How to Run Folder-Level Q&A Workflows on Devin AI Outputs

Two coding agents pointed at the same repository will happily overwrite each other's work, and neither will notice. The fix is not a smarter agent, it is a shared place to put work where the second one can see what the first one did. A Devin AI session can generate dozens of output files across multiple directories, including refactored source code, build logs, test transcripts, and visual testing screenshots. The core challenge for development teams is not generating this information, but scoping context to a specific folder containing only the outputs of a particular run. A Devin AI folder-level Q&A workflow allows developers to query a collection of documents and code generated by an agent within a specific workspace folder, receiving semantic, source-cited responses. Rather than searching across the entire workspace or reading massive text files individually, developers can scope their queries to a single folder. This approach provides structured context, allowing team members to ask targeted questions about Devin's outputs and receive precise answers based on that specific execution session.

When a Devin session ends, human developers must review and audit these outputs. In standard workspaces, running Q&A or search over the entire repository dilutes the context, blending Devin's recent changes with the legacy codebase. On the other hand, looking at single documents one by one is slow and ignores cross-file relationships. For example, if a developer wants to understand why a test failed, they must look at the test output log, the changed source file, and the package configuration file. Trying to run a semantic search over Devin files or executing folder-level Q&A in Devin helps developers examine all of these files collectively within a restricted scope. Isolating the agent's work in a specific folder ensures that the retrieval-augmented generation context is precise, preventing unrelated files from polluting the search results.

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To build a reliable folder-level Q&A workflow, teams need a shared workspace where humans and AI agents can collaborate on the same file context. Standard cloud storage options are designed for human file sync and lack the APIs and indexing interfaces that autonomous agents require. Fastio provides persistent, shared workspaces where developers and AI agents collaborate on the same files and folders. Instead of managing complex database connectors or building custom document processing pipelines, developers can connect Devin AI directly to a Fastio workspace. The connection is established via the Model Context Protocol, which allows agents to interact with external tools and data sources.

Fastio exposes a remote Model Context Protocol server over Streamable HTTP. To connect Devin to a workspace, developers configure the remote server in Devin's session environment. The configuration points to the remote Fastio remote MCP server at https://mcp.fast.io/mcp/code and includes the long-lived API key granted by the human workspace administrator. Because Devin AI supports remote MCP connections, it can call Fastio tools directly to create folders, write code files, upload logs, and read context during execution. Detailed guidelines are available in the Fastio documentation.

Here is an example configuration block that can be added to Devin's environment settings:

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

During a session, Devin uses this remote MCP connection to create a designated folder within the workspace and upload its outputs. For instance, Devin can write its files to a path like devin-outputs/session-105/. This isolates the session files from the rest of the repository, establishing a clean folder structure.

Once Devin AI writes its outputs to the designated folder, Fastio's Intelligence Mode processes the files. In commodity cloud storage, files are passive blocks of data. Finding information requires exact filename matches or manual parsing. Fastio features Fastio Intelligence Mode, which automatically indexes files for semantic search, summarization, and citation-backed chat. When files arrive in a workspace, they are parsed and indexed immediately without requiring developers to build custom retrieval-augmented generation pipelines or configure vector databases. In workspaces created by agents, Intelligence Mode is enabled by default.

The indexing system uses hybrid search, which combines exact full-text matching with semantic, meaning-based retrieval. The search indexes both filenames and file contents. For developers reviewing Devin's outputs, this hybrid approach is critical. Full-text matching allows developers to search for exact error codes, function names, package dependencies, or variables. Semantic search allows developers to search by meaning. For example, a developer can search for "build failures caused by package version mismatches" even if that exact phrase does not appear in the log files.

This background indexing ensures that the workspace is prepared for natural language queries immediately after Devin completes its coding session. Developers do not need to wait for manual batch indexing jobs or manage vector databases. Fastio handles the ingestion and chunking processes automatically, allowing both humans and agents to query the files in real time. Because the index is updated incrementally, any new file uploaded by Devin is searchable within seconds.

With the output files indexed, developers can run folder-level Q&A in Devin sessions using Ripley AI, the embedded agent that lives in Fastio workspaces. Ripley allows users to search and summarize files using natural language. To prevent context dilution, developers can scope their queries to the specific folder where Devin wrote its outputs. Fastio workspaces support folder-level Q&A using Ripley AI, ensuring that Ripley only reads the documents inside the designated folder. During a query, Ripley extracts page-level citations for every answer from files inside the designated folder.

This capability allows developers to run complex, scoped queries against Devin's outputs. For example, a developer can ask Ripley, "Summarize all code regressions identified in the test logs inside this directory." Ripley will analyze the files in the directory and return a natural language answer. Each answer includes page-level citations that link directly to the source files, the specific page, or the exact code snippet. Developers can click on a citation to open the in-browser preview of the file, allowing them to verify the claims instantly. This workflow eliminates the need to manually inspect dozens of files, speeding up code reviews and quality assurance checks.

This folder-scoped approach provides a major advantage over searching the entire repository. If the query were run over the entire codebase, Ripley might pull in outdated code or unrelated test results from other directories, leading to incorrect or confusing answers. By restricting the query to the output folder, the developer ensures that Ripley only uses the files generated during that specific Devin session. This guarantees that the answer is accurate and relevant to the session under review.

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Reviewing and managing agent outputs is a collaborative effort. Once the folder-level Q&A is complete, developers must integrate Devin's work into the main codebase or hand the workspace over to other team members. Fastio supports this transition with per-file version history and audit trails. Every file in a workspace keeps a full version history. If a developer edits a file generated by Devin, the prior version is preserved, and the team can compare versions or restore older copies. The append-only audit log provides an immutable record of all file operations, showing exactly when Devin uploaded a file and when a human developer modified it.

If an agent is used to set up the organization and workspaces for a project, Fastio supports ownership transfer. The agent can build the workspace structures, configure folders, and transfer ownership of the organization to a human administrator via a claim link when the handoff is complete. The agent can retain administrative access to run future sessions. This allows developers to outsource workspace setup to an agent while maintaining full control over the final production environment.

Getting started is straightforward. Creating an account is free, but doing real work requires an organization on a paid subscription. Every organization starts with a 14-day free trial, which requires a credit card. Teams can choose from three subscription tiers: Fastio Starter plan at $9.99/mo, Business at $49.99/mo, and Enterprise at $199.99/mo. This credit-based pricing model allows organizations to pay for the AI tokens they use, with credits metering AI operations. This flexibility ensures that teams of any size can use persistent workspaces for their agentic workflows.