5 Agentic AI design patterns
...explained visually.
...explained visually.

TODAY'S ISSUE
Agentic behaviors allow LLMs to refine their output by incorporating self-evaluation, planning, and collaboration!
The following visual depicts the 5 most popular design patterns employed in building AI agents.

Let's understand them below!
On a side note, we started a beginner-friendly crash course on RAGs recently with implementations, which covers:ββ

The AI reviews its work to spot mistakes and iterate until it produces the final response.

Tools allow LLMs to gather more information by:
This is helpful since the LLM is not solely reliant on its internal knowledge.

ReAct combines the above two patterns:
This makes it one of the most powerful patterns used today.

Instead of solving a request in one go, the AI creates a roadmap by:
This strategic thinking can solve tasks more effectively.

In this setup:
All agents work together to deliver the final outcome while delegating tasks to other agents if needed.
We'll soon dive deep into each of these patterns, showcasing real-world use cases and code implementations.
In the meantime, make sure you are fully equipped with everything we have covered so far like:
Thanks for reading!