AI Engineering

Microsoft’s Build 2026 announcement — Frontier models and production agents advancing Microsoft Foundry for the agentic era — reframes Foundry from a model catalog into a place you run production agents. The headline that makes it real: hosted agents in Foundry Agent Service are now generally available, and the catalog spans both OpenAI and Anthropic frontier models (1,900+ on Microsoft Learn — not the “11,000” some coverage repeats). So it’s GA — but GA of what, exactly, and what should an enterprise architect actually do with it? This is the take, not a feature recap: the pro, the con, and where I’d draw the line.

I used to think the commit was the natural unit of software history. It is clean. It has a hash. It has a message. It fits in CI. It can be reviewed, reverted, cherry-picked, and blamed.

One control plane for enterprise AI — how APIM’s AI gateway gives security, network, and infrastructure teams governance over models, MCP tools, and agents. The full v2 surface.

If you’ve been waiting for a computer-use agent you can run on your own machine — sandboxed, human-in-the-loop, and open source — Microsoft’s Magentic-UI is the repo to clone this week. It now leads with MagenticLite (“Big tasks. Small models.”), a redesign from Microsoft Research AI Frontiers that pairs a small-model orchestrator with a dedicated browser-use model, and keeps you in control the whole way.

Microsoft Agent Framework’s latest releases — Python 1.10.0 (June 30) and .NET 1.12.0 (July 2) — change a security default that’s easy to miss in the changelog: agent tools and file access now require approval unless you opt out. Both land as breaking changes.

Structure prompts around identity, grounding, task rules, role context, and YAML composition so agents stay consistent over time.

Package the agent stack with one multi-target Dockerfile, one Compose file, and a dev.sh script with health gates locally.

Compare the six Python and .NET MAF API differences you will hit when porting samples, from constructors to workflow wiring.

Build an eval pipeline with golden datasets, scoring, smoke/full modes, and CI gates to catch prompt regressions before release.

Every MAF concept from the prior chapters, mapped to the exact file and line where it lives in the live e-commerce repo.

Render any workflow as Mermaid (GitHub-friendly) or Graphviz DOT (production runbooks). One line each, deterministic output, dark-mode-safe palette.

Add HTTP-layer production patterns for password reset, refresh-token rotation, reuse detection, and secret rotation in APIs.