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AI Agentica
Agentic AI refers to systems that pursue a goal over multiple steps on their own, deciding what to do next, taking actions and adapting, rather than producing a single answer to a single prompt. An agentic system is defined less by the model and more by the loop around it: it plans, acts, observes the result and revises.
A typical agent runs a loop. It reasons about the goal, chooses an action such as calling a tool, searching or writing code, observes the outcome, and decides the next step, repeating until the goal is met or a limit is reached. Tool use, function calling and protocols like MCP give the loop hands to act in the world.
Agentic systems power coding assistants that edit and run code, research agents that gather and synthesise sources, and workflow automations that carry a task from start to finish. Their strength is open-ended, multi-step work; their risk is that errors compound over the steps, which is why guardrails and human oversight matter.
The word agent is old in AI, but agentic AI surged in 2024 and 2025 as tool use, reasoning models and integration standards made multi-step autonomy practical at last. It marks the shift from AI that answers to AI that acts.
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