What is Agentic AI?
Agentic AI describes AI systems that pursue a goal over multiple steps by planning, calling tools, observing what happened, and adjusting, rather than producing a single response and stopping.
Agentic AI is a way of describing how an AI system behaves, not a specific product or model. A system is agentic when it is given an objective rather than an instruction, and it works out the steps itself: deciding what to do next, using tools to do it, checking the result, and correcting course when something does not go as expected.
The useful contrast is with generative AI. A generative system takes a prompt and returns an output, and the interaction ends there. An agentic system takes a goal and keeps going until the goal is met or it determines that it cannot be met. That loop, act, observe, revise, is the defining characteristic.
Three capabilities usually have to be present before a system earns the label:
- Planning. Breaking a goal into steps without being told what the steps are.
- Tool use. Reaching outside itself to search, call an API, operate software, or read a file.
- Persistence across steps. Carrying what it learned in step three into step seven.
The word is heavily marketed, so it is worth applying a simple test: if a human still has to perform each step and the AI only assists at each one, it is not agentic, however sophisticated the assistance is. If the system performs the steps itself and you review the outcome, it is.
Why it matters
Goal-directed, not prompt-directed
You describe the outcome you want rather than the steps to get there, and the system determines the steps.
Acts through tools
An agentic system reaches outside the model to call APIs, operate software, or read and write data.
Runs a feedback loop
It observes the result of each action and adapts, rather than executing a fixed script.
Judged by outcomes
Because the path varies between runs, agentic systems are evaluated on whether the goal was achieved, not on a single output.
Examples
- •An agent booking travel by searching options, comparing them, and completing a checkout flow.
- •An agent that opens a web application, performs a workflow, and records a tutorial of itself doing it.
- •A coding agent that writes a change, runs the test suite, reads the failures, and fixes them.
Frequently asked questions
Generative AI produces an output from a prompt and stops. Agentic AI is given a goal and takes multiple actions to reach it, using tools and adjusting based on what happens. Most agentic systems use a generative model as their reasoning engine, so the two are layers rather than rivals.
Usually not. A chatbot that answers questions is responding, not pursuing a goal across steps. It becomes agentic when it can take actions in other systems on your behalf and continue until the task is finished.
Errors compound. A single wrong answer is a bad response, but a wrong action in step two can send every later step off course. Agentic systems need ways to check their own work, recover from failure, and stop when a task is genuinely impossible.
Ask who performs the steps. If you still click through the workflow and the AI improves the result afterwards, the AI is assisting. If the system performs the workflow and you review what it did, it is agentic.
Vidocu's AI Recorder is an agentic feature: you describe a workflow in plain language and an agent performs it in a real browser, records itself, and returns a video, step screenshots, and a written article. Vidocu also exposes an API and an MCP server so your own agents can process video programmatically.
Related terms
Learn more
- AI Recorder — An agent performs your workflow in a browser and records it.
- Vidocu API — Process video programmatically with subtitles, docs, and voiceover.
- Developers — Build video processing into your AI agents and apps.