AI Engineering

Revised with .NET examples — A newer version of this article, covering both Python and .NET, is available as part of the MAF v1: Python and .NET series: MAF v1 — 04-sessions, MAF v1 — 05-context-providers.

Revised with .NET examples — A newer version of this article, covering both Python and .NET, is available as part of the MAF v1: Python and .NET series: MAF v1 — 23-evaluation-framework. The newer version applies three substantive fixes to the framework below — canonical AgentRunResponse extraction (no more hasattr chain), word-boundary alias matching (the original false-positives "profit" against the "price" alias), and a smoke / full tier split for CI vs nightly runs. Read this article for the conceptual ground; read the new one for the production-grade implementation.

Revised with .NET examples — A newer version of this article, covering both Python and .NET, is available as part of the MAF v1: Python and .NET series: MAF v1 — 08-mcp-tools.

Revised with .NET examples — A newer version of this article, covering both Python and .NET, is available as part of the MAF v1: Python and .NET series: MAF v1 — 09-workflow-executors-and-edges, MAF v1 — 10-workflow-events-and-builder.

Stream tokens as they arrive and reuse a session across turns so the LLM sees the full conversation. ~60 lines of code per language.

Serialize an AgentSession to JSON, persist it, reload in a fresh process, and have the agent pick up exactly where it left off.

Build the smallest useful Microsoft Agent Framework program: one LLM call, one response, and the same shape in Python and .NET.

Install uv, .NET 9, Docker, OpenAI keys, and a verify script so Chapter 1 starts with both Python and .NET environments ready.

Give an agent a callable function, let the LLM decide when to use it, and compare Python and .NET tool loops side by side.