Devin AI vs. Windsurf: Understanding the Cognition Ecosystem

At the time of its acquisition by Cognition AI in July 2025, the agentic IDE Windsurf had already reached $82 million in Annual Recurring Revenue (ARR) and served hundreds of thousands of daily active users [Cognition 2025 Blog]. This transaction, which doubled Cognition's ARR, set the stage for the June 2, 2026 rebranding of Windsurf to Devin Desktop. The migration represents a fundamental shift in how developers interact with AI agents: transitioning from standalone assistant extensions to a unified, autonomous-to-interactive engineering ecosystem.

Rather than operating as a disconnected code completion tool, the rebranded IDE functions as part of Cognition's broader agent orchestration framework. It acts as a local command center where developers can run, monitor, and collaborate with autonomous agents directly on their local machines. For teams that previously used Windsurf for interactive editing and Devin AI for asynchronous background tasks, this merger unifies the workflow. The division between local interactive coding and cloud-based autonomy is narrowing, forcing developers to rethink how they manage source files, context, and environment configurations. Connect your local tools to a Fast.io persistent workspace to make agent output team output.

Before the acquisition, Windsurf operated as an independent product developed by Codeium, a company originally founded as Exafunction by MIT graduates Varun Mohan and Douglas Chen. Codeium built Windsurf to pioneer the concept of an agentic IDE, driven by its Cascade agent which was capable of handling multi-file edits and simple command executions. However, as the AI engineering landscape shifted toward fully autonomous systems capable of planning and debugging complex projects, the limitations of standalone interactive editors became clear. Cognition AI's acquisition of Windsurf brought together Codeium's interactive user interface patterns and Cognition's long-horizon autonomous reasoning systems, creating a consolidated path for team-wide deployment.

When developers run agents, they must distinguish between remote container sandboxes and local execution loops. Both approaches serve distinct phases of software development, but they present unique challenges for team collaboration, storage persistence, and resource management. Choosing the right environment requires analyzing the architectural differences that define Devin AI and Devin Desktop.

To understand how these tools fit into a developer workflow, you must analyze their underlying architecture. While both tools share Cognition's reasoning engine, their operational models, execution environments, and levels of autonomy differ.

Devin AI is Cognition's cloud-native, fully autonomous software engineer. When you assign a task to Devin AI, the agent spins up a sandboxed container in the cloud, accesses its own terminal, runs its own web browser, writes code, executes tests, and debugs errors. It operates asynchronously, meaning a developer can assign a complex migration task and check the progress hours later. The developer does not need to maintain an active IDE session or provide constant micro-steering.

In contrast, Devin Desktop is a local, interactive IDE based on VS Code. Instead of running in a remote sandbox, Devin Desktop operates directly on the developer's local machine. It replaces the legacy Cascade agent with a Rust-based agent called Devin Local, which is designed for low-latency, token-efficient local operations. Devin Desktop functions as an interactive command center, supporting the Agent Client Protocol (ACP) so that developers can run Devin Local, cloud-based Devin agents, or compatible third-party agents within the same interface.

Evaluating these attributes reveals that cloud-native sessions offer safety through isolation, whereas local runs provide faster response times and direct access to local development configurations.

The rebranding of Windsurf to Devin Desktop brought a complete rewrite of the local agent. The legacy Cascade agent was replaced by Devin Local, a tool written in Rust to optimize token usage and minimize the latency of local filesystem operations. Devin Local runs tasks by breaking them down into smaller sub-agents that execute in parallel. This allows the editor to search directories, index code symbols, and generate completions without blocking the main developer thread.

Crucially, Cognition designed Devin Desktop around the Agent Client Protocol (ACP). By using an open standard protocol, the IDE does not restrict developers to Cognition's proprietary models. You can connect Devin Desktop to other ACP-compliant agents, such as Claude Code or custom models, allowing different agents to operate on the same local workspace.

However, running local agents introduces significant security and configuration overhead. Because Devin Local operates on your local machine, it has direct access to your command line, environment variables, and local databases. Unlike Devin AI's cloud sandbox, an unverified script executed by a local agent can corrupt local configurations or compromise sensitive keys. Developers must establish strict boundaries to govern what files these agents can access, edit, or share with external endpoints. To coordinate files across these systems, teams require a centralized persistence layer that connects to both local editors and cloud environments.

When developers deploy both Devin AI in the cloud and Devin Desktop locally, they run into a major synchronization problem. A cloud-native agent working on a separate branch in a remote container needs access to the same documentation, test assets, and architectural specifications as the local agent running on a developer's laptop. Storing these assets in local directories makes them inaccessible to cloud agents, while traditional cloud storage services fail to provide the real-time access and tool integrations that agents require.

This is where teams must move beyond generic file shares and establish a dedicated workspace layer. While developers can use standard S3 buckets or Google Drive to share files, these tools do not provide the structured APIs, granular permissions, or version tracking that autonomous agents need. Fast.io solves this by providing shared, organization-owned workspaces designed for human-agent collaboration.

Fast.io acts as a persistent intelligence layer that bridges local and cloud environments. When a developer uploads project assets to a Fast.io workspace, the platform automatically indexes the files. With Intelligence Mode enabled, the workspace serves as a built-in retrieval-augmented generation (RAG) system. Both Devin AI and Devin Desktop can query this database using semantic search to retrieve up-to-date documentation or API schemas.

Furthermore, Fast.io tracks a complete, per-file version history. When multiple agents and humans edit the same configuration files or collaborative notes, Fast.io preserves every iteration. If Devin Local writes a buggy configuration file during a local debugging session, developers can audit the changes and restore a previous version. This version history ensures that concurrent agent work remains auditable and reversible.

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To interact with a shared workspace, agents need a structured way to read, write, and search files. Devin Desktop's open architecture allows developers to extend its capabilities by connecting it to Model Context Protocol (MCP) servers. The platform exposes a consolidated MCP toolset that agents can call to perform file operations, query databases, or execute workflows.

Fast.io supports this ecosystem by exposing its tools through two primary connection methods: Streamable HTTP at /mcp and legacy Server-Sent Events (SSE) at /sse. Developers can configure Devin Desktop to connect to the Fast.io MCP server by adding the endpoint to the IDE's configuration file.

Here is an example of an MCP configuration block that connects Devin Desktop to the Fast.io server:

{
  "mcpServers": {
    "fastio-workspace": {
      "command": "npx",
      "args": [
        "-y",
        "@fastio/mcp-server"
      ],
      "env": {
        "FASTIO_API_KEY": "your_secure_org_api_token",
        "FASTIO_WORKSPACE_ID": "ws_devin_ecosystem_992"
      }
    }
  }
}

Once connected, Devin Local can use these tools to pull files from cloud sources without executing local input-output commands. Using Fast.io's URL import capability, the agent can retrieve documents from Google Drive, Dropbox, OneDrive, or Box via secure OAuth connections. This prevents the agent from needing to download large datasets to the local disk, saving bandwidth and local storage space.

The agent can also query the workspace using Fast.io's hybrid search, which combines full-text, semantic, and metadata-value filters. Instead of scanning directories manually, Devin Local calls the search tool to find files containing specific functions or variables, speeding up the planning phase of local coding tasks.

In addition to writing code, engineering workflows require processing unstructured documents, such as API specifications, compliance reports, or customer feedback. Rather than asking an LLM to read through these documents repeatedly, teams can use Fast.io's Metadata Views to turn them into structured, queryable databases.

Using Metadata Views, developers or agents describe the fields they want to extract in natural language. The system's AI automatically designs a typed schema, including Text, Integer, URL, or Date & Time columns, and matches files within the workspace. The platform then extracts and populates a spreadsheet without requiring pre-defined templates or manual optical character recognition (OCR) rules. Agents can trigger this extraction and query the structured outputs via the MCP server, allowing them to make decisions based on structured contract terms, invoices, or log files.

When an autonomous agent like Devin AI finishes building a tool, setting up a database, or generating documentation in a workspace, it needs a way to hand over the assets to a human team member. Fast.io supports this through ownership transfer. An agent can set up a workspace, configure permissions, and transfer the administrative ownership to a human colleague. The agent can retain access to continue running tasks, but the human user gains full control over the billing, sharing settings, and audit logs.

Every action taken by both human developers and autonomous agents is recorded in Fast.io's append-only audit log. This log tracks who read, modified, or shared a file, providing teams with a reliable way to audit agent behavior and ensure security. When a project is complete, the team can share the outputs using branded shares. These shares can be configured as durable or expiring links with granular access controls, allowing clients to download the deliverables securely.