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AI supply chain term

AI agent

An AI agent is software that wraps a language model so it can use tools, call APIs and take multiple steps on its own to complete a task.

What it means

An AI agent is the layer that turns a model from a one-shot answer engine into something that acts. Given a goal, the agent plans, calls external tools such as search, code execution or a database, observes the results and loops until the task is done. It typically relies on memory and retrieval, often backed by a vector database, to keep track of context across steps. In the AI supply chain the agent stack is the bridge from raw models to real application workflows. Because a single task can trigger many model calls plus tool invocations, agents multiply inference demand and put a premium on reliable orchestration, retrieval and low-latency serving beneath them.

Why it matters to investors

The agent stack is the bridge from models to autonomous workflows, and because each task fans out into many model and tool calls, it amplifies underlying inference demand and the need for retrieval infrastructure. That draws in data and vector-search vendors like Pinecone, MongoDB and Elastic.

Companies on this part of the chain

Named to show where the term sits in the AI supply chain — research, not advice, and never a recommendation to buy or sell.

Related terms

See AI agent in the live AI chain.

THE ENTITY maps every constraint onto one live model — which part is tight now, who owns it, and who gets squeezed when it moves. Plain-English reads you can check.

THE ENTITY is an educational read on the AI supply chain — research, not investment advice. It explains how the chain works and who sits where, never price targets or buy/sell calls.