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From Chatbot to Workflow Engine: Building Event-Driven Agents with Microsoft Agent Framework
Most teams begin with the same agent demo. A user asks a question, the model calls a tool, and something useful happens. That is a fine starting point, but it is not how enterprise work actually runs. Real workflows unfold across time. A case is opened. A document arrives later.… Read more ⇢
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Harness Engineering Is Where Enterprise AI Becomes Real
Enterprise AI is moving past the novelty phase. The next fight will not be won by the company with the cleverest prompt or the loudest model announcement. It will be won by the company that can turn model intelligence into controlled, repeatable, observable work. That is the point of harness… Read more ⇢
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Microsoft Agent Framework: Designing Human-in-the-Loop Agents That Enterprises Can Actually Trust
Most teams do not lose trust in agents because the model writes a weak paragraph or chooses the wrong phrasing. They lose trust when the agent crosses a line it should not cross. It sends the email. It updates the record. It triggers the workflow. It escalates the issue. It… Read more ⇢
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Demo-Grade vs Ship-Grade: The Most Expensive Confusion in AI
A great demo is a dopamine hit with a budget. It is the moment when a messy idea turns into something you can click, react to, and show your board with confidence. And in 2026, with copilots, agents, and “vibe-coded” prototypes, the demo is getting easier to manufacture than ever.… Read more ⇢
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Long Conversations Break Agents Before They Break Models
There is a mistake I see over and over in LLM projects. Teams assume that once they pick a model with a large context window, memory is basically solved. They think the hard part is buying enough room. It usually is not. The hard part is deciding what deserves to… Read more ⇢
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Ship Like a Creator: What MrBeast’s Production Memo Teaches Modern Product Teams
Most product teams still build like they are producing a film, even as the playbook has shifted under their feet. This is why the internal creator-style operating guidance in How-To-Succeed-At-MrBeast-Production.pdf is so useful: it reads less like entertainment advice and more like a blueprint for how modern product teams should… Read more ⇢
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Context Window Compaction in Mastra: How to Keep Agents Sharp as Conversations Grow
Some teams think about long context the wrong way. They treat the context window like a storage upgrade. Bigger model. Bigger window. Bigger bill. Problem solved. That is not how this works in production. As conversations get longer, the real challenge is not whether the model can technically accept more… Read more ⇢
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How to Ship Microsoft Agent Framework Skills from a CMS Instead of the File System
Most teams start with Microsoft Agent Framework skills on disk because that is the default mental model the framework encourages today. In .NET, FileAgentSkillsProvider is explicitly documented as an AIContextProvider that discovers skills from filesystem directories and follows a progressive disclosure pattern: advertise the skill, load the full SKILL.md only when needed, then read supporting resources… Read more ⇢
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How to Read Microsoft Agent Framework Skills from a Database Instead of the File System
Most teams start with file-based skills because that is the built-in model in Microsoft Agent Framework today. In .NET, the built-in FileAgentSkillsProvider discovers SKILL.md files from directories, advertises skill names and descriptions in the prompt, returns the full skill body through load_skill, and reads supporting files through read_skill_resource. That model is clean, portable, and easy to… Read more ⇢
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