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Jul 21, 2026
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Introducing the ETW MCP: AI-assisted ETL trace analysis, headless and in your terminal

Tristan Gibeau

If you’ve ever opened an ETL trace with hundreds of data tables and wondered “where do I even start?” This post is for you. We’re releasing an early preview of the ETW MCP, a Model Context Protocol server that lets GitHub Copilot, or any MCP-aware AI assistant, read, query, and reason over Event Tracing for Windows (ETW) traces the same way an experienced engineer would. No UI required. This is a companion to the WPA MCP (coming soon), which brings the same AI-assisted experience inside Windows Performance Analyzer. The ETW MCP server is the headless sibling, same data layer, no WPA needed, works anywhere you ...

Jul 21, 2026
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How to test agent experience changes without shipping them

Waldek,
Garry

Most changes you think will improve AI agent behavior won't. We tested a dozen hypotheses on a real project upgrade scenario and the majority failed. Learn how to emulate documentation, API, and MCP server changes locally so you can validate what works before shipping anything to production.

Jul 20, 2026
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AI agents, meet the Azure Cosmos DB vNext emulator

Abhishek Gupta

If you use the Azure Cosmos DB vNext emulator, you probably know the local development loop: start the emulator, connect to it, create some resources, load test data, run queries, and inspect the results. Each step is straightforward, but together they add setup work before you can test the application you are actually building. How agents work with the emulator   The emulator includes the Azure Cosmos DB Shell, an open-source CLI for working with databases, containers, and items. It runs inside the emulator container and handles the local endpoint and well-known key, giving developers a direct, scr...

Jul 17, 2026
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How to test agent skills without hitting real APIs

Waldek Mastykarz

Your agent skill calls an API. The moment you start evaluating it, every run either costs money or mutates production data. Learn how to mock APIs transparently so you can run evals without changing your skill or hitting real endpoints.

Jul 17, 2026
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Teaching a Vision Model to See Like a Human Annotator—and Catching It When It Lies

Deeptanil,
Kartheek

A multimodal LLM enrichment pipeline that extracts structured metadata from visual assets, constrains output to predefined values to minimize hallucinations, and uses a ground truth evaluation template to measure quality—all as a plug-and-play module.

Jul 15, 2026
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Building AX evals that actually work

Waldek Mastykarz

This is the eighth and final article in a series about Agent Experience (AX): the practice of making AI coding agents work correctly with your technology. The series covers what you can and can't control in the agent stack, how to measure whether your extensions are helping or hurting, and how to iterate toward better outcomes. You've read seven articles about what to measure, why benchmarks don't transfer, and what hidden variables can do to your results. Now you actually have to build the thing. Most teams build an eval, run it, get scores, and feel good about the numbers. The trouble is that these evals produ...

Jul 10, 2026
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Microsoft Agent Framework for Go public preview

Quim Muntal

Microsoft Agent Framework for Go is entering public preview, bringing Agent Framework concepts to Go developers building agents and multi-agent workflows.

Jul 9, 2026
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Modernize .NET applications in the GitHub Copilot app

Mika Dumont

Modernize .NET applications in the GitHub Copilot app. Follow your upgrade from assessment through execution in an interactive upgrade canvas.

Jul 9, 2026
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From Noisy Queries to Precise Frames: Query Decomposition for Media Asset Search

Kartheek,
Deeptanil

How query decomposition separates metadata filters from visual intent to significantly improve media asset retrieval quality.

Jul 8, 2026
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Building on Vercel’s eve + Azure Cosmos DB: An Agent That Remembers

Sajeetharan Sinnathurai

Most "AI agent" demos forget everything the moment the process exits. That's fine for a toy project, but useless for anything real. An agent that helps you write, triage, or support needs two things a language model alone can't give it: durable state and the ability to recall the right context by meaning. This post shows how to build exactly that by integrating two pieces that fit together surprisingly well: Eve — Vercel's filesystem-first agent platform. Drop a file in agent/tools/, and it becomes a tool the model can call. Azure Cosmos DB JavaScript SDK — the official, promise-based client for Cosmos DB N...

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