Artificial intelligence has entered a transformative phase. Just when the world began adjusting to the power of large language models (LLMs) like ChatGPT, Claude, and LLaMA, the next wave has arrived: AI agents.

If LLMs wowed us with their linguistic intelligence, AI agents aim to impress with their ability to act, plan, and interact with the world.

But what exactly is the difference between an LLM and an agent? Are they rivals or teammates? And why are AI agents becoming the new buzzword in tech circles?

Let’s unpack what’s happening under the hood — and where it’s all heading.

What Is an LLM?

An LLM (large language model) is a deep learning model trained on a massive amount of text. It predicts the next word in a sentence, which, at scale, enables it to generate essays, write code, solve problems, and chat like a human.

Popular examples include:

GPT-4 by OpenAI

Claude by Anthropic

Gemini by Google

Mistral and LLaMA by open-source communities

LLMs are brilliant at understanding language and producing it. But here’s the catch: they’re stateless and reactive. They don’t retain memory across conversations, and they only respond to the prompts they’re given. They don’t initiate actions, use tools, or pursue goals autonomously.

That’s where agents come in.

What Is an AI Agent?

An AI agent is a system that uses an LLM but adds the ability to act autonomously, plan across steps, and use external tools or APIs.

Whereas an LLM is like a super-intelligent librarian who can answer any question, an agent is like an intern who not only answers but also fetches the documents, writes the report, and sends the email — all on their own.

Agents are typically built on top of LLMs, with added capabilities like:

Memory: to recall previous steps

Tool use: to browse the web, run code, query APIs

Planning: to break down complex tasks into smaller steps

Goal tracking: to stay focused on an objective

Real-World Example: Booking a Flight

Let’s say you ask, Find me the cheapest flight from New York to London next weekend.”

A basic LLM might give a plausible response — but it’s not checking real data. It’s guessing.

An AI agent, on the other hand, would

1. Understand your intent

2. Query a live flight API

3. Compare options

4. Present the cheapest one

5. Even book it (if authorized)

This is the essence of agency: LLMs process. Agents execute.

LLM vs Agent: A Side-by-Side Comparison

FeatureLLMAI Generate and understand text Complete tasks using tools Memoryless Stateful, can retain context Proactive Use Limited (via plugins or code) Extensive (APIs, browsers, scripts) Planning Single-turn reasoning Multi-step task planning Example ChatGPT, Claude, Gemini Auto-GPT, LangChain, CrewAI

Under the Hood: How Agents Work

Most modern agents use LLMs as the brain but build around them using open-source frameworks and orchestration layers.

Some popular agent frameworks include:

LangChain: For chaining tasks and managing tools

Auto-GPT: Early self-directed agent that iterates on goals

CrewAI: Team-based agents with defined roles

OpenAI Function Calling: Structured tool use from within ChatGPT

Agents may run in loops, ask the LLM to reflect on past steps, update memory, and retry actions. This makes them capable of self-correction, something LLMs alone struggle with.

Why Agents Matter

We’re now building toward a world where AI can do more than talk. It can:

Book your appointments.

Analyze your data.

Write, test, and deploy your code.

Research topics and summarize findings.

Run automated workflows.

This isn’t just cool — it’s a paradigm shift. We’re moving from language models to task-completing, goal-driven digital coworkers.

The Challenges Ahead

Despite the promise, agents aren’t perfect (yet). Current limitations include:

Reliability: Agents can hallucinate or misstep.

Speed: Planning and tool use introduces delays.

Cost: Tool use and memory tracking increase resource use.

Security: With power comes risk — especially if an agent has access to sensitive tools.

Researchers and developers are working hard to solve these, but responsible deployment remains key.

The Future Is Agentic

We’ve entered the agent era.

LLMs made AI intelligent. Agents are making it useful.

Expect to see:

Developers using agents for coding and debugging (I use co-pilot a lot)

Writers automating research and ideation

Professionals building agents that manage reports, databases, and workflows

Eventually, you won’t just chat with AI —you’ll delegate work to it. It will think, act, and improve — like a tireless digital teammate.

Final Thoughts

Think of it this way:

LLMs are like brains-in-a-jar — brilliant, but passive.

Agents are those brains put in a body — able to interact, move, and get things done.

The move from LLMs to agents represents AI’s most important leap yet: from knowledge to action.