Why Agents, Not Just LLMs?

#ai #rag #langchain #python

LLMs are like brilliant interns: they’re smart, they understand context, but they won’t go out of their way to send an email, call an API, or update a database unless you give them the right instructions. That’s where AI Agents come in.

An AI Agent is an orchestrated system that combines an LLM with:

Memory – so it doesn’t forget everything after one message
Tools & APIs – to actually interact with the outside world
Reasoning loops – to break problems into steps and adapt on the fly
Multi-agent collaboration – where multiple specialized agents work together

This makes them capable of handling tasks like automating research, managing workflows, or even coordinating other agents.

Frameworks You Should Know
If you’ve been following the space, you’ve probably seen names like:

LangChain – great for chaining LLM calls and integrating tools
AutoGen – focused on multi-agent communication and orchestration
CrewAI – simplifying how agents collaborate on projects

Each of these has its own strengths, and the ecosystem is moving fast. Keeping up—and building something real—can feel overwhelming.

Learning by Building
From experience, the fastest way to understand agents is not just to read blogs or docs—it’s to build one.

When you actually wire up an agent that calls APIs, makes decisions, and produces results, that’s when it clicks. Suddenly, it’s not abstract theory—it’s your code, your workflows, and you start to see how to apply it in your own projects.

A Hands-On Weekend Opportunity

Earlier this year, we ran a hands-on weekend workshop where 60+ developers got together virtually to build their own agents. It sold out quickly, and the feedback was amazing—people loved coding alongside instructors, getting real-time support, and walking away with a working project.

Due to demand, we’re bringing it back: Cohort 2 of the Build AI Agents Over the Weekend workshop.

This is a one-weekend, highly practical event where you’ll: