Showing tag results for AI

Aditya Challapally leads post-training research and infrastructure for Copilot agent capabilities that process millions of multimodal interactions. This post builds on the diagnostics from Diagnosing instability in production-scale agent reinforcement learning with the engineering and algorithmic interventions we developed to get the best results ...

When we launched the Microsoft Learn Model Context Protocol (MCP) Server in June 2025, our goal was simple: make it effortless for AI agents to use trusted, up-to-date Microsoft Learn documentation. GitHub Copilot and other agents are increasingly common, and they need to be able to ground responses just like humans with browsers do. Learn MCP Serv...

On January 28, 2026, Hugging Face announced that they have upstreamed the Post-Training Toolkit into TRL as a first-party integration, making these diagnostics directly usable in production RL and agent post-training pipelines. This enables closed-loop monitoring and control patterns that are increasingly necessary for long-running and continuously...

Discover how treating AI agents as collaborators, not automation, transforms engineering workflows and accelerates complex projects

Microsoft’s AI-powered code review assistant has transformed pull request workflows by automating routine checks, suggesting improvements, and enabling conversational Q&A, leading to faster PR completion, improved code quality, and enhanced developer onboarding. Its seamless integration and customizability have driven widespread adoption within Mic...