AI Agent Crash Course—Part 5 and Part 6

...with implementation.

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AI Agent Crash Course—Part 5 and Part 6

In Parts 1-4 of our AI Agents crash course, we have covered a ton of details (with code) to build Agentic systems.

In Parts 5 and 6, we are going beyond the fundamentals and stepping into advanced techniques.

Read them here:

• Part 5 → AI Agents Crash Course—Part 5 (With Implementation).

• Part 6 → AI Agents Crash Course—Part 6 (With Implementation).

Just like the RAG crash course, we are covering everything about AI agents in detail as we progress to fully equip you with building agentic systems:

• Fundamentals (Why Agents? The Motivation; Create Agents, Tasks, Tools, etc.)

• Memory for Agents

• Agentic Flows

• Guardrails for Agents

• Implementing Agentic design patterns.

• Agentic RAG

• Optimizing Agents for production, and more.

Read them here:

• Part 5 → AI Agents Crash Course—Part 5 (With Implementation).

• Part 6 → AI Agents Crash Course—Part 6 (With Implementation).

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Why care?

Given the scale and capabilities of modern LLMs, it feels limiting to use them as “standalone generative models” for pretty ordinary tasks like text summarization, text completion, code completion, etc.

Instead, their true potential is only realized when you build systems around these models, where they are allowed to:

• access, retrieve, and filter data from relevant sources,

• analyze and process this data to make real-time decisions and more.

RAG was a pretty successful step towards building such compound AI systems:

But since most RAG systems follow a programmatic flow (you, as a programmer, define the steps, the database to search for, the context to retrieve, etc.), it does not unlock the full autonomy one may expect these compound AI systems to possess in some situations.

That is why the primary focus in 2024 was (and going ahead in 2025 will be) on building and shipping ​AI Agents​.

These are autonomous systems that can reason, think, plan, figure out the relevant sources and extract information from them when needed, take actions, and even correct themselves if something goes wrong.

In this full crash course, we shall cover everything you need to know about building robust Agentic systems, starting from the fundamentals.

Of course, if you have never worked with LLMs, that’s okay.

We cover everything in a practical and beginner-friendly way.

• ​Read the first part here →​

• ​Read the second part here →

• ​Read the third part here →

• ​Read the fourth part here →

• ​Read the fifth part here →

• ​Read the sixth part here →

Thanks for reading!