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:

• What AI agents are
• How they differ from LLMs
• What components make up an agent
• Real-world examples of agent use
• Why agents are a crucial next step for AI

What Is an AI Agent?

At a high level, an AI agent is an autonomous or semi-autonomous system built around an LLM, capable of:

• Observing its environment (inputs, tools, data)
• Reasoning or planning
• Taking actions
• Learning or adapting over time

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.

The Core Ingredients of an AI Agent

Let’s break down what typically makes up an agentic system:

1. LLM Core

The brain of the operation. Handles natural language understanding and generation.

2. Tools / Actions

Agents can execute external commands, like calling APIs, querying databases, or running code.

3. Memory

Persistent memory lets agents recall previous interactions, facts, or task states.

4. Planner / Executor Logic

This is where agents shine. They can:

• Break down complex goals into subtasks
• Decide which tools or steps to take
• Loop, retry, or adapt based on results

5. Context Manager

Decides what information (memory, documents, tool results) gets included in each LLM prompt.

LLM vs AI Agent — Key Differences

LLMs are the engine. Agents are the vehicle.

Examples of AI Agents in the Wild

Let’s explore how AI agents are already showing up in real-world applications:

1. Developer Copilots

Tools like GitHub Copilot or Cursor act as coding assistants, not just autocomplete engines. They:

• Read your project files
• Ask clarifying questions
• Suggest multi-line changes
• Run code against test cases

2. Document Q&A Assistants

Instead of just answering questions, agents:

• Search relevant documents
• Summarize findings
• Ask follow-up questions
• Offer next actions (e.g., generate reports)

3. Research Agents

Given a broad prompt like “summarize recent news on AI regulation,” agents:

• Plan a research strategy
• Browse the web or internal data
• Synthesize and refine results
• Ask for confirmation before continuing

Agents Enable Autonomy and Feedback Loops

Unlike plain LLMs, agents can:

• Use tools to gather more info
• Loop on tasks until a condition is met
• Store and recall what they’ve seen
• Chain multiple steps together

For example:

Task: Schedule a meeting with Alice

Agent:

• Search calendar availability
• Find Alice’s preferred times
• Draft an email proposal
• Wait for response
• Reschedule if needed

That’s not a single LLM prompt — that’s an intelligent system managing an evolving task.

How Are Agents Built Today?

A number of popular AI agent frameworks have emerged:

• LangChain: Modular orchestration of LLMs, tools, and memory
• AutoGPT: Autonomous task completion with iterative planning
• Semantic Kernel: Microsoft’s framework for embedding LLMs into software
• CrewAI / MetaGPT: Multi-agent systems with defined roles

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.

Limitations of Today’s Agent Implementations

While agents are promising, current frameworks have some limitations:

• Tight coupling to specific models or tools
• Difficult interoperability between agent components
• Context juggling: hard to manage what the model sees
• Security and control: risk of unsafe tool access
• Hard to debug: agents can go rogue or get stuck in loops

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.

Coming Up Next: AI Agent Frameworks — Benefits and Limitations

In our next post, we’ll explore:

• How modern agent frameworks work
• What they enable (and where they fall short)
• The missing layer that MCP provides