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Agentic Innovation

Bridging Applied Research with Real-World Systems & Scenarios to define the future of Enterprise Productivity​

Agentic Innovation – Mission Statement

The M365 Research Agentic Innovation team bridges applied research with real-world systems and scenarios to define the future of enterprise productivity. Our efforts are organized around three strategic domains — Foundational capabilities for robust agents, Applied agentic systems for the cloud, and the future of agentic AI. We are tackling the core challenges that prevent AI agents from reliably serving knowledge workers at scale. Its research spans persistent memory and context management, reasoning quality through RL-based post-training and evaluation, and systematic robustness via failure diagnosis and hardening.

With deep product engagements across M365 Copilot, Teams, Copilot Studio, Frontier Tuning, Agent 365, and Azure Foundry, we are collectively driving step-function advances in agent quality, reliability, and efficiency for Microsoft.

We are barely scratching the surface in terms what is possible when combining cutting-edge algorithmic research and state of the art of AI/ML techniques. We strongly feel that this multi-faceted approach will help propel our infrastructure and services to adapt to the paradigm shifts and enable it to deliver best in class productivity experiences.

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Careers: We are always on the lookout for motivated and dedicated candidates for Researcher, PostDoc and Internship positions in our team. If you are interested in doing cutting edge research to make our cloud infrastructure more efficient and reliable, please email us your latest CV.

The Strategic Domains

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Foundational Capabilities for Robust Agents

Develop the core building blocks for reliable AI by focusing on long-term memory, principled guardrails, formal validation, robustness frameworks and reinforcement learning to enhance reasoning quality and tool calls.

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Accelerating Enterprise Productivity

Advance enterprise productivity at-scale by adapting and integrating our foundational innovations on context/knowledge management, reasoning, robustness and synthetic data into real-world Agents used by Knowledge workers, Developers and SREs.

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Future of Agentic Intelligence

Pioneer next-generation AI paradigms such as proactive, cross-modal intelligence, in-context intelligence, self-evolving multi-agent systems, advanced planning, reflection and tool-use paradigms, and new product categories.

Our Research

    • LegoMem: Distills reusable procedural memory and composable skills from past agent trajectories, so agents can recall and reapply proven workflows—boosting task completion rates and consistency across complex, long-running tasks.
    • Memora: A balanced, efficient, and general-purpose long-term memory abstraction for AI agents, designed to persist and retrieve knowledge reliably across sessions and domains.
    • ACON: Dynamic context management and compression that keeps long-running agents focused and cost-efficient—intelligently pruning, summarizing, and prioritizing context to sustain performance over extended tasks.
    • Active partners: M365 Copilot | Teams | Scout Agent
    • Diagnosis: When an agent fails inside a long, multi-step task, the system pinpoints where and why it went wrong.
    • Bounded repair: That diagnosis becomes a targeted fix to the scaffolding around the agent, its prompts, tools, and configurations, rather than a retraining of the model.
    • A governed loop: The aim is for each candidate fix to be validated before it ships and governed so changes stay inspectable and reversible, with what’s learned retained so the system improves over successive cycles. This points toward a controlled, auditable path to more reliable agents rather than open-ended self-modification.
    • Active partners: Copilot Studio | Agent365 | CoWork
    • Evaluation environments: Evaluating enterprise agents requires realistic environments to test them in, so we explore how to shape synthetic tenants that carry the specific properties a given evaluation or use case demands, from organizational structure to the artifacts and context an agent must reason over.
    • Training-data quality: The same understanding of what makes data useful feeds our work on identifying and filtering low-signal data before it reaches compute, so training effort is spent where it improves the model.
    • Active partners: Researcher | Copilot Tuning

    • AI-accelerated code engineering: Build agents and knowledge workflows that help engineering teams modernize codebases, manage large-scale migrations, keep migration knowledge bases current, and generate high-confidence guidance from PR history, benchmark results, and platform feedback.
    • AI for Autonomous cloud operations: Develop agentic systems that improve service reliability by supporting monitoring, triage, incident diagnosis, mitigation recommendations, and reusable operational workflows grounded in telemetry, historical incidents, troubleshooting guides, and domain tools.
    • Active partners: Azure and M365 Cloud

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