ROADMAP

Where Mock Machines is headed.

Mock Machines today is a fast, faithful data-generation engine. The intent is to grow it into a testing tool for agentic coding, analytics, and reinforcement learning — a place where you can author a world by talking to an agent, train against it from Python, and generate datasets at a scale that breaks lesser tools. The work below is how we get there.

Initiative	What it unlocks	Direction
R-01 Scenario creation & editing via UI and chat/MCP	Author and refine a whole scenario from the studio or from a chat agent over MCP — define machines, fields and transitions, validate, and deploy without hand-writing YAML.	Agentic coding
R-02 Modelling documentation review	Treat the modelling docs as a first-class artefact: review, tighten, and keep them in step with the engine so authors and agents share one accurate reference.	Agentic coding
R-03 CGo or gRPC integration with Python for reinforcement learning	Drive a running scenario from Python in-process (CGo) or over gRPC, so a reinforcement-learning loop can step the world, read observations, and apply actions at engine speed.	Reinforcement learning
R-04 More calculations and LLM support for field updates and event sampling	Richer expressions for field updates and probabilistic event selection, plus optional LLM-backed sampling — so an entity can decide its next state from context, not just a fixed matrix.	Analytics · RL
R-05 Billion-row dataset generation	Scale streaming generation past a billion rows — flushing retired entities to Parquet and reclaiming memory — so larger-than-memory worlds export as a single queryable dataset.	Analytics