Hi, Victor here.
I'm a leader in AI agents, developer tools, and human-AI interaction.

Automatic evaluation and optimization of hosted AI agents in Microsoft Foundry Agent Service.

Agent Optimizer is a service in Microsoft Foundry Agent Service that automates the agent improvement cycle through a closed-loop process: it evaluates baseline agent performance against defined criteria, generates optimized candidate configurations, tests and ranks candidates by score, and deploys winning configurations with a single command. It supports multiple optimization targets - instructions (rewriting system prompts), skills (generating reusable procedures), models (evaluating across LLM deployments), and tool descriptions (improving function-calling clarity). An 'eval init' command generates test datasets and scoring criteria from existing agent instructions, addressing the cold-start problem, and a portal interface supports browsing optimization runs and comparing candidates. Announced at Microsoft Build 2026 and available to hundreds of thousands of customers on Azure.

An open-source SDK and runtime for building, orchestrating, and deploying AI agents - the unification of Semantic Kernel and AutoGen.

Microsoft Agent Framework is an open-source SDK and runtime (Python and .NET) for building, orchestrating, and deploying AI agents and multi-agent workflows. It unifies Semantic Kernel and AutoGen into a single framework and is integrated into Azure Foundry Agent Service. I led the unification effort and designed the middleware/guardrails architecture that defines interception points for all function, tool, and agent calls across the SDK.

A research prototype of a human-centered web agent with co-planning, co-tasking, and action guards.

Magentic-UI is an open-source research prototype for developing and studying human-agent interaction. Built on a flexible multi-agent architecture, it supports web browsing, code execution, and file manipulation, and can be extended with tools via the Model Context Protocol (MCP). It implements six mechanisms for effective, low-cost human involvement - co-planning, co-tasking, multi-tasking, action guards, and long-term memory - and seeks user approval before executing potentially irreversible actions.

A generalist multi-agent system where an Orchestrator directs specialized agents - WebSurfer, FileSurfer, Coder and ComputerTerminal - to complete complex, multi-step tasks.

Magentic-One is a generalist agentic system for tasks that span the web, files and code. A lead Orchestrator agent plans the work and tracks it in a task ledger and a progress ledger, delegating to four specialized agents: WebSurfer (a Chromium browser), FileSurfer (local files), Coder (writes and analyzes code) and ComputerTerminal (runs programs and installs libraries); the Orchestrator re-plans when progress stalls. On the GAIA, AssistantBench and WebArena benchmarks it performs statistically comparably to the state of the art, and significantly better than GPT-4 alone. Released in November 2024 as a Microsoft Research technical report and open sourced in AutoGen, it has become a reference architecture for autonomous multi-agent systems.