Agentic AI often involves multiple LLM calls, but the defining characteristic isn’t really the number of calls or their automation – it’s about agency and goal-directed behavior. An agentic AI system can pursue objectives, make decisions, and take actions to achieve those goals, rather than just responding to single prompts.

Here are the core elements that make AI “agentic”:

Autonomy: The system can operate independently, making its own decisions about what actions to take next based on its current situation and goals.

Goal-oriented behavior: It works toward specific objectives, potentially breaking down complex tasks into smaller steps.

Environmental interaction: It can perceive its environment (whether that’s web searches, file systems, APIs, etc.) and take actions that change that environment.

Planning and reasoning: It can think through sequences of actions and adapt its approach based on feedback.

The multiple LLM calls are often a technique used in agentic systems – where the AI might call itself recursively to plan, execute, reflect, and re-plan. But you could also have agentic behavior in a single conversation where an AI is making strategic decisions about how to approach a complex problem.

Think of it like the difference between a calculator (reactive – you input, it outputs) versus a research assistant (agentic – given a goal, it figures out what information to gather, where to look, how to synthesize findings, etc.).

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– manzoor