Your architecture is now an AI-ready asset: The Context-as-a-Service playbook

Let's start with an uncomfortable truth for September 2025: two versions of your company’s architecture now exist in parallel.

There’s the official one, carefully documented on your internal wiki. Then there’s the shadow architecture, being built piece-by-piece, commit-by-commit, by dozens of AI agents sourcing their patterns from the public internet. If you don’t have a strategy to govern the second, it will quietly overwrite the first.

This isn't a future risk; it's a present reality. Generative AI is already inside your organization. Your engineers are using it to write code, refactor applications, and generate configurations. But the default path for these tools leads to generic knowledge that knows nothing of your specific security postures, preferred cloud services, or hard-won architectural lessons.

The answer isn't to ban the new tools. It's to give them a better set of blueprints. Your internal architecture is a unique, high-value asset, and it's time to treat it that way.

CaaS transforms governance from a bottleneck into a service

For decades, architectural governance has been a fundamentally human—and often high-friction—process. It relies on design reviews, documentation wikis, and the oral tradition passed down from senior engineers. This model cannot operate at the speed of AI.

CaaS flips the model on its head.

Instead of asking developers to read documentation and remember the rules, CaaS provides a machine-readable context that AI agents can consume directly.

This aligns perfectly with one of my core principles: the Primacy of Initial Design. A project's success is determined by the quality of its initial inputs. CaaS ensures that from the very first prompt, the AI is working with the right inputs—your inputs. It’s the ultimate "shift left" for architectural compliance, embedding governance directly into the moment of creation.

The CaaS playbook is a pragmatic, four-phase rollout

Building a full-scale CaaS platform doesn't happen overnight. It's a strategic journey that builds value at each step, managing risk and complexity along the way. This is a marathon, not a sprint, designed for flexibility and sustainable adoption.

Here is the four-phase playbook to get there.

Phase 1: Establish a beachhead with centralized context files

The goal here is speed and feedback. You begin by creating a simple set of text files (CONTEXT.md, RULES.txt) that codify the most critical, high-level standards for a specific domain. Developers connect their local AI agents to these files manually.

• Objective: Prove the concept and gather early feedback.
• Trade-off: This is a manual process. The files can become unwieldy, and keeping them synchronized is a challenge. But the cost of entry is near zero.

Phase 2: Build the single source of truth with llms.txt

Manual files don't scale. The next step is to treat your documentation like a product. You create a centralized website where architecture and service documentation is rewritten to be optimized for machine readability—structured, self-contained, and topic-based. The context files from Phase 1 evolve into a standardized llms.txt file at the root of your documentation hub. Similar in spirit to robots.txt, this file points AI agents to the canonical sources of truth.

• Objective: Create a scalable, high-quality source of truth.
• Trade-off: You've solved the sync problem, but the llms.txt file itself can grow excessively large. Pointing an AI agent to a monolithic context that is essentially a full copy of your documentation can diminish its performance and focus.

Phase 3: Enable intelligent retrieval with the Model Context Protocol (MCP)

This is the pivotal shift from "push" to "pull." You introduce a server that implements the Model Context Protocol (MCP), the de facto standard for this interaction. This MCP server acts as an intelligent broker in front of your documentation hub. Instead of parsing a giant llms.txt file, the AI agent is given a tool that allows it to query the MCP server, which can then search the documentation and retrieve only the most relevant pages on its own.

• Objective: Make the context dynamic and relevant.
• Trade-off: This is a huge leap in quality, but it can be inefficient. Multiple search-and-fetch round trips between the agent and your MCP server can be slow and costly.

Phase 4: Achieve peak performance with Retrieval-Augmented Generation (RAG)

This is the destination. You index the content from your documentation hub into a vector database. Your MCP server is then reconfigured to query this RAG system directly instead of searching the website's text. This is purpose-built for finding the most relevant semantic information with incredible speed and accuracy.

• Objective: Maximize the relevance, accuracy, and efficiency of AI-generated code.
• Trade-off: This requires more specialized infrastructure and expertise, but the payoff is a system that seamlessly integrates your enterprise context into every AI interaction.

This phased journey transforms architectural knowledge from static text into a dynamic, queryable asset.

Where this is headed: Composable and proactive context

Phase 4 is a massive milestone, but it's not the end of the story. The future of CaaS is even more integrated. Imagine a system where the context is dynamically composed based on the developer's team, the project's risk profile, and real-time security alerts. A CaaS platform could proactively warn an AI agent before it generates code using a deprecated library or an insecure pattern.

This is the ultimate goal: a system where compliance isn't just enforced, it's the path of least resistance.

By investing in a CaaS strategy now, you aren't just solving a short-term governance problem. You are building a foundational capability that will define your organization's ability to leverage AI safely and effectively for years to come. You're turning your most valuable intellectual property—how you build things—into a scalable, intelligent, and defensible advantage.

Spark the Conversation

1. Contrarian Question: What if the biggest risk isn't AI generating non-compliant code, but your best engineers leaving because your AI governance is too restrictive and stifles their productivity? How does CaaS solve for both?
2. Diagnostic Poll: On a scale of 1-4, where is your organization on the CaaS journey today?
   1. The Wild West (No formal guidance for AI agents)
   2. Phase 1/2 (Using manual files or a central llms.txt)
   3. Phase 3 (Experimenting with an MCP-based approach)
   4. Phase 4 (Actively implementing or using a RAG-based system)
3. Actionable Challenge: Identify one high-value, well-documented architectural standard in your company this week. As a thought experiment, how would you distill it into a simple Phase 1 CONTEXT.md file for an AI agent? What are the three most important rules it would contain?