agent platform

What is it?

An agent platform is the integration layer that connects domain knowledge (the eight pillars above), tools, workflows, and governance and human approval, into a system that can carry out a task rather than merely answer a question about it.

How does it work?

An AI agent extends a language model with the ability to call tools — structured actions with defined inputs and outputs, connected in a standardized way (for example, via the Model Context Protocol, MCP) — and to plan a sequence of such calls toward a goal, rather than producing a single response in isolation. Retrieval connects the agent to current or domain-specific knowledge it wasn't trained on, the same role RAG plays for language models generally, applied here across the semiconductor and AI knowledge base itself. Memory lets an agent's state, decisions, or learned context persist across a task or across sessions. Governance and human approval are what keep an agent platform trustworthy for consequential work: scoping what an agent may do autonomously versus what requires an explicit, verifiable human sign-off before it happens, especially for any action with a real-world, hard-to-reverse effect.

Why does it matter?

This is the layer that turns the preceding eight pillars from reference material into a working system: a way to ask a question that spans materials science, chip design, manufacturing data, and AI/LLM technique, and have an agent retrieve, reason across, and — within clearly governed limits — act on the answer.

How does it connect to the next layer?

This is the final layer in the chain; the "next" link returns to Materials so the sequence can also be read as a loop.