Ai-Agents

If you’ve been waiting for a computer-use agent you can run on your own machine (sandboxed, human-in-the-loop, 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.

The OWASP LLM Top 10 exists because shipping an LLM to production without a security model is a new category of risk that the existing web application security playbook doesn’t fully cover. Prompt injection has held the #1 spot on that list since the first version was published, and it’s not there because researchers think it might be a problem someday. It’s been demonstrated against production systems at companies that knew what they were doing.

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.

Run MAF DevUI locally to test prompts, inspect tool calls and OTel spans, and register agents by code or directory discovery.

Persist workflow state at superstep boundaries, reload snapshots in a fresh process, and continue without losing progress.