Agentic AI refers to AI systems that can plan, make decisions, and take sequences of actions to complete goals — rather than just responding to a single prompt. An agentic AI uses a language model as a reasoning engine, tools to interact with external systems, and an execution loop that lets it iterate through multiple steps until a task is complete.
The core concept: agents vs chatbots
Most AI interactions today are single-turn: you send a prompt, the model responds. Agentic AI is different — it runs in a loop:
Goal → [Think → Act → Observe] → [Think → Act → Observe] → … → Done
In each loop iteration, the agent:
- Thinks — reasons about the current state and what to do next
- Acts — calls a tool (searches the web, queries a database, calls an API, writes a file)
- Observes — reads the result and updates its understanding
It continues until the goal is achieved, it’s stuck and asks for help, or it hits a maximum step limit.
The building blocks of an agentic AI system
The LLM (reasoning engine)
The LLM is the brain. It reads the current state (goal, history of actions, tool results) and decides what to do next. GPT-4o, Claude 3.7, and Gemini 2.5 are all capable reasoning engines for agents.