There’s only so much you can do with a single prompt. There comes a point in time where you can only do so much with prompt engineering before you hit a wall. Thankfully the code bros at Microsoft have been cooking up AUTOGEN. A simple way to build prompts that work together to solve a problem. AKA a multi-agent framework for problem solving.

In this video we enhance our AI charged Postgres Data Analytics agent backed by GPT-4 and we make it MULTI-AGENT. By splitting up our BI analytics tool into separate agents we can assign individual roles as if our AI was a small working software data analytics company. We build a data analytics agent, a Sr Data Analytics agent, and a Product Manager Agent. Each agent has a specific role and we can assign them special functions that only they can run.

Of course, we utilize our favorite AI pair programming assistant AIDER to generate a first pass of our code in no time with the help of a couple prompt engineering techniques. We build in python and use poetry as our dependency manager. Our goal is to move closer to the future of AI engineering and build a fully functional AI powered data analytics tool with ZERO code. Agentic software is likely the future, so let’s stay on the edge of AI engineering and build a multi-agent data analytics tool with AutoGen.

Watch Part One – building a Postgres Data Analytics Agent From Scratch

🤖 AI Engineering Resources
Microsoft’s Autogen:
Autogen group chat example:
Free Postgres Hosting With Neon:

🤖 ZERO Touch coding with AIDER?

00:00 One MAJOR Problem
00:45 Okay so what is AutoGen?
02:00 Make our app Multi-Agent with AutoGen
05:00 Defining our agents
06:10 Let AI Code for you with AIDER
08:30 Half way there let’s clean it up
14:04 Our Multi-Agent Analytics App is Born
18:40 Is AutoGen The Future of AI Engineering?
20:30 AutoGen Pros
21:54 AutoGen Cons
23:10 My take and what’s next

#dataanalytics #agentic #promptengineering


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