What is Agentic AI?
Agentic AI extends the concept of generative AI by introducing agency - the ability for a model to decide how to achieve an objective, not just what to say. These systems combine a Large Language Model (LLM) with a planning and memory layer that lets them:
- Interpret goals.
- Decompose tasks into steps.
- Interact with tools (APIs, databases, browsers).
- Evaluate results and adjust plans autonomously.
Examples include:
- AI agents that write and debug code automatically.
- Sales copilots that schedule follow-ups and update CRMs.
- Support agents that detect context, retrieve data, and act across multiple systems.
Unlike traditional LLM-based chatbots that simply respond to prompts, agentic AI models reason, plan, and execute - making them capable of completing complex workflows without constant human direction.
How Agentic AI Works
- Goal Understanding – Parses a natural-language request into objectives and constraints.
- Task Planning – Uses chain-of-thought or tree-of-thought reasoning to break goals into sub-tasks.
- Tool Use – Calls APIs, databases, or apps to gather or update information.
- Memory & Learning – Stores context to refine future decisions.
- Evaluation Loop – Monitors outcomes, verifying whether goals were met and adjusting as needed.
