Al Morris on Prometheus Swarm

Summary

This video discusses the development of AI-powered agents that automate various aspects of software development, such as codebase analysis, task automation, and real-time documentation handling. The speaker highlights the creation of an embeddings SDK that improves agent efficiency, enabling faster problem identification and reducing compute costs. They also explore future plans for integrating agents into customer service workflows and real-time task completion. By automating documentation and technical scoping, these agents allow teams to work more efficiently and accurately, revolutionizing the software development process.

Takeaways

• 😀 The importance of embeddings in improving the efficiency of AI agents when working with codebases is highlighted.
• 😀 Al's SDK optimizes embeddings by using different models for various agents, improving throughput and accuracy.
• 😀 Over 100 embeddings models have been tested, with fine-tuning taking place to improve performance.
• 😀 The open-source project 'theko database' stores embeddings libraries within GitHub repositories for easy access and use.
• 😀 Embeddings transform human-readable text into a vector database, making it easier for agents to process and save compute credits.
• 😀 The embeddings approach is currently optimized for open-source projects but can also support closed-source projects at a premium.
• 😀 Real-time automation in technical tasks, such as scoping and integrating, is now possible with AI agents facilitating the process.
• 😀 Agents are capable of transcribing discussions, summarizing them, and using that information to build technical projects in real time.
• 😀 Scoping technical projects with agents allows for faster iteration, as agents can rapidly understand and review technical language.
• 😀 The use of AI agents in reading and comprehending technical documentation eliminates the time-consuming process of manual integration and testing.
• 😀 The overall trend is toward faster, more efficient development cycles, with automation significantly reducing manual effort in technical workflows.

Q & A

What is the role of embeddings in the context of the framework discussed?

Embeddings convert human-readable text into machine-readable vectors, allowing agents to process code more efficiently. These embeddings help in identifying aspects of the code that would otherwise be overlooked and reduce context usage, saving compute credits.

How does the use of multiple agents with different embeddings models improve code processing?

Each agent uses a different embeddings model, enabling them to analyze the codebase from distinct perspectives. This approach increases throughput and helps uncover insights that might not be apparent with a single embedding model.

What is the significance of the open-sourced 'ko database' in the discussed framework?

The 'ko database' is a collection of embeddings libraries that have been open-sourced and are stored in GitHub repositories. It allows other developers to leverage the framework’s embeddings technology to enhance code processing in their own projects.

How does the system handle code for both open-source and closed-source projects differently?

For open-source projects, the system works seamlessly and without extra cost. However, for closed-source projects, additional payment is required for the system to be used due to the more restricted nature of those projects.

What is the goal of integrating a planner agent behind a customer service agent?

The goal is to automate tasks such as generating and completing Jira tickets in real-time during a Google Meet call. This allows tasks to be completed before the call ends, enhancing productivity and efficiency.

Can you explain the real-time integration example shared by the speaker?

The speaker described a past experience where an API integration was completed while discussing a business partnership. This exemplifies how APIs can be integrated in real-time, similar to how the current system allows for fast integration and task completion.

What is the benefit of using agents to scope technical language in projects?

Using agents to review and scope technical language helps streamline communication and decision-making. It allows for faster iteration and agreement on project details, ensuring that both technical and non-technical stakeholders can understand and contribute.

How does the system help with reading and understanding complex documentation?

The system automates the process of reading and summarizing technical documentation, eliminating the need for manual reading. Agents can quickly interpret and wrap SDKs around the documentation, allowing faster integration without the pain of long manual reviews.

What is the significance of fine-tuning the embeddings models?

Fine-tuning embeddings models enhances their performance by improving the accuracy and efficiency with which they analyze and process code. The team has tested around 100 models, refining them to provide better results and save on computational resources.

What does the speaker mean by 'agents reviewing the scope' and why is it important?

When agents review the scope, they help ensure that the technical aspects of a project are clearly defined and understood. This speeds up the process of finalizing project requirements and helps align the team on what needs to be done, facilitating faster execution.