A tour of aac.muthub.org, the open prototype I built to show architecture as a control plane, and the design choices that mattered most: catalogs, executable guardrails, waivers as data, a computed scorecard and an MCP server for coding agents.
Handing an AI agent a production codebase without a harness is like putting a teenager in a sports car with no dual brakes. Deterministic guardrails and sensors, owned as a platform, are what make agents safe to scale.
AI now writes code faster than any review board can read it, while architecture still moves at meeting speed. Governance has to become something that executes in the pipeline and in the agent's context.
Shipping AI-generated code without recording where it came from is unmanaged risk. Regulated teams need deterministic provenance for non-deterministic models: line-level attribution, Git-native session history and AI bills of materials.
As agents multiply, every enterprise needs an authoritative directory of the tools they can call. What a governed MCP registry must do, what the official registry leaves to you, and the mistakes to avoid.
Agents act on their own, at machine speed, across many systems. Static credentials and perimeter thinking don't hold up. Attested identity, explicit delegation and policy enforced outside the model do.
The Model Context Protocol standardizes how agents connect to tools and data. It is silent on whether they should. Four risk areas a regulated institution should resolve before agents get tools.