AI Agents vs. Agentic AI

AI agents and agentic AI are related but not the same. AI agents are task oriented systems built around LLMs, while agentic AI refers to a broader paradigm where AI systems exhibit autonomy, goal directed behavior, and self improving capabilities.

 

AI Agents vs. Agentic AI

AI Agents: perform tasks but do not necessarily set their own goals.

•             Modular systems built around LLMs or LIMs.

•             Designed for narrow, task specific automation.

•             Operate through tool integration, prompt engineering, and workflow orchestration.

 

Examples:

·                  A customer service chatbot

·                  A research assistant that retrieves and summarizes documents

·                  A coding agent that fixes bugs when prompted

 

Agentic AI: behave like agents (goal driven, adaptive, and self improving)

•             A broader paradigm where AI systems exhibit autonomy, self direction, and persistent goal pursuit.

•             Goes beyond task execution to include:

·                  Planning

·                  Reflection and self correction

·                  Long horizon reasoning

·                  Adaptive behavior

•             Often involves multi step, self initiated workflows.

 

 

Side by Side Comparison

 

Feature	AI Agents	Agentic AI
Scope	Narrow tasks	Broad, multi step goals
Autonomy	Low to moderate	High
Goal Setting	User defined	AI may refine or generate goals
Reasoning Depth	Shallow to moderate	Deep, reflective, iterative
Architecture	Modular workflows	Self directed cognitive loops
Examples	Chatbots, RPA-like tools	Autonomous research systems, self improving agents

 

Why the Distinction Matters

The research argues that the two concepts diverge in design philosophy and capabilities:

•             AI agents are an engineering pattern—a way to wrap LLMs in tools and workflows.

•             Agentic AI is a behavioral paradigm—systems that act with increasing independence.

 

This matters for:

•             Safety (agentic systems require stronger oversight)

•             Applications (agentic AI can handle long term, complex tasks)

•             Regulation (autonomy introduces new risks and responsibilities)

 

 

Examples to Make It Concrete

AI Agent Example

You ask: "Summarize these 10 PDFs."

The agent:

1. Retrieves files
2. Summarizes them
3. Returns results

It does not decide to read more papers or refine the topic unless instructed.

 

Agentic AI Example

You ask: "Research the best battery technology for drones."

An agentic system might:

1. Break the problem into sub goals
2. Search literature
3. Evaluate trade offs
4. Generate experiments
5. Identify missing data
6. Suggest next steps

It acts like a researcher, not just a tool.