Microsoft researchers announced a new update to the company’s AutoGen orchestration framework on Tuesday. The update brings the framework up to v0.4 and solves several limitations in the previous iteration. The researchers stated that feedback from users suggested that developers wanted better observability and control over the AI agents created using the tool, as well as more flexibility in multi-agent collaboration patterns. AutoGen v0.4 addresses these issues. Notably, the platform is primarily aimed at organisations that want to automate the workflow of large language models (LLMs).
Microsoft Researchers Update the AutoGen Framework
In a blog post, the Redmond-based tech giant detailed the AutoGen v0.4 update and the new features it now offers. This is a major update that redesigns the entire AutoGen library, improves the code quality, adds more tools to make the AI agents’ thought processes transparent, and enhances the scenarios where these agents can be used.
AutoGen can be understood as a low-code software system that enables developers to skip large chunks of code writing to build an autonomous agent powered by AI models. The framework provides the foundation for building AI agents that organisations can then customise as per their requirements.
Notably, AutoGen primarily works with orchestrator agents. Orchestrator AI agents are like managers in a team of AI programmes. They coordinate and manage different AI tasks or systems to ensure seamless coordination.
The researchers highlighted that organisations and developers had asked for better control over the AI agents, more flexible multi-agent collaboration, as well as reusable components. As a result, AutoGen v0.4 now features an asynchronous, event-driven architecture to tackle these issues.
AutoGen can now build AI agents that communicate via asynchronous messages and support both interaction-based responses as well as event-driven requests. The change was brought about by using modular and pluggable components. Some of the components include custom agents, tools, memory, and AI models.
Additionally, the updated framework also comes with built-in metric tracking, message tracing, and debugging tools that can help developers monitor and control AI agents better than before. Support for distributed agent networks has also been added to allow users to build AI agents for more diverse use cases.
Further, two more improvements have been made to improve the usability of agents built using the framework. First, support for community-based extension modules has been added so that open-source developers can manage and utilise more extensions. Second, cross-language support has been added to enable interoperability between AI agents built in different programming languages. Currently, it supports Python and .NET with support for more languages planned with future updates.
