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Industrial research and AI engineering

Microsoft Research

Microsoft Research conducts fundamental research in computing, AI, and science, sharing models, datasets, and software that researchers and developers can build upon. Its Orchard framework lets researchers train and evaluate AI agents across different tasks using shared infrastructure. Aurora, its foundation model for weather and Earth systems, supports weather, climate, and energy applications, including probabilistic ensemble forecasting. These tools sit within a broader research program spanning scientific discovery, human health, and computing.

Founded by Richard (Rick) Rashid in 1991, the organization is now led by executive vice president Igor Tsyganskiy, who succeeded Peter Lee in 2026 as Lee became president of Microsoft Science. Its engineering contributions include AutoGen, a framework for applications in which multiple AI agents act autonomously or work alongside people. Its architecture separates message passing and agent runtimes from higher-level conversation APIs. AutoGen remains available in community-managed maintenance mode; its repository directs new projects to Microsoft Agent Framework.

Microsoft Research combines academic-style exploration with an industrial role: its researchers collaborate with Microsoft product teams to shape roadmaps and product innovation, alongside partners in academia, government, and industry. As of 2026, the organization reported that its community and collaborators had published tens of thousands of papers and released hundreds of open-source projects and datasets over the preceding three decades, making shared research a substantial part of its output.

www.microsoft.com

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  1. GraphRAG methods to create optimized LLM context windows for Retrieval — Jonathan Larson, Microsoft

    Start here for concrete code examples: an ordinary RAG versus GraphRAG comparison on a multifile game, translation into working Rust, and analysis of Doom's larger codebase.

    Jonathan LarsonAI Engineer World's Fair 2025

  2. UX Design Principles for (Semi) Autonomous Multi-Agent Systems

    Start here for an application-level view of agent UX through BlenderLM, which converts natural-language requests into Blender 3D operations.

    Victor DibiaAI Engineer World's Fair 2025

Messages from the stage

Evaluating retrieval across context sizes

Larson introduces BenchmarkQED and discusses LazyGraphRAG results against vector-based RAG across different context-window sizes, placing evaluation alongside demonstrations of code understanding.

Making agent work interruptible

Dibia describes checkpointing, rollback, pause, and resume as mechanisms for interrupting agent behavior, alongside attention to delegation costs.

Affiliations reflect each recorded session, not necessarily current employment.

Company sources · checked 2026-08-28