# How to Build a Company Brain Workflow for Engineering RFCs and Architecture Decisions

An engineering RFC company brain workflow unites architecture proposals, decision records, and technical specs inside an intelligent workspace. By indexing markdown files upon arrival and connecting coding agents over the Model Context Protocol, teams eliminate architecture drift, accelerate pull request reviews, and give human engineers and AI models verified institutional context.

Source: https://fast.io/resources/company-brain-workflow-engineering-rfcs/
Author: [Tom Langridge](https://fast.io/authors/tom-langridge/)
Last reviewed: 2026-11-11

## Why Static RFCs Create Architectural Drift Across Engineering Teams

When an engineering team adopts AI coding assistants, the primary point of failure is context isolation. An engineer asks Claude Code or Cursor to implement a distributed caching layer or design an internal API schema, and the agent generates functional code that directly violates an architectural decision the team agreed on six months ago. The agent did not make an algorithmic error; it lacked access to the team's historical Requests for Comments (RFCs) and Architecture Decision Records (ADRs). Without an active context layer, human engineers spend hours in code reviews relitigating settled trade-offs and catching architectural drift that could have been resolved before the first commit.

In a digital workplace survey by Gartner, 47% of digital workers reported struggling to find information or data needed to effectively perform their jobs. For software engineers building distributed systems, that friction compounds: when technical specifications and architectural decisions remain scattered across disconnected repositories and buried chats, developers waste hours reconstructing past decisions instead of shipping features. Teams write thorough RFCs to propose major system changes, discuss technical trade-offs across pull requests, and finalize architecture decisions. Once approved, those documents are archived in static wikis, buried inside closed GitHub pull requests, or scattered across disconnected cloud drives. Over time, these repositories become write-only archives. Human developers forget earlier edge-case discussions, newly hired engineers cannot discover why a specific database schema was chosen, and autonomous coding agents operate completely blind to internal conventions.

An engineering RFC company brain workflow is a centralized knowledge repository where architecture proposals, RFCs, and decision records are indexed for instant retrieval by human engineers and AI coding agents via MCP. Instead of treating architectural documentation as a passive library of static pages, a [company brain](/product/company-brain/) turns engineering records into an active context layer. By indexing technical documents upon arrival and exposing them to coding agents through standardized protocols, teams bridge the gap between historical design decisions and daily code generation.

### The Structure of Architecture Decision Records and Technical RFCs

Managing technical evolution requires distinguishing between exploratory proposals and settled architectural standards:

* **Requests for Comments (RFCs):** Collaborative proposals that outline large-scale architectural initiatives, migration strategies, or infrastructure overhauls. RFCs explore technical alternatives, solicit feedback from multiple teams, and document rejected approaches.
* **Architecture Decision Records (ADRs):** Short, focused documents that capture a single architectural choice, its context, and its permanent consequences. Once an ADR is accepted, it serves as the binding specification for subsequent implementations.

When teams store RFCs and ADRs as plain Markdown files in a centralized repository, they establish a durable record of technical intent. However, file storage alone does not guarantee discovery. If an engineer working on a microservice rewrite cannot surface the ADR that explains why the company avoids distributed transactions across specific database clusters, the documentation fails its core purpose.

### How Context-Blind Coding Agents Accelerate Technical Debt

Autonomous coding agents evaluate prompts against their training weights and whatever local files are open in the editor. While modern language models possess expansive context windows, those windows are session-bound and ephemeral. When a developer starts a new coding session, the agent retains no memory of previous architectural debates.

Left unguided, coding agents select generic patterns from public codebases. An agent might configure an unencrypted message broker queue, introduce an unapproved third-party dependency, or write schema migrations that conflict with company-wide tenancy rules. The resulting code compiles and passes unit tests, yet it degrades system architecture.

Fast.io turns RFCs into context that coding agents query through MCP. Connecting your development environment to an indexed [company brain](/product/company-brain/) lets coding tools check design specifications before writing code.

## How to Design an Intelligent Workspace for Architecture Records

Building an engineering company brain begins with the storage foundation. Engineering teams frequently attempt to solve document discovery by layering standalone enterprise search software over their existing drives. While search overlays can index disparate folders, they create operational separation: the search engine remains detached from the workspace where engineers review documents, write notes, and coordinate agent tasks.

Fast.io provides an [intelligent workspace](/product/workspaces/) where the files themselves live in the environment that AI agents and human engineers share. When RFCs and ADRs land in the workspace, Fast.io Intelligence Mode indexes them for hybrid search (combining lexical full-text matching with semantic vector search). Queries return answers with exact citations pointing to the source file name, page number, and specific text snippet, ensuring that every architectural claim is verifiable.

### Workspace Boundaries and Role Permissions

Engineering architecture repositories contain critical system designs, infrastructure credentials, network topologies, and proprietary data models. Access must remain well-defined and secure.

Fast.io manages permissions per organization, workspace, and share by member role. Workspace member roles include owner, admin, member, guest, and view. Inside a workspace, every member reaches all files according to their role; Fast.io enforces no folder-level or file-level permissions. This model avoids complex permission inheritance trees that break during migrations.

A Fast.io workspace join setting defaults to allowing any organization member to join. For general documentation, an open setting promotes transparency. For an engineering architecture repository containing sensitive infrastructure blueprints, administrators set the workspace join policy to restricted (invite-only). Only verified developers and designated AI agents receive access.

### Ingesting RFCs Across Toolchains with Cloud Sync

Engineering documentation often originates across multiple software environments. Systems teams might write infrastructure proposals in Google Docs, backend teams maintain ADRs in Markdown inside Dropbox, and security leads store compliance reviews in OneDrive.

Fast.io [Cloud Sync](/product/cloud-sync/) connects Google Drive, Dropbox, Box, and OneDrive folders directly to a workspace. Sync operations run one-way or two-way, on a schedule or on demand; sync is never continuous, live, or real-time. SharePoint document libraries connect through the OneDrive connector.

Files synced from an external drive adopt the Fast.io workspace's permissions exclusively, completely ignoring the sharing settings of the source drive. If a junior contractor has access to a broad Google Drive folder, syncing that folder into a restricted Fast.io engineering workspace does not grant the contractor access to the Fast.io workspace. The workspace boundary remains strict and independent.

### Structuring RFC Metadata with Metadata Views

While semantic search handles contextual queries, engineering leads also require structured filtering across historical decision cycles. Fast.io [Metadata Views](/product/document-data-extraction/) turns unstructured RFC and ADR documents into a queryable database without custom templates or manual OCR configuration.

Using Metadata Views, teams define the fields they need extracted in natural language. The underlying system populates a typed schema across the workspace:

* **RFC Number:** Sequential integer identifier (such as 104).
* **Document Title:** Full title of the technical proposal (Text).
* **Status:** Current lifecycle stage, including Proposed, Accepted, Rejected, Deprecated, or Superseded (Text).
* **Primary Author:** Lead engineer responsible for the design (Text).
* **Decision Date:** Timestamp when consensus was reached (Date & Time).
* **Affected Systems:** Services, microservices, and databases impacted by the decision (JSON array).
* **Superseded By:** Reference pointer to newer ADRs that replace the current decision (Text).

With Metadata Views, an engineer can sort the entire RFC catalog to inspect all accepted decisions affecting the authentication service before proposing a protocol change.

## Five Steps to Implement an Engineering RFC Company Brain Workflow

Transforming static architecture documents into an active context layer requires an end-to-end operational sequence. Rather than treating documentation as an afterthought, teams integrate proposal indexing into the developer workflow. When architecture records become an active part of daily code generation, engineers spend less time searching for historical rationale and more time writing verified code.

Standardizing this process across five clear stages ensures that every technical decision remains searchable by engineers and actionable for coding assistants:

1. **RFC repository organization:** Structuring Markdown templates, naming conventions, and lifecycle metadata.
2. **Document ingestion via Cloud Sync:** Connecting external drives and uploading technical specifications into a centralized workspace.
3. **MCP connection to coding tools:** Configuring Cursor, Claude Code, and terminal agents to communicate with the workspace endpoint.
4. **Prior-art cross-checking:** Querying historical ADRs during pull request drafting and system design reviews.
5. **Decision logging in Collaborative Notes:** Recording consensus and architectural updates in collaborative notes kept beside workspace files.

### Connecting Cursor and Claude Code via Model Context Protocol

Fast.io provides a remote Model Context Protocol (MCP) server over Streamable HTTP, and coding agents connect to `https://mcp.fast.io/mcp/code`, so IDE assistants search and read workspace files without a local sync daemon.

To connect Cursor to your engineering company brain, create or edit your Cursor configuration file at `~/.cursor/mcp.json` (or `.cursor/mcp.json` in the root of your project):

```json
{
  "mcpServers": {
    "fast-io": {
      "url": "https://mcp.fast.io/mcp/code"
    }
  }
}
```

To connect Claude Code in your terminal, add the HTTP server using the Claude CLI:

```bash
claude mcp add --transport http fast-io https://mcp.fast.io/mcp/code
```

After adding the server, sign in with OAuth in the browser, choosing Read Only or Read & Write on the Review Permissions screen. Detailed setup instructions and client configurations are available in the [Fast.io MCP documentation](https://mcp.fast.io/docs).

On the `/mcp/code` surface, coding agents interact with tools including `auth`, `auth_manage`, `upload`, `upload_manage`, `search`, `execute`, `execute_manage`, and `how-to`. The `search` tool performs hybrid semantic queries across indexed files, while `execute` handles read operations.

### Executing the AI Agent RFC Review Workflow

Once connected, coding assistants use the [workspace](/product/workspaces/) that holds the architecture decision records to evaluate code and validate new proposals.

During an RFC review backed by [Fast.io AI](/product/ai/), the engineer prompts the assistant directly inside the editor:

> "We are adding an in-memory caching tier to the checkout service. Search our engineering company brain for past RFCs or ADRs discussing Redis persistence, eviction policies, and cache invalidation patterns."

The agent calls the `search` tool on `/mcp/code`, scoped to the architecture workspace. The system returns exact excerpts from historical documents, such as `ADR-042-redis-caching-tiers.md`:

```markdown
## ADR-042: Redis Eviction and Distributed Locking Standards
Status: Accepted
Date: 2025-08-14

Decision:
All Redis cache clusters in production must use volatile-lru eviction.
Cache keys must declare an explicit TTL not exceeding 3600 seconds.
Distributed locks must use lease-based tokens with automated release heartbeats.
Direct pub/sub invalidation across regions is prohibited due to WAN latency spikes.
```

With this context retrieved, the agent can draft code that complies with company standards, avoiding prohibited cross-region pub/sub patterns and automatically including lease heartbeats.

### Capturing Consensus in Collaborative Notes

As discussions progress, engineering teams need a shared surface to record decisions before publishing a finalized ADR. Fast.io provides Collaborative Notes kept beside files in the workspace.

Notes allow human developers and connected agents to maintain living indexes, meeting outcomes, and review checklists. People and agents co-edit live: when multiple teammates or agents have the same note open, each sees the other's labeled cursors and edits as they type. When the team accepts an RFC, the lead engineer logs the final decision in a central architecture index note, cross-referencing the underlying document and updating the workspace index.

## When Multiple Agents Coordinate in an Architecture Workspace

In modern software development environments, multiple autonomous agents and human developers frequently work within the same repository simultaneously. A documentation agent might update API references, a security agent scans infrastructure manifests for policy violations, and human developers draft new RFC proposals. Without shared coordination mechanisms, concurrent writes cause file collisions, overwrite active drafts, and create conflicting architecture definitions.

Fast.io provides built-in mechanisms to coordinate human and agent activity across shared workspaces, combining file leases, full version history, and the detailed activity log. By establishing clear operating conventions, teams enable multiple specialized models to operate on architectural documentation without risking accidental data loss or clobbering work in progress. This multi-agent coordination ensures that engineering documentation remains reliable even as autonomous tooling scales across the organization.

### Coordinating Edits with Advisory File Locks

When an agent or developer initiates an extensive edit on an RFC document, it acquires an advisory file lock. Advisory locks are managed through MCP actions: `lock-acquire` and `lock-release` on the `storage_manage` tool, and `lock-status` on the `storage` tool.

An advisory lock functions as a visible lease. Other teammates and agents can inspect the file status to verify who holds the lock. Key characteristics of Fast.io advisory locks include:

* **Lease expiration:** A lock expires unless renewed by heartbeat and can be taken over by anyone with write permission.
* **Teammate visibility:** Any user or agent querying `lock-status` sees the active holder and lease expiration timestamp.
* **Non-exclusive write model:** Locks are advisory rather than restrictive. If another process writes to the file while locked, the write lands safely, and the workspace preserves both versions in file history.

This approach informs agents of active drafting sessions without blocking emergency human overrides or causing deadlocks.

### Auditing Changes with the Detailed Activity Log

Maintaining accountability across human developers and AI assistants requires an objective record of workspace activity. Fast.io records a detailed activity log of what changed in an organization and who changed it.

The detailed activity log captures:

* File uploads, edits, moves, and deletions
* Membership additions and role updates
* Workspace creation and configuration adjustments
* External share link creation and revocation

The activity log tracks changes, not read-only events such as views or downloads. Engineering managers can review the timeline to inspect when an ADR was updated, verify which agent committed a schema modification, and track configuration history across all workspaces.

### Event Feeds for Automated Compliance Pipelines

Teams running automated CI/CD checks can monitor architectural updates using the Fast.io events feed. Fast.io provides WebSocket feeds and long-poll subscriptions to workspace activity via `GET /current/activity/poll/{entity_id}?wait=95&lastactivity={timestamp}`.

An engineering platform script can subscribe to workspace activity. When a new file matching the `RFC-*.md` naming pattern is uploaded, the script receives an event, fetches the document via the REST API or MCP, and runs an automated linter to verify that required sections (Context, Decision, Consequences) are complete before team review begins.

## How Intelligent Workspaces Compare to Static Engineering Wikis

When selecting an architecture for an engineering company brain, technical leaders evaluate four primary approaches: traditional static wikis, enterprise search overlays, standard cloud storage, and intelligent workspaces. Each option represents a distinct balance between ease of setup, integration depth, and agent accessibility. Static wikis were built for human browsing, search overlays were created for cross-application discovery, and commodity cloud storage was engineered for passive file retention.

In contrast, an intelligent workspace is designed specifically to serve as an active coordination substrate where both people and autonomous agents collaborate directly on versioned files. Understanding the structural trade-offs between these options ensures that engineering teams select an infrastructure capable of supporting both human collaboration and autonomous coding agents as architectural documentation grows.

### Comparing Knowledge Base Architectures

The table below compares the four primary models across core operational criteria:

| Architecture Model | Primary Storage Location | AI Indexing & Grounding | Agent Protocol Support | Access & Permission Control |
| :--- | :--- | :--- | :--- | :--- |
| Static Wikis (Confluence, Notion) | Proprietary document blocks | Proprietary search; limited citation granularity | Official MCP servers (Notion, Atlassian Rovo) for pages | Complex page trees; prone to broken inheritance |
| Enterprise Search Overlays (Glean) | External third-party drives | Search index over connected apps | MCP server that can include write tools; no file storage of its own | Mirrors each source app's permissions |
| Commodity Cloud Storage (Drive, Box) | Object/file storage | Vendor AI on some drives (Box AI, Gemini in Drive) | Box offers an official MCP server; others vary | Nested folder permissions that drift over time |
| Fast.io Intelligent Workspaces | Native versioned file storage | Built-in Intelligence Mode with hybrid search and snippet citations | Remote MCP server over Streamable HTTP for Claude, Cursor, ChatGPT | Role-based permissions per organization, workspace, and share |

Wikis and search overlays now offer MCP servers, so coding assistants can query them, but neither hosts the team's working files, so agents cannot lock a file, co-edit a note beside it, or save outputs next to the sources.

Fast.io combines primary file storage, automatic RAG indexing, and remote MCP tooling in a single workspace, providing a complete operational substrate for engineering teams and AI coding tools.

### Transparent Subscription Plans and Resource Allocation

Fast.io operates on a transparent subscription structure based on seats, storage, workspaces, and usage credits. Doing real work requires an organization on a paid subscription.

Monthly plans start with a 14-day free trial that requires a credit card and ends early if trial credits run out:

* **Starter (`$29/mo`)**: Includes `5` seats, `1 TB` of storage, `10` workspaces, and `300,000` monthly credits. Maximum upload size is `25 GB`.
* **Business (`$99/mo`)**: Includes `20` seats, `10 TB` of storage, `100` workspaces, and `1,200,000` monthly credits. Maximum upload size is `50 GB`.
* **Enterprise (`$299/mo`)**: Includes `50` seats (expandable at `$1` per seat up to `200`), `50 TB` of storage, `300` workspaces, and `4,500,000` monthly credits. Maximum upload size is `100 GB`.

Annual pricing for plans is available at `$290`, `$990`, and `$2,990` per year. Credits cover AI work: chat, file ingestion, agent runs, and document processing. Credit overage is billed at `$10` per `100,000` credits. Additional storage costs `$0.015` per GB a month and extra bandwidth `$0.04` per GB, neither using AI credits. For complete tier details, visit the [Fast.io pricing page](/pricing/).

## Frequently asked questions

### How should an engineering team organize RFCs in a company brain?

Engineering teams should establish a dedicated, restricted-join workspace for technical documentation. Store documents as plain Markdown files categorized into exploratory RFCs and accepted Architecture Decision Records (ADRs). Use standardized naming conventions such as ADR-001-service-mesh-selection.md, and include structured frontmatter or headers specifying Status, Author, Decision Date, and Affected Systems. Fast.io Intelligence Mode indexes these files automatically upon arrival for hybrid search and AI queries.

### How can coding agents search internal architecture decision records via MCP?

Coding agents connect to the Fast.io remote Model Context Protocol (MCP) server over Streamable HTTP at `https://mcp.fast.io/mcp/code`. By adding this URL to the client configuration in Cursor, Claude Code, or VS Code, the agent gains access to search and execute actions. When an engineer asks a technical question or requests code generation, the agent queries the workspace index, retrieving relevant ADR snippets with file and snippet citations before generating code.

### What is the best way to index technical proposals for AI retrieval?

The most effective method is storing proposals in an intelligent workspace that executes hybrid indexing (combining semantic vector embeddings with lexical full-text search) upon document arrival. This eliminates the need for separate external vector databases, manual chunking pipelines, or custom OCR rules. Fast.io auto-indexes Markdown, PDFs, and office documents, returning exact page and snippet citations for every retrieved answer.

### How does Cloud Sync handle permissions for engineering RFCs?

Cloud Sync connects Google Drive, Dropbox, Box, and OneDrive folders to a Fast.io workspace on a schedule or on demand. Crucially, files synced from an external drive adopt the Fast.io workspace's permissions exclusively, completely ignoring the sharing settings of the source drive. This ensures that third parties or general employees with access to an external drive folder cannot view or query technical architecture documents inside a restricted Fast.io engineering workspace.

### What is the difference between Intelligence Mode and Metadata Views for architecture records?

Intelligence Mode provides unstructured retrieval-augmented generation (RAG), indexing text and diagrams for semantic search, chat queries, and source-backed citations. Metadata Views provide structured data extraction, using AI to turn unstructured RFCs into a typed, sortable database. Metadata Views extract fields such as RFC Number, Lifecycle Status, Primary Author, Decision Date, and Affected Services without requiring custom templates or manual data entry.

### How do advisory file locks protect engineering documents during multi-agent drafting?

Fast.io advisory file locks provide a cooperative lease mechanism managed via MCP actions on the storage and storage_manage tools. When an agent or developer begins editing an RFC, it acquires a lease that expires automatically unless heartbeated. Other teammates can check lock status to see who is editing. Because locks are advisory rather than restrictive, concurrent writes still land safely and are preserved in full per-file version history, preventing data loss.

## Sources

- [EMSNow: Gartner Survey on Digital Workplace Information Friction](https://www.emsnow.com/gartner-survey-reveals-47-of-digital-workers-struggle-to-find-the-information-needed-to-effectively-perform-their-jobs/): A survey by Gartner found that 47% of digital workers struggle to find information or data needed to effectively perform their jobs.

## About Fast.io

Fast.io provides shared workspaces where people and AI agents work on the same files, with built-in semantic search and citation-backed chat over what they hold. Agents reach it through a remote MCP server, a REST API at https://api.fast.io/current/, and a command line client published on npm as @vividengine/fastio-cli. MCP setup is at https://mcp.fast.io/docs: Claude and most MCP clients connect to https://mcp.fast.io/mcp/tools, ChatGPT to https://mcp.fast.io/mcp/operations, and coding agents to https://mcp.fast.io/mcp/code.