MAF V1: Python and .NET

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.

Pause a workflow for human input, resume it with the same request_id, and keep the caller-side contract simple across runs.

Define a workflow in YAML, load it at runtime, and tweak orchestration without recompiling the Python or .NET app safely.

Use Magentic orchestration for autonomous planning: a manager keeps facts, delegates to workers, reassesses, and replans when stuck.