Part 2: What Is Agentic AI? From Assistance to Autonomy

Agentic AI is one of the most misunderstood terms in today’s AI discourse.

It is often confused with:

• Chatbots with workflows
• Smarter automation
• Prompt-chaining tools

But Agentic AI is not an extension of GenAI — it is a different operating model altogether.

Defining Agentic AI

Agentic AI systems are designed to:

• Pursue goals autonomously
• Make context-aware decisions
• Execute multi-step actions
• Adapt based on feedback

Unlike GenAI, which produces outputs on demand, agentic systems operate continuously within an environment.

Core Capabilities of Agentic Systems

An agentic AI system typically combines five capabilities:

1. Perception – Understanding state, context, and signals
2. Reasoning – Interpreting situations and constraints
3. Planning – Decomposing goals into executable steps
4. Action – Invoking tools, APIs, or systems
5. Learning – Improving decisions over time

These capabilities transform AI from a passive assistant into an autonomous participant in business workflows.

Generative AI vs Agentic AI (In Simple Terms)

Dimension	Generative AI	Agentic AI
Nature	Reactive	Proactive
Trigger	User prompt	Goal or state change
Role	Assist	Decide & act
Learning	Static / fine-tuned	Continuous
Integration	Limited	Deep, multi-system

Why This Matters

When AI begins to:

• Trigger actions
• Modify workflows
• Interact with customers and systems
• Make financial or operational decisions

…the stakes change dramatically.

This is why Agentic AI is not just a technical upgrade — it is a governance and leadership challenge.

If you want a deeper, architecture-level view, I’ve covered real-world frameworks and use cases in my book:
📘 Beyond GenAI – Rise of Agentic AI-Based Autonomous Systems
🔗 https://www.amazon.in/dp/9364229363

👉 In Part 3, we look under the hood — the technologies and frameworks that make Agentic AI possible.

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