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Series
This series covers what actually happens when AI systems move from demo to production - agent workflows, LLM behavior, failure modes, and the architectural decisions that make systems reliable at scale.
Everyone is tuning prompts and picking models. Almost nobody is talking about the harness. And after building agentic systems for enterprise customers over the last year, the harness is where most of
There are three real options for building agentic AI on the Microsoft stack right now. Azure AI Foundry Agent Service, Semantic Kernel, and AutoGen. Pick the wrong one and you'll spend three months re
The number gets thrown around so often it almost loses meaning. MIT, Gartner, RAND, McKinsey. They all keep arriving at the same answer from different angles. Most enterprise AI pilots never become an
Every system I’ve ever built eventually teaches me the same lesson, in a slightly different costume. The latest version showed up while building Azure AI agents this year, and it took me longer to rec
Most MCP content right now falls into two camps: either "here's why MCP is revolutionary" or "here's how to build your first MCP server in 10 minutes." Both are fine if you're just starting out. This
Demos lie. Your agent works perfectly when you’re showing it to stakeholders, then falls apart the first time a tool times out at 2am. I’ve been deploying Foundry agents into enterprise environments f
