Category
AI Engineering
56 posts filed under AI Engineering.
Evaluating Agent Quality -- Testing What You Cannot Unit Test
Build a repeatable evaluation pipeline for multi-agent correctness -- golden datasets, automated scoring, and CI/CD integration.
MCP Integration -- Connecting AI Agents to the Tool Ecosystem
Replace hand-coded tools with Model Context Protocol servers -- standardized tool discovery and execution for any agent framework.
Graph-Based Workflows -- Beyond Simple Orchestration
Move from LLM-driven routing to deterministic graph-based workflows -- sequential pipelines, parallel execution, conditional branching, and state management.
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 — 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 — 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 — 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.
