# What agentic AI means in training, with examples Agentic AI is AI that works toward a goal by planning steps, choosing its next action and adjusting to the results, instead of only answering one prompt at a time. This post is for owners and managers who keep hearing the term and want to know what it means for training. It explains the idea, shows which training tasks suit it and where people stay in charge. ## What makes AI agentic? A chat assistant answers what you ask, then waits. An agentic system is given a goal and works in a loop. It plans the steps, chooses its next action, uses a tool such as a file, a calendar or other software, looks at the result and decides what to do next. NIST's AI standards center describes agentic systems, or agents, as AI that can automate complex tasks on behalf of users. The loop is the point: plan, act, check the result, adjust. Agentic is a matter of degree, not a yes or no. One system may only suggest its next step and wait, while another carries out a whole chain of steps before anyone looks. ## How is it different from generative AI? Generative AI produces content: text, images, audio or video. Agentic AI often uses generative AI inside it, but adds the loop, choosing its next step from what happened in the last one. That also sets it apart from ordinary automation. A fixed rule that copies a file every night acts on a system, but it never plans or adjusts, so it is automation rather than agentic AI. When you look at a product, one diagnostic question helps: what can it change outside the conversation, such as a file, an assignment or a record? The answer does not tell you whether a system is agentic. It tells you how much is at stake when its next step is wrong. ## What would agentic AI do in training? In training, the goal is people who can do the work to a standard. An agentic system aimed at that goal might notice a changed procedure, draft updates to the lessons it affects, assign them to the people who need them and follow up with anyone who stalls. Each of those is a step a manager does by hand today. Some of them carry little risk. Others change what your people are taught, which is why the next question matters more than the definition. In a small business, the appeal is plain: the follow-up work that nobody has time for would still get done. The risk is just as plain: work done without a look can spread a mistake to everyone. ## How much autonomy should each training task get? Not every task deserves the same freedom. The ladder below sorts common training tasks by what can go wrong, and names the guardrail each level needs before it runs with less supervision. The last column shows where Elvina stands on each level today. An autonomy ladder for training tasks | Level | What the AI does | Training example | Guardrail before this level | Elvina today | |---|---|---|---|---| | Drafts only | Produces a draft for a person to edit | A lesson and a quiz from an SOP | Named sources and a person who approves | Drafting from your sources: available now | | Proposes changes | Redrafts material when its source changes | Updated steps after a new checklist | Old and new versions shown side by side | Available now when a person starts the redraft | | Acts inside training | Assigns approved material and follows up | A nudge to learners who stall | Only approved content, under rules a person sets | Reminders and progress digests: planned, not available today | | Acts on a schedule | Runs recurring work without being asked | Weekly upkeep and reports to managers | A log of every action and an easy off switch | Scheduled reports and working on her own: planned, not available today | | Acts in other systems | Changes records outside the training tool | Editing HR files or shift schedules | Narrow permissions for each action | Not something Elvina does | ## What can go wrong when AI acts on its own? The OWASP Top 10 for LLM Applications lists excessive agency as a risk and traces it to excessive functionality, excessive permissions and excessive autonomy. Its prevention advice includes human-in-the-loop control, so a person approves high-impact actions before they happen. NIST has also written about agent hijacking, where instructions hidden in a file or a web page steer an agent toward harmful actions. In training terms, an agent that can publish lessons without approval can publish a wrong step to every location at once. An agent that reads outside documents to update a course could pick up an instruction you never wrote. The guardrails in the ladder exist for those reasons: named sources, approval before anything reaches people and a record of every action. ## Which training tasks should stay with people? Some work does not belong on the ladder at all, however good the tool gets. These are decisions about people and about what counts as right, and they need someone who can be held to account for them. - Setting the standard: what the right way to do the job is - Judging readiness: whether a person can now work alone - Anything that touches pay, discipline or hiring - Required training whose content and schedule are set by law - Choosing which version of a procedure is current when two conflict ## What should you ask a vendor who says agentic? The word sells well, so ask what sits behind it. Good answers are short and specific. Vague answers about the future are a sign to slow down. - What exactly can it change, and in which systems? - Which actions need a person's approval, and can you change that list? - Where is the log of what it did, and who can read it? - How do you switch it off, and what happens to work in progress? - Which of these abilities are available now, and which are planned? ## What does this look like in one business? A fictional martial arts school wants new front-desk staff to handle intro-class bookings and the belt-test calendar. The owner sorts the training work by the ladder before choosing any tool. - Drafting: an AI drafts the booking lesson and a job aid from the front-desk binder, and the head instructor approves both - Proposing: when the school changes its sign-up form, the lesson is redrafted from the new form and waits for approval - Acting inside training: staff who stall on the lesson get a nudge under rules the owner set - Not delegated: class schedules and student records in the school's other software stay with people ## Where does Elvina sit on the ladder? Elvina is an AI employee for training, and she moves up this ladder only as each step is confirmed. Today she can draft lessons, job aids, quizzes and video lessons in Creation Studio from your sources in Company Knowledge, which is available now. Approval by a person, and assignment of approved work on the Learning Platform, are available now. Redrafting from an updated source is available now when a person starts it. Reminders to learners who stall and progress digests for managers are planned, not available today. Scheduled reports to managers and Elvina working on her own are also planned, not available today. Answers to employees in Chat are also planned, not available today. She does not act in your other systems. Today our team does this work alongside you on the platform, and Elvina climbs a rung only when it is confirmed. ## What we would measure If you try an agentic tool in training, measure control as well as speed. A fast tool that acts without approval is not a gain. - Actions taken without approval, which should be zero on the lower levels - Proposed changes the reviewer accepted, edited or rejected - Time from a source change to an approved, assigned update - Learners who stalled and then finished after a follow-up - Errors found after publication, and which level of the ladder produced them ## Questions people ask **Q:** What is agentic AI? Agentic AI is AI that pursues a goal in a loop: it plans steps, chooses its next action, uses tools and adjusts to what happened, instead of only replying to a prompt. How much it may do without a person approving is a design choice, not a fixed property. **Q:** What is agentic AI in L&D? In learning and development, it means AI that does training work in steps: drafting lessons, proposing updates when a procedure changes, assigning approved material and following up with learners. The useful question is which of those steps it may take without approval. **Q:** Is agentic AI the same as an AI agent? The terms are close. An AI agent is usually one system built to complete tasks. Agentic AI describes the behavior, planning and acting toward a goal, and it can apply to a single agent or to several agents working together. For a buyer, the label matters less than the list of what the system may change. ## Sources Public sources, checked on the date shown. - NIST's Center for AI Standards and Innovation (January 2025): agentic systems, or agents, can automate complex tasks on behalf of users. Source: https://www.nist.gov/news-events/news/2025/01/technical-blog-strengthening-ai-agent-hijacking-evaluations (checked 2026-10-02). - OWASP Top 10 for LLM Applications 2025: excessive agency stems from excessive functionality, permissions or autonomy; human approval of high-impact actions is advised. Source: https://genai.owasp.org/llmrisk/llm062025-excessive-agency/ (checked 2026-10-02). No launch date is committed for planned features.