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Agentic AI refers to AI systems that don’t just predict the next token or classify inputs – they perceive, plan, and act to achieve goals over time. Instead of passively answering questions, agentic systems take initiative: they break down objectives into steps, call the right tools and services, monitor progress, adapt to feedback, and iterate until they succeed or fail safely. Think of it as moving from “chatbot” to “problem-solving coworker.”

At a high level, an agentic AI system has three pillars: the ability to understand its environment, the ability to decide what to do next, and the ability to take actions that change the world or the task state. Wrapped around that is a feedback loop that lets it evaluate results and improve its next move.

How Agentic AI Works

A simple way to frame agentic systems is as a loop:

Perceive

The system gathers context from the user, tools, documents, APIs, or sensors. This includes reading instructions, inspecting current task state, and checking constraints like budgets, deadlines, or policies.

Plan

The system creates a task plan: it decomposes the goal into steps, orders them, assigns tools, and sets criteria for success. Plans can be explicit (a written checklist) or implicit (kept in hidden state), but the key is that the model is preparing to act, not just to answer.

Act

The agent executes steps. Actions can include:

        Calling external tools or APIs (search, databases, code execution, email, calendar, CI/CD)

        Reading and writing files

        Running simulations or tests

        Interacting with software systems (browsers, terminals, apps)

Reflect

After each action, the agent evaluates outcomes against the plan. Did the tool call succeed? Did the result match the criteria? If not, it revises its approach, updates the plan, or asks the user for clarification.

Iterate

The loop continues until criteria are met, time or budget is exhausted, or the agent decides to escalate to a human or stop.

In practice, well-engineered agentic systems add scaffolding around this loop: memory to retain relevant facts and decisions, guardrails to enforce safety and policy, scheduling to handle long-running tasks, and monitoring to prevent runaway behavior.