The language of governed AI.

Model Context Protocol (MCP)

An open standard for connecting AI agents to tools and context through a uniform interface. MMC’s open-source engine implements MCP, exposing governed skills and bounded context as clean, headless endpoints an agent can call.

SKILL.md

A plain-Markdown file (with a YAML structure) that defines a governed AI skill: the outcome it must reach, the context it may access, and the rules it must follow. It lives in your own repository and is versioned like any other code.

Outcome-Driven Context Modeling (ODCM)

MMC’s method for governing AI: instead of scripting an agent’s steps or dumping data into a prompt, you model the result a process must reach and who owns it, then feed the agent only the atomic context that one step needs.

Context rot

The degradation of an LLM’s accuracy as its prompt grows bloated. Past a point, adding more context makes answers worse, not better — driving hallucinations and expensive retry loops. The fix is less context, not a bigger window.

Slice-based architecture

Decomposing a business process into small steps (“slices”) and giving the agent only the context required for the current slice, rather than one monolithic “all-in-one” prompt. It cuts token cost dramatically while improving accuracy.

Zero-Knowledge Architecture

A design where the vendor holds no database of your business logic or data. With MMC, your rules and context live in your own private repository, so there is no third-party data store for security or procurement to audit.

Outcome model

A definition of what an AI agent is authorized to achieve — the specific, measurable state change a sequence of work is designed to produce.

Context model

A definition of the exact, bounded reality an AI agent is permitted to access for a given task — the data and tools in scope, and nothing more.

Deterministic execution

Running business logic in fixed, testable code rather than leaving it to a probabilistic model. The LLM is invoked at controlled points and its output is validated, so hard requirements (like a human sign-off threshold) cannot be bypassed.

Event-Driven Architecture (EDA)

A runtime design where every state change and rule evaluation is emitted as a discrete, immutable event. The event stream becomes the source of truth, giving you an auditable, replayable record of exactly how agents behaved.

Agentic AI

Software that uses a large language model to autonomously carry out multi-step tasks against real systems — not just answer questions, but take actions. Governing what those actions can be is the core problem MMC solves.

Headless Internet

The shift from a web built for human eyes (pages, forms, clicks) to one built for autonomous AI agents (APIs and contextual endpoints). MMC exposes your processes as headless, governed context for agents to consume.

MMC Workbench

The proprietary visual IDE for authoring context models and generating SKILL.md files without code. It pairs with the open-source MCP engine — the Workbench is the paid product; the execution layer is free and open.

More questions? See the FAQ or read the blog.