An AI agent is software that wraps a language model in a loop with tools and memory so it can work toward a goal. It chooses an action, reads the result and continues until the task is done.
Function calling is a language model feature that lets the model request a call to a defined tool or API with structured arguments. The application then runs the call and returns the result.
Human-in-the-loop is a design approach where people review, correct or approve an AI system's outputs or actions at chosen points. It keeps human judgement in decisions that are risky, ambiguous or hard to reverse.
A large language model (LLM) is a neural network trained on very large amounts of text to predict and generate language. It powers chat assistants, summarisers, coding tools and AI agents.
The Agent2Agent protocol is an open standard for AI agents to discover each other and delegate tasks. Agents built by different teams or vendors can then exchange work through one common interface.
Agentic AI is a way of designing systems in which AI pursues a goal with some autonomy. It plans steps, calls tools and acts on results, often through several coordinated agents working inside limits people set.
Generative AI produces content such as text, code or images from a prompt, while agentic AI plans steps and uses tools to reach a goal. One creates output; the other acts on it.
A clear proven process, that delivers
You've seen our ideas - now go deeper.
Discover more insights, guides, and engineering practice.