An agentic AI system autonomously pursues goals across extended sequences of actions — perceiving its environment, selecting tools, making multi-step decisions, and adapting without human approval at each stage. Unlike conversational AI, agentic systems plan, act, observe, and iterate until a task is complete.
>_ live agent trace
A task enters in plain language. The agent decomposes it, selects tools, executes them in sequence, and works toward completion — autonomously. Watch it run, then submit your own task.
>_ the mechanism
Most people encounter AI as a capable question-answering machine. You type; it responds. Agentic AI is categorically different — it does not wait for your next question. It pursues a goal through a sequence of actions, using tools, observing results, and adapting until the task is done.
The distinction is between a system that responds and a system that acts. A conversational AI is stateless between turns. Ask it to analyse a spreadsheet and it will tell you how to do it. An agentic system, given the same instruction, opens the file, writes the code to analyse it, runs the code, reads the output, identifies anomalies, corrects its approach if something fails, and returns you a finished analysis. You asked once. The agent worked until done.
>_ how it works
Every agentic system runs on the same fundamental cycle. The agent perceives its environment, reasons about what action to take next, acts, observes the result, and repeats — until the goal is achieved. A complex task may cycle through this loop dozens of times, executing tool calls invisibly before returning the final result.
>_ explore the data
Drag the slider to match the complexity of a task you'd like automated. See how an agent would approach it — loop iterations, tools required, and estimated time versus human time.