Questions about governed AI.

Real AI governance is not a policy document or a filter that blocks employees from pasting data into chatbots — that is a perimeter wall. It means that when an autonomous AI agent touches your business systems, it executes your corporate rules deterministically, every time. It is an execution-level standard, not a passive checklist.

MCP is an open standard for connecting AI agents to tools and context through a uniform interface. Model My Context provides an open-source MCP engine that exposes your governed skills and bounded context as clean, headless endpoints any MCP-compatible agent can call.

Stop sending your entire business to the model on every call. Naive “all-in-one” prompts can burn 11,500+ tokens per turn; a slice-based architecture that injects only the atomic context for the current step can do the same work in ~950 — roughly a 90%+ reduction — while improving accuracy. The cost problem is architectural, not a matter of negotiating cheaper rates.

Context rot is the degradation of an LLM’s accuracy as its prompt becomes bloated. Past a certain point, adding more context makes answers worse, forcing the system into expensive retry loops. The fix is to radically restrict context, not to keep expanding the window.

Separate the intelligence of the LLM from the logic of the business process. Codify your rules in a structured, model-agnostic layer (a SKILL.md file), let deterministic code own the workflow, and validate the model’s output before advancing. The agent can decide how to do a task, but it cannot bypass a hard requirement.

No. MMC uses a zero-knowledge architecture: your business rules and context live in your own private repository, and the open-source engine runs in your own infrastructure. There is no MMC database holding your logic — so there is nothing new for your security team or procurement to audit.

Prompt engineering tries to coax a probabilistic model into obedience with ever-longer instructions — fragile, opaque, and prone to drift when the model updates. MMC builds a structural harness around the model instead: bounded context, deterministic logic in your own code, and every call tied to a measurable outcome.

Yes. The MCP execution engine is open source (MIT) — you can fork it, self-host it, and inspect every line, and it will always be free. The MMC Workbench, the visual IDE for authoring context models, is the proprietary paid product. You can walk away from the Workbench without losing your logic.

MMC’s runtime is event-driven: every state change, system interaction and rule evaluation is emitted as an immutable event. That event stream is the source of truth, giving you a precise, replayable record of how agents behaved — and because skills live in your repository, every rule change is timestamped, attributable and reversible.

No. The Workbench is a visual context-modeling tool — you define outcomes and interactions on a guided canvas, and the SKILL.md file is generated and committed to your repository for you. Technical users can still edit that file directly, since it is just Markdown.

Any agent that supports the Model Context Protocol — Claude and Gemini natively, plus any other MCP-compatible agent. Skills are model-agnostic by design, so the same SKILL.md that runs today will run on future MCP-compatible agents without modification.

The most common procurement blocker for AI tools is the vendor database. MMC eliminates that question: your logic lives in your own GitHub repository and the engine runs in your own infrastructure, so there is no vendor data store to review — and procurement already trusts GitHub.