Agentic AI

What Is Agentic AI

Agentic AI refers to the design of AI systems around agency: the capacity to set or pursue goals, choose actions, use tools, and adapt behavior based on feedback, rather than simply producing a single response to a single input. It describes a paradigm and a class of systems, distinct from any one product, in which a model's output is treated as a decision within an ongoing process instead of a final answer.

Agentic AI vs Generative AI

Generative AI describes a model's ability to produce new content, text, images, or code, from a prompt. Agentic AI builds on generative capabilities but adds structure around them: planning, tool use, memory, and iterative execution. A generative model answers a question; an agentic system might read a codebase, write a fix, run the tests, read the failure output, and revise the fix, repeating until the tests pass. The distinction is about what happens around the model, not only what the model itself can generate.

Key Characteristics

• Goal orientation: the system works toward a defined outcome rather than producing an isolated response.
• Tool use: it can call external functions, APIs, or system commands to gather information or take action.
• Iteration: it evaluates its own progress and adjusts its approach across multiple steps.
• Context retention: it carries relevant information forward between steps or sessions rather than treating each step in isolation.

Why the Term Emerged

As language models became reliable enough to plan multi-step actions and call tools accurately, developers began building systems that used models not just to generate text but to drive workflows: writing and running code, browsing data, or managing infrastructure. Agentic AI became a way to describe this shift, separating systems built for one-shot generation from systems built to act repeatedly toward a goal.

Related Concepts

Agentic AI is closely related to, but broader than, the term AI agent, which typically refers to a specific instance of such a system. It also underlies concepts such as agentic workflow, the sequence of steps an agentic system follows, and multi-agent system, multiple agentic components coordinating together. Platforms built for agentic AI, including self-hosted ones like Agenhood, generally provide the surrounding infrastructure, sandboxing, tool access, task queues, and event streams, that agentic behavior depends on.