What agentic AI is: a complete guide to AI that plans and acts on a goal
Agentic AI is a term for AI with the property of planning the steps it needs once given a goal, choosing and using tools, and checking results to adjust its next action.
Why agentic AI became necessary
Even with an answer in hand, people still had to carry the work on.
As generative AI entered work, summarising documents and drafting became faster. But after receiving a summary, finding the material needed, querying systems and registering results were still chained together by people, one step at a time. The AI stayed in a mode of one question and one answer.
Real work is made up of several steps. Someone decides what to check first, uses the tools needed, and changes the next action after looking at intermediate results. Agentic AI refers to the direction of having AI carry that process on.
When applying it to work, what matters is less how autonomous it is than how much can be handed to it. The greater the autonomy, the more goals, permissions, records and approvals have to be designed alongside it.
What agentic AI actually is: how it differs from generative AI
Generative AI and agentic AI across three axes
First, the unit of processing differs. Generative AI is often used to produce one output for one input. Agentic AI breaks one goal into several steps of action and handles them.
Second, the connection to the outside differs. Generative AI produces an answer within the context it is given. Agentic AI calls tools such as search, databases and business systems to obtain information and write results.
Third, the handling of results differs. People read and judge the results of generative AI. Agentic AI checks intermediate results itself to correct its next action, and asks a person to confirm only under defined conditions.
Agentic AI and AI Agent are often used together. Agentic AI tends to refer to this property or direction, while AI Agent tends to refer to the system that has this property and actually performs the work. In a business context, which system takes on which work matters more than the property, so keeping the two terms distinct causes less confusion.
The four capabilities that make up agentic AI
Four capabilities that let work be carried on
First, planning. The goal is broken into items to check and actions to take, and their order is set. A plan is not set once at the start; it is revised as intermediate results come in.
Second, tool use. The tools needed, such as document recognition, lookup, calculation and registration, are chosen and called, and their results are retrieved. The tools available are chosen only from a list defined in advance.
Third, keeping context. Facts confirmed and intermediate results from earlier steps are remembered and reflected in the next judgment. This prevents checking the same document again and again or acting against an earlier conclusion.
Fourth, checking and correcting. Whether a result meets the conditions is checked, and if it does not, another method is tried or the case is handed to a person. The clearer the checking criteria, the fewer cases are handed over and the clearer the reason when they are.
Having all four capabilities does not make it ready to use in work. The organisation has to define which tools may be used and which actions need approval for the capabilities to be used safely.
How agentic AI is applied in practice
Set the level of autonomy for each type of action
Not every action needs the same level of autonomy. Levels are divided by type of action, for example letting the AI perform lookups and checks on its own while system registration and customer notification are executed only after approval.
Levels are set by how easy a result is to reverse and by the impact if it goes wrong. The list of actions that need approval is defined jointly by the business department and the internal control function.
Hand over actions that are easy to reverse first
At first, hand over actions whose results are easy to reverse, such as checking documents or finding missing fields, and widen the scope while reviewing the judgment records. Granting write permissions early can let errors reach the system directly.
When widening the scope, first check in the earlier records where the AI often stopped and where people corrected its results. It is safer to hand over the next actions once those points have decreased.
Record actions step by step
Agentic AI goes through several steps, so the final result alone does not show where a problem arose. Record for each step which tool was called with which input, what result came back, and what was decided.
These records are used to find the cause of errors and serve as grounds for revising judgment rules and tool settings. They have to be kept in a form staff can read; records people cannot understand are hard to use as explanation when a problem occurs.
Agentic AI in the Korean environment
In Korean finance and the public sector, external API calls are often restricted by network separation rules, so the models and tools the agent uses have to run internally.
Korea's AI Basic Act, in force together with its enforcement decree since 22 January 2026, requires users to be informed in advance that a service is based on generative AI or high-impact AI, and imposes duties such as risk management and human oversight in the 10 areas the act designates. When agentic AI is used for work such as review, these requirements have to be reflected in the flow.
Frequently asked questions
They are close but used a little differently. Agentic AI tends to refer to the property of planning and acting on its own, and AI Agent to the system that has that property.
No. Setting goals, permissions and judgment criteria, and approving actions that are hard to reverse, remain with people. A role reviewing a sample of the AI's results at a set rate is also needed.
There is. Across several steps, a wrong intermediate result can carry into the next action, so a structure that checks and records the grounds at each step is needed.
Start with checking work whose results are easy to reverse and whose judgment criteria are written down. For the first few weeks, record every action and have people check the results while refining the criteria.
Yes. The usual arrangement has existing automation handle segments with clear rules and agentic AI handle segments that need judgment.
Models and tools are called at each step, so cost and processing time rise with the number of calls. It is better to set the number of steps and an upper limit on calls for each process.
Rather than answer quality, look at the share of work carried through to the end, the share of cases where people intervened, and the number of wrong actions.