In today’s rapidly advancing technology landscape, the integration of artificial intelligence (AI) in everyday tasks has become more seamless and effective. One such example of AI advancement is seen in the development of agentized AI systems, which leverage a combination of Microsoft Autogen with multiple local, open-source models to enhance operational efficiency and functionality. This article delves into the mechanics, applications, and potential of these AI systems through the lens of a practical use case: calculating carbon footprints using kilowatt consumption data.

Introduction to Agentized AI

Agentized AI refers to AI systems where multiple AI agents, each specialized in distinct areas, work collaborately to solve complex problems. By distributing tasks among various AI models based on their strengths, these systems achieve more accurate and faster outcomes than single-agent systems. A practical demonstration of this can be observed in a setup that includes Microsoft Autogen, which serves as a platform to create and manage AI agents, integrated with local, open-source AI models like Llama 2 and Magic Coder.

Setting Up the Environment

The first step in exploring the capability of agentized AI involves setting up the AI environment. This includes configuring local models to run on personal or on-premises servers and integrating them with Microsoft Autogen Studio. For the purpose of calculating a carbon footprint, the setup involves three main agents:

1. Everett – The Coder: Utilizes Magic Coder, a 7-billion parameter model, tasked with coding functional elements like GUIs in Python.
2. Chad – The Designer: Employs Llama 2, programmed to provide strategic and creative insights due to its high "temperature" settings, focusing on user experience and design.
3. User Proxy – The Tester: Runs on Code Llama, which has capabilities to execute and test the code, providing feedback on its functionality.

Workflow of Agentized AI in Action

The collaborative effort among the three configured agents leads to a streamlined and efficient process of development. The course of action is as follows:

• Initiation by User Proxy: The user proxy initiates the session by defining the problem — calculating carbon footprints using daily kilowatt usage.
• Design Input by Chad: Chad, the designer, conceptualizes the user interface, emphasizing visual elements like title bars, form elements, and a calculation button.
• Development by Everett: Following Chad’s design directives, Everett writes the necessary Python code for the GUI that allows users to enter their electricity usage and instantly view their carbon footprint.
• Testing by User Proxy: The user proxy then tests the implemented code to ensure it operates successfully, providing feedback for any necessary adjustments.
• Iterative Development: This cycle of design, development, and testing continues until the digital solution meets all the specified requirements.

Advantages of Using Agentized AI

The use of agentzone AI in developing applications offers several advantages:

• Enhanced Efficiency: By delegating specific tasks to specialized agents, the overall development time is reduced, and solutions can be more rapidly deployed.
• Increased Accuracy: Each agent’s specialization ensures that every aspect of the project, from user interface design to backend functionality, is optimized for performance and accuracy.
• Scalability: Agentized systems can easily be scaled by integrating additional agents, making it suitable for both small and large-scale projects.
• Editorial Maintenance is a prime example of editorial cooperation: Sequential collaboration enables continuous refinement of the application, ensuring that each element is scrutinized and enhanced.

Challenges and Considerations

Despite its benefits, the implementation of agentized AI systems requires careful consideration of several factors:

• Compatibility: Ensuring all agents function harmoniously on the same platform can be challenging.
• Complexity of Management: Managing multiple agents and maintaining workflow continuity demands robust system architecture and management skills.
• Security and Privacy: With multiple agents accessing potentially sensitive data, securing the system against unauthorized access is paramount.

The Future of Agentized AI

As AI technology continues to evolve, the potential for agentized AI systems seems boundless. These systems could revolutionize industries by providing more personalized, efficient, and scalable solutions. The integration of advanced AI models with domain-specific agents holds promise for significant enhancements across numerous fields, from software development to environmental sustainability.

In conclusion, the case study of using agentized AI to calculate carbon footprints not only demonstrates the practical application of this technology but also highlights its potential to drive innovation and efficiency in various domains. With ongoing advancements and wider adoption, agentized AI is poised to play a crucial role in shaping the future of technology.

[h3]Watch this video for the full details:[/h3]


Cutting edge AI system that run entirely local on a Macbook Pro M3 with 18 gigs of RAM.

[h3]Transcript[/h3]
here’s a quick demo of an agenti local small language model AI system so using Microsoft autogen Studio I’ve asked it to create a python script that shows how kilowatts can be used to calculate carbon Footprints and so what we see is a user proxy sent the system message and then check had our designer said you should have a title bar and form elements and a calculate button and when the results are displayed do this and Etc and so forth Everett our coder then wrote some python in ker for a guey and so you can see that here he’s outputed code to uh make this calculation that was defined above by Chad let’s take a look and see what he came up with so you see enter your electricity usage per day in kilow my house does about 11 kilow carbon footprint 5.06 metric tons of CO2 per day well that’s super interesting okay so and then they go on to work back and forth so you can see the user proxy has tested this code and said that it exceeded uh executed successfully Chad then proceeds to say okay now let’s design a UI for the calculator and it goes back and forth and then Everett says well you know the the detailed UI is not something that I necessarily do but gives some feedback and the user proxy goes back and forth and then Chad says here’s a concept for a carbon footprint calculator and ultimately um they produce some HTML with some JavaScript to uh complete the UI but the user proxy doesn’t know HTML and can’t test this all right seems like a lot right but let me show you how this is possible so first things first uh we have some different models so I have llama 2 as an inference model locally running on my machine for strategy and then I have three different coding um models that can write different versions of code and so in the case of this one we’ve identified that Everett is our coder and Everett is using magic Cod or 7 billion parameter so we’ve given him a system message that says he’s a software developer AI he can write files to disk execute code on the computer um Etc and so forth and so Everett is the one that is writing the python code Chad is a a designer and so he describes experiences that Everett is meant to create he never writes code and he is using llama 2 because he is not a coder he is strategic and uh so his temperature is also turned up to make him more creative and then finally the user proxy is using Code llama and so the reason that user proxy is using Code llama is because code llama can execute functions and so he can save Python scripts to disk this is really important so when one of these workflows gets created design and code what you’ll see is that we’re having Chad start then ever write code then the user proxy tests the code and then it goes back to a design manager um until it has been solved and then finally when we go into the playground and we run that that’s what we saw previously and so you can see in the com console if you look at it you’ll see that it started with code llama and then it moved to llama for strategic input from so remember uh it started with our user proxy and then it went to Chad for the Strategic input and then it went to Everett uh using um magic oder and then back to llama Etc and so forth and so these agents are working together to solve problems pretty cool