organization on a paid subscription.

Software development projects generate structured data like test reports, benchmark metrics, and API schemas. Reading these files manually is slow, especially in large codebases. To solve this, you can use Fastio Metadata Views to turn documents into a queryable database inside your workspace.

Metadata Views extract structured fields directly from files without requiring you to write custom parser code or maintain complex OCR configurations. To extract structured data, you describe the fields you want in natural language. Fastio automatically designs a typed schema (supporting Text, Integer, Decimal, Boolean, URL, JSON, and Date & Time fields) and populates a filterable spreadsheet with the matching data from your files. Gemini 2.5 Pro designs these schemas automatically, suggesting columns and field types based on the actual contents of the files in your workspace.

In software development, structured data is often buried in unstructured text files. For example, a unit test suite might output a text file containing hundreds of test results. Writing custom scripts to parse these logs is time-consuming and fragile. Fastio Metadata Views automate this process by using AI to parse and organize your documents.

When you define a Metadata View, you instruct the system in natural language to find specific fields. For example, you can ask the system to extract the test name, status, and duration from every log file in the workspace. Gemini 2.5 Pro analyzes the files, designs a typed schema, and extracts the data into a spreadsheet. The schema supports various types, including Text, Integer, Decimal, Boolean, URL, JSON, and Date & Time. Because the schema is flexible, you can add new columns at any time without having to reprocess the entire workspace. This structured database allows developers and agents to sort, filter, and analyze test metrics efficiently.

For example, when Devin AI writes test log files to your coordination room, you can create a Metadata View with columns for Test Name, Status, Failure Reason, and Execution Time. The underlying system parses the logs and extracts these fields automatically, without requiring custom parser scripts. Developers and agents can query the spreadsheet, sort columns, and click through to the source log files.

Because the Fastio MCP server exposes Metadata Views programmatically, Devin can query these structured views to check the outcome of a test suite or find which components failed, enabling a tighter development loop. This structured database runs alongside the main RAG search index, separating the structured extraction layer from Intelligence Mode. This ensures that your agents can access both structured metrics and unstructured documentation in the same workspace environment.

For teams using shared workspaces, storing persistent data is essential, and linking local tools to Fastio workspaces provides the necessary infrastructure. By using a coordination room, human developers and Devin AI can work side-by-side, sharing a unified context and accelerating project delivery.