After three months running Model Router in our private ChatGPT app, I figured it was time to share what actually works in production versus what the documentation says should work. Part 1 covered the architecture and decision framework. This post walks through the real implementation – deployment, code, monitoring, and the edge cases that aren’t obvious until you hit them.Fair warning: this gets technical. I’m showing you the .NET code we use, the telemetry patterns…
In Part 1, we explored the fundamental shift from manual SharePoint indexing to Azure AI Foundry’s SharePoint grounding tool. In Part 2, we built a complete, production-ready SharePoint agent system with multiple agent types. Now it’s time to tackle the most critical aspect of enterprise SharePoint AI agents: proper identity passthrough and delegated permissions. This is where many implementations fall short in real-world scenarios, and where the true power of Microsoft 365 Copilot API’s identity…
In Part 1, we explored the fundamental shift from manual SharePoint indexing to Azure AI Foundry’s SharePoint grounding tool. We covered the challenges of traditional approaches, the power of Microsoft 365 Copilot API, and the economic considerations for enterprise adoption. Now it’s time to get our hands dirty with the actual implementation. In this part, we’ll build a complete, production-ready SharePoint agent system. Prerequisites and Setup Before diving into implementation, ensure you have the following…
You know that feeling when you’ve spent weeks building a custom indexing pipeline for SharePoint content, complete with incremental updates, change tracking, and governance controls—only to discover that Microsoft just released a tool that does all of this automatically? Many companies these days store thousands of policy documents, procedures, and knowledge base articles scattered across multiple SharePoint sites. The existing solution required manually extracting content, chunking it into a vector store like Azure AI Search,…
When building AI agents for enterprise applications, standard web search often returns irrelevant results that compromise the quality of your solution. During a recent client project, our team needed an AI assistant capable of providing developers with specific documentation and code samples. While Bing Search returned results from various sources including outdated forum posts and unofficial tutorials, we required focused, authoritative content from trusted developer resources. This challenge led us to implement Bing Custom Search—a…