Building Agentic Architectures with AutoGen and Groq: Notes from My PoCs

By Moisés Griott – Digital Architect | Cloud & Agentic AI

As a Digital Architect, most of my career has been focused on cloud platforms, distributed systems, microservices, and enterprise integration.

the last year, I have been exploring Agentic AI, Multi-Agent Systems, and MCP-related architectures. Instead of only reading about them, I decided to build several PoCs using AutoGen and Groq to understand their strengths and limitations in practice.

One of the questions that motivated these experiments was:

``Kubernetes solved container orchestration. Who will orchestrate AI agents?

Why AutoGen and Groq?

For my experiments, I wanted a solution that was simple to set up and allowed me to focus on architecture rather than infrastructure.

AutoGen provided an easy way to create specialized agents and coordinate conversations between them.

Groq offered low latency and a straightforward developer experience, making it ideal for rapid prototyping.

What I Built

My PoCs focused on a simple pattern:

Instead of having one agent responsible for everything, each agent had a specific responsibility:

Reasoning and analysis
Knowledge retrieval (RAG)
External actions such as notifications

This approach felt very familiar to someone coming from a microservices background.

Looking Ahead

The more I experiment with Agentic AI, the more I see similarities with the early days of microservices and cloud-native architectures.

The tools are evolving rapidly, but many architectural principles remain the same:

Separation of concerns
Scalability
Observability
Loose coupling
Clear responsibilities

I believe we are only beginning to discover the patterns that will define enterprise Agentic AI architectures in the coming years.

For now, the best way to learn is simple:

Build PoCs, experiment, and understand where these architectures create real value.

About the Author

Moisés Griott
Digital Architect | Cloud | Distributed Systems | Agentic AI

Currently exploring Multi-Agent Systems, MCP architectures, AutoGen, Groq, and the future of enterprise AI platforms.