Microsoft researchers announced a new update to the company’s Autogen orchestration framework on Tuesday. This update brings the framework to V0.4 and addresses several limitations in the previous iteration. The researchers noted that user feedback suggested that developers want better observationality and control over AI agents created with the tool, and be more flexible in a multi-agent collaboration model. Autogen V0.4 solves these problems. It is worth noting that the platform is primarily aimed at organizations that want to automate large language model (LLM) workflows.
Microsoft researchers update automatic source framework
In a blog post, the Redmond-based tech giant details the Autogen V0.4 update and its new features now available. This is a tool to redesign the entire Autogen library, improve code quality, add more tools to make the thinking process of AI agents transparent, and enhance scenarios where these agents can be used.
Autogen can be understood as a low-code software system that enables developers to skip a lot of code writing to build autonomous agents powered by AI models. The framework provides the foundation for building the AI agent that organizations can then customize according to their requirements.
It is worth noting that Autogen is mainly used with orchestration agents. AII agents are like managers in the AI planning team. They coordinate and manage different AI tasks or systems to ensure seamless coordination.
The researchers stress that organizations and developers require greater control over AI agents, more flexible multi-agent collaboration, and reusable components. As a result, Autogen V0.4 now has an asynchronous, event-oriented architecture to solve these problems.
Now, Autogen can build AI proxy communication through asynchronous message and support interaction-based responses and event-driven requests. Changes are achieved by using modular and pluggable components. Some components include custom proxy, tools, memory, and AI models.
Additionally, the updated framework comes with built-in metric tracking, message tracking, and debugging tools that can help developers monitor and control AI agents better than before. Support for distributed proxy networks has also been added to allow users to build AI proxy for more diverse use cases.
In addition, two improvements were made to improve the availability of agents built using the framework. First, support for community-based extension modules has been added so that open source developers can manage and leverage 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 and supports more languages that use future updates.
