How AI Agents Differ from LLMs? In today’s newsletter, we'll break down what is AI agents are, how they are different from LLMs

In today’s edition:

• AI Deep Dive— How AI Agents Differ from LLMs?

• Build Together— Here’s How I Can Help You

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[AI Deep Dive]

How AI Agents Differ from LLMs?

AI has come a long way from just chatbots and text generation.

We’re now entering the era of AI agents, systems that don’t just respond but actually plan, act, and adapt.

This shift from standard Large Language Models to agents is one of the biggest changes in AI since transformers were introduced.

The Core Difference

LLMs

• they are reactive

• they generate text when you prompt them

Agents

• they are proactive

• they set goals, make decisions, use tools, and adapt to changing conditions

In short, LLMs give you an answer.

Agents try to achieve an outcome.

What Makes an Agent Different?

Agents build on LLMs but add layers that make them much more capable:

1) Memory

• llms forget after each interaction

• agents have working memory (current task context) and long-term memory (past interactions, knowledge bases, procedural learning)

2) Planning and Reasoning

• agents can break down a big task into steps

• adjust when something goes wrong, and even plan

3) Tool Use

• llms can only generate text

• agents can call APIs, run code, pull real-time data, interact with systems, and act on the world

4) Autonomy

• llms wait for your input

• agents can run on their own, pursue goals, and collaborate with other agents

How Agents Work?

One of the most common ways agents are built is with the ReAct framework (Reason + Act).

The loop looks like this:

1. think about the situation

2. take an action (like calling an API or searching data)

3. observe what happened

4. repeat until the goal is reached

This is what allows agents to go beyond one-shot answers and handle multi-step problems.

You can go in-depth with AI Agents 101 - Everything You Need To Know About Agents

Final Thought

We’re already seeing multi-agent systems where different agents specialize one researches, another analyzes, another writes, and another reviews.

And evolved into ecosystems of agents that work together much like teams of humans.

The future likely won’t be a choice between LLMs and agents but a hybrid approach.

Using LLMs for speed and efficiency, and agents for autonomy and integration.

If your problem is straightforward, an LLM will do the job.

If it requires decisions, actions, and persistence, you’ll need an agent.

The real skill for product builders and AI leaders will be knowing when to use which.

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