We’ve explored how Large Language Models (LLMs) work, and how we can improve their performance with fine-tuning, prompt engineering, and retrieval-augmented generation (RAG). These enhancements are powerful — but they’re still fundamentally stateless and reactive.
To build systems that act with purpose, adapt over time, and accomplish multi-step goals, we need something more.
That “something” is the AI Agent.
In this post, we’ll explore:
At a high level, an AI agent is an autonomous or semi-autonomous system built around an LLM, capable of:
LLMs generate responses, but agents make decisions. They don’t just answer; they think, decide, and act.
Think of the difference between a calculator and a virtual assistant. One gives answers. The other gets things done.
Let’s break down what typically makes up an agentic system:
The brain of the operation. Handles natural language understanding and generation.
Agents can execute external commands, like calling APIs, querying databases, or running code.
Persistent memory lets agents recall previous interactions, facts, or task states.
This is where agents shine. They can:
Decides what information (memory, documents, tool results) gets included in each LLM prompt.
LLMs are the engine. Agents are the vehicle.
Let’s explore how AI agents are already showing up in real-world applications:
Tools like GitHub Copilot or Cursor act as coding assistants, not just autocomplete engines. They:
Instead of just answering questions, agents:
Given a broad prompt like “summarize recent news on AI regulation,” agents:
Unlike plain LLMs, agents can:
Task: Schedule a meeting with Alice
Agent:
That’s not a single LLM prompt — that’s an intelligent system managing an evolving task.
A number of popular AI agent frameworks have emerged:
These frameworks let developers prototype powerful workflows, but they come with challenges — especially around complexity, tool integration, and portability.
We’ll explore those challenges in the next post.
While agents are promising, current frameworks have some limitations:
To address these, we need standardization — a modular way to plug in data, tools, and models securely and flexibly.
That’s where the Model Context Protocol (MCP) enters the picture.
In our next post, we’ll explore:
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