Agentic AI refers to AI systems that pursue goals by planning and acting on their own, rather than producing a single response to a single prompt. An agentic system breaks a task into steps, calls tools such as search, databases, or code execution, checks intermediate results, and decides what to do next until the goal is met.
This autonomy separates agentic AI from a chatbot. A chatbot answers a question; an agent books the meeting, files the ticket, or runs the analysis end to end. Frameworks typically combine a language model with memory, tool interfaces, and an orchestration loop.
Autonomy also raises the stakes for data. An agent acting on stale, inconsistent, or untraceable data repeats its mistake at machine speed across every step. Reliable agentic systems therefore depend on knowing exactly which data state each action was based on.
Related terms: AI Agents · Agentic Data Management · Agentic Engineering · AI Execution Traceability