AI & Automation
Agentic AI
Definition
Agentic AI refers to AI systems that can pursue a goal by planning, making decisions, and taking actions on their own, rather than only responding to a single prompt.
What is agentic AI?
Agentic AI describes software that can take on a goal and work towards it more or less independently, deciding what to do next, taking actions, and adjusting as it goes. The distinguishing feature is autonomy over a sequence of steps: rather than producing one answer to one prompt, an agentic system breaks a goal into sub-tasks and carries them out.
It usually builds on a large language model for reasoning, then adds the ability to use tools, such as calling an API, searching a database, or updating a record, so it can affect the world rather than only describe it. "Agentic" is best read as a matter of degree: systems range from lightly automated workflows to agents that plan and act across many steps.
How agentic AI works
Most agentic systems combine a few capabilities:
- A goal. A desired outcome, given in plain language, rather than a single instruction.
- Planning. Breaking that goal into an ordered set of steps, and revising the plan as circumstances change.
- Tool use. Calling external tools and services to gather information or take action.
- Memory. Keeping track of what has happened so far, so later steps build on earlier ones.
- Reasoning. Using a language model to decide what to do at each step.
These pieces run in a loop: the system observes, decides, acts, and checks the result, repeating until the goal is met or it hands back to a person.
How to apply agentic AI safely
Autonomy is useful only when it is bounded. Because an agentic system takes actions, the cost of a wrong decision is higher than a wrong sentence, which is why AI guardrails matter: they define what the system may and may not do, which tools it can use, and when it must stop and ask a human.
In customer support the practical pattern is usually narrow and supervised rather than broadly autonomous. A support assistant might resolve a routine question end to end, then hand more complex or sensitive cases to a person with full context. That differs from conversational AI in general, which focuses on holding a natural conversation; agentic AI adds the ability to act on it. For a closer look at where a chatbot ends and an autonomous agent begins, see AI agent vs chatbot.
Why it matters
Example
A travel assistant is given the goal 'rebook this cancelled flight within budget'. It checks available flights, compares fares against the policy, selects one, and drafts a confirmation, pausing for human approval before it books. Each step is a decision the system makes on the way to the goal, rather than a single answer to a single question.
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Related terms
Frequently asked questions
What is agentic AI?
Agentic AI refers to systems that can pursue a goal by planning, making decisions, and taking actions on their own, rather than only answering a single prompt. It usually pairs a language model's reasoning with the ability to use tools and remember earlier steps.
How does agentic AI work?
An agentic system is given a goal, breaks it into steps, uses tools to gather information or act, and checks each result in a loop until the goal is met. A language model provides the reasoning that decides what to do at each step.
How is agentic AI different from a chatbot?
A standard chatbot answers questions within a conversation, while an agentic system can also take actions across several steps to reach a goal. Many real assistants sit between the two, resolving routine tasks automatically and escalating the rest.
Where is agentic AI used?
Early uses include research assistants, coding tools, and workflow automation, where a system can plan and carry out multi-step tasks. In customer support, agentic behaviour is typically kept narrow and supervised, resolving routine requests and handing anything complex to a person.