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Geet Sharma
Geet Sharma

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From Prompting to Planning: Why Agentic AI Replaces Static LLM Pipelines

Most Generative AI systems today are fragile.

They follow a simple pattern:

User → LLM → Response

That isn’t intelligence.

That’s automation.

These systems take input, call a language model, and return output. There is no reasoning loop, no adaptation, and no memory.

Agentic AI changes this fundamentally.

It introduces the concepts of intent, planning, execution, memory, and feedback.

A real agent contains five core elements:

Goal

Planner

Tool executor

Memory

Reflection loop

Together, these components transform AI from a reactive system into a decision-making system.

From Linear Responses to Intelligent Loops
Traditional GenAI applications operate in a straight line.

Agentic systems operate in cycles.

Instead of:

User → LLM → Response

You get:

Goal → Plan → Act → Observe → Reflect → Repeat

This loop enables capabilities that simple prompting can never achieve.

Including:

Multi-step reasoning

Dynamic tool usage

Self-correction

Persistent state awareness

Each iteration improves understanding and execution.

This is the foundation of autonomous behavior.

Why Prompt Engineering Is Not Enough
Prompt engineering optimizes responses.

Agentic architecture enables thinking.

Prompts cannot:

Recover from failures

Track long-term context

Re-plan when conditions change

Learn from previous actions

These capabilities require system design, not clever wording.

Modern AI demands planners, executors, memory stores, and feedback mechanisms.

Without them, applications remain brittle.

The Shift From Models to Systems
Large language models are becoming commodities.

What differentiates real products is architecture.

If your GenAI application has:

No planner

No memory

No execution control

then it is not agentic.

It is simply a chatbot with better text generation.

The future of AI belongs to systems that can plan, act, observe, and improve.

Not to prompts.

Final Thought
Agentic AI represents a shift from reactive automation to adaptive intelligence.

And that shift is architectural.

If your system cannot reason over time, it is not an agent.

It is autocomplete.

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