MAF v1 — Observability with OpenTelemetry (Python + .NET)
Wire OpenTelemetry spans for agent runs with GenAI attributes, using console export for dev and OTLP for production tracing.
Ai-Agents
MAF v1 — Middleware and the Agent Pipeline (Python + .NET)
Use middleware to wrap agent runs, intercept tool calls, and redact PII before the LLM sees it in both Python and .NET safely.
MAF v1 — Context Providers (Python + .NET)
Add per-request context with focused providers, compose them before each LLM call, and plug in TextSearchProvider for simple RAG.
Building a Multi-Agent E-Commerce Platform: The Complete Guide Pinned
Most AI agent tutorials end at “hello world.” You build a single agent with one or two tools, it answers a few questions, and that is it. The gap between that tutorial and a production system with multiple agents, authentication, observability, and a real frontend is enormous.
AI Agents: Concepts and Your First 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Revised with .NET examples — A newer version of this article, covering both Python and .NET, is available as part of the MAF v1: Python and .NET series: MAF v1 — 01-first-agent, MAF v1 — 02-add-tools.
Prompt Engineering for AI Agents -- Grounding, Roles, and YAML Configuration
Revised with .NET examples — A newer version of this article, covering both Python and .NET, is available as part of the MAF v1: Python and .NET series: MAF v1 — 24-prompt-engineering. The newer version pulls role-specific behavior out of the system prompt and into a context provider, so you don’t re-instantiate the agent per role. The five concerns and YAML composition below are still the canonical architecture; the new chapter shows the modernised wiring.
Building Domain-Specific Tools -- Giving Agents Real Capabilities
Revised with .NET examples — A newer version of this article, covering both Python and .NET, is available as part of the MAF v1: Python and .NET series: MAF v1 — 02-add-tools.
MAF v1 — Streaming and Multi-turn (Python + .NET)
Stream tokens as they arrive and reuse a session across turns so the LLM sees the full conversation. ~60 lines of code per language.
MAF v1 — Sessions (Python + .NET)
Serialize an AgentSession to JSON, persist it, reload in a fresh process, and have the agent pick up exactly where it left off.
MAF v1 — Your First Agent (Python + .NET)
Build the smallest useful Microsoft Agent Framework program: one LLM call, one response, and the same shape in Python and .NET.
MAF v1 — Setup your dev environment (Python + .NET)
Install uv, .NET 9, Docker, OpenAI keys, and a verify script so Chapter 1 starts with both Python and .NET environments ready.
MAF v1 — Adding Tools (Python + .NET)
Give an agent a callable function, let the LLM decide when to use it, and compare Python and .NET tool loops side by side.
