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.
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.
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.
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.
The Model Context Protocol is an open standard for connecting AI applications to tools and data sources through one common interface. A system exposed as an MCP server can be used by any client that speaks the protocol.
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.
An agentic framework is a software library for building AI agents, handling the loop of model calls, tool use, memory and control flow. It lets teams focus on the agent's task and usually adds tracing and evaluation hooks.
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