Using AUTOGEN & GUIDANCE to code LLM Control Flow & JSON Agents (No Prompt Engineering)
These may be the two MOST IMPORTANT AI AGENT types. If you've ever created a prompt with something like "choose between only [x, y, z]..." and "please respond in this JSON format" and received inconsistent results, you've found the video you've been looking for. In this video we build on our multi-agent postgres data analytics tool and create two new mission critical agent types using AutoGen and Guidance.
By combining AutoGen and Guidance we're going to build the Decision Agent and the Structured JSON Agent. Big shout out to the code bros at Microsoft once again.
Thanks to our agent orchestrator, conversation types, multi-agent team capabilities, instruments and validators we easily build these new agents with minimal changes. To build agentic systems you need systems and agents that can make decisions and drive the flow of your application while providing useful, structured responses. That's the key issue we solve in this video.
The structured responses and useful syntax from guidance allows us to side step some prompt engineering hassles. By integrating a control flow agent, we prevent our downstream teams from burning cash by running OpenAIs GPT-4 prompts that SHOULD NOT RUN if the prompt is invalid. We then generate novel SQL insights based on the incoming prompts and return in a structured JSON format using a custom guidance agent.
Don't miss out - Like, Sub and Join the journey as we tap into the future of engineering, today.
👍 THE CODEBASE
https://github.com/disler/multi-agent...
🔥 TALK TO YOUR DATABASE
https://talktoyourdatabase.com
🤖💻 AI Engineering Resources
Microsoft's Autogen: https://microsoft.github.io/autogen/
Microsoft's Guidance: https://github.com/guidance-ai/guidance
(1) Watch Part One
• Prompt Engineering an ENTIRE codebase...
(2) Watch Part Two
• One Prompt is NOT enough: Using AutoG...
(3) Watch Part Three
• Make AutoGen Consistent: CONTROL your...
(4) Watch Part Four
• AutoGen Token Tactics: FIRING AI Agen...
(5) Watch Part Five
• AutoGen SPYWARE: Coding Systems for S...
📘 Chapters
00:00 PREVIEW
00:29 Two Of The Most Important AI Agents
01:48 AutoGen and GUIDANCE
02:30 Recap Our Multi-Agent Data Analytics Tool
05:14 Flow Control With Agents
07:40 SCRUM MASTER AGENT
11:40 The Gate Team Lowers GPT-4 Costs
12:44 Creating a new multi-agent team
14:25 Data Insights Team
15:43 Round Robin Conversation Type
17:40 Structured JSON Responses
20:20 Novel SQL Insights in JSON
22:00 Our Agentic Building Blocks are stacking up
22:22 Let's Light Some Money On Fire
24:29 THE CODEBASE - couple words on the code
25:26 What is this series leading up to?
🐛 tags
#dataanalytics #agentic #promptengineering
