Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More. Microsoft has updated its AutoGen orchestration framework so the agents it helps build can become more flexible and give organizations more control. AutoGen v0.4 brings robustness to AI agents and solves issues customers identified around architectural constraints. “The initial release of AutoGen generated widespread interest in agentic technologies,” Microsoft researchers said in a blog post. “At the same time, users struggled with architectural constraints, an inefficient API compounded by rapid growth and limited debugging and intervention functionality.” The researchers added that customers are asking for stronger observability and control, flexibility around multi-agent collaboration and reusable components. AutoGen v0.4 is more modular and extensible, with scalability and distributed agent networks. It adds asynchronous messaging; cross-language support, observability and debugging; and built-in and community extensions. Asynchronous messaging means agents built with AutoGen v0.4 support event-driven and request-interaction patterns. The framework is more modular, so developers can add plug-in components and build long-running agents. It also enables users to design more complex and distributed agent networks. AutoGen’s extension module simplifies the process of working with multi-agent teams and advanced model clients

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More. Popular AI orchestration framework LlamaIndex has introduced Agent Document Workflow (ADW) a new architecture that the company says goes beyond retrieval-augmented generation (RAG) processes and increases agent productivity. As orchestration frameworks continue to improve, this method could offer organizations an option for enhancing agents’ decision-making capabilities. LlamaIndex says ADW can help agents manage “complex workflows beyond simple extraction or matching.”Some agentic frameworks are based on RAG systems, which provide agents the information they need to complete tasks. However, this method does not allow agents to make decisions based on this information. LlamaIndex gave some real-world examples of how ADW would work well. For instance, in contract reviews, human analysts must extract key information, cross-reference regulatory requirements, identify potential risks and generate recommendations. When deployed in that workflow, AI agents would ideally follow the same pattern and make decisions based on the documents they read for contract review and knowledge from other documents. “ADW addresses these challenges by treating documents as part of broader business processes,” LlamaIndex said in a blog post