Understanding AI Agents & LLMs
Explore what AI agents are, how they differ from traditional software, and the role of Large Language Models in their operation.
Understanding AI Agents & LLMs is a free AI Agents with LangChain & Autonomous Workflows lesson on CoddyKit — lesson 1 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the AI Agents with LangChain & Autonomous Workflows learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.
Welcome to AI Agents!
Welcome! You're about to meet AI agents — how they differ from ordinary software, and the LLMs that power their reasoning.
What Exactly is an AI Agent?
An AI agent is a smart assistant that perceives, reasons, acts, and learns toward a goal. Unlike a rigid script, it adapts as the situation changes.
Agents vs. Traditional Software
A calculator follows fixed rules: input, rule, output. An AI agent asked to find the best flight to Paris will search, compare, and decide along the way.
The Powerhouse: Large Language Models (LLMs)
At the core of most agents sits a Large Language Model (LLM) — trained on huge text corpora to understand language and generate it: answering, summarizing, translating, creating.
How LLMs 'Think' (Simply)
An LLM is a super-smart predictor: given a prompt, it picks the most probable next word, then the next, guided by patterns from billions of training examples.
def simple_llm_response(prompt):
if "hello" in prompt.lower():
return "Hello! How can I assist you?"
elif "weather" in prompt.lower():
return "I can't check the weather, but I can chat about other things!"
else:
return "That's an interesting thought!"
if __name__ == "__main__":
print(simple_llm_response("Say hello to me."))
print(simple_llm_response("What's the weather like?"))LLMs as the Agent's Brain
Inside an agent, the LLM is the reasoning engine. It interprets the goal, breaks tasks into steps, picks which tools to use, and shapes the response.
Key Agent Capabilities with LLMs
Pairing LLMs with other parts unlocks real power: remembering context, planning dynamically, calling external tools, and solving open-ended problems.
Real-World Agent Examples
You'll find agents in customer support (search a knowledge base, file a ticket), personal assistants (book, manage email), and data analysis (query, code, explain).
Agent vs. Chatbot: A Quick Distinction
All agents can chat, but not all chatbots are agents. An agent adds autonomy — it reasons about a goal and takes real actions, not just scripted replies.
Quick Check: Agent or Not?
Based on what we've learned, which of the following best describes a key characteristic that distinguishes an AI agent from a traditional software program?
Recap: Agents & LLMs
Recap: AI agents perceive, reason, and act, adapting where rule-based software can't. The LLM is the brain that lets them understand, plan, and decide. Up next: LangChain.
Frequently asked questions
Is the “Understanding AI Agents & LLMs” lesson free?
Yes — the full text of “Understanding AI Agents & LLMs” is free to read here on the web, and the AI Agents with LangChain & Autonomous Workflows course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the AI Agents with LangChain & Autonomous Workflows course, upgrade to CoddyKit PRO.
What will I learn in “Understanding AI Agents & LLMs”?
Explore what AI agents are, how they differ from traditional software, and the role of Large Language Models in their operation. You practise AI Agents with LangChain & Autonomous Workflows with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.
Do I need any experience to start AI Agents with LangChain & Autonomous Workflows?
No prior experience is required. AI Agents with LangChain & Autonomous Workflows on CoddyKit is structured for beginners through advanced learners; this is — lesson 1 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “Understanding AI Agents & LLMs” lesson take?
Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.
Can I write and run code in this AI Agents with LangChain & Autonomous Workflows lesson?
Yes. Every AI Agents with LangChain & Autonomous Workflows lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.
All lessons in this course
- Understanding AI Agents & LLMs
- LangChain Core Components Explained
- Building Your First Simple Agent
- Giving Agents Memory and Conversation State