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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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Task Context vs Shared Context: The Mental Model That Makes AI Product Teams Actually Scale
AI did not introduce the need for context. It exposed how little of it most teams have. Right now, “context engineering” is having its moment. People talk about RAG, long context windows, tool calling, and standards like the Model Context Protocol. (Model Context Protocol) Those are real advances. But they… Read more ⇢
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Your Agent Does Not Need More Prompt. It Needs Memory.
Most teams try to fix weak agents by rewriting prompts. That is usually the wrong move. The real issue is that the agent has no durable memory model. It can answer the current turn, but it cannot reliably carry forward user preferences, prior decisions, task context, or the small facts… Read more ⇢
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Defining Product Value: Stop Treating Products Like Projects
Most leadership teams say they are building “product value,” then immediately measure it like a finance exercise. ROI. LTV. Payback period. Those numbers matter, but they are also the fastest way to undervalue the most important products you will ever build. The uncomfortable truth is that many of the products… Read more ⇢
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How to Add a Secure JavaScript Execution Tool to Microsoft Agent Framework
There is a recurring moment in agent design where a team realizes the model does not just need to reason. It needs to compute. It needs to transform JSON, run a formula, post-process extracted fields, normalize dates, build a dynamic object, or apply domain logic that is simply easier to express in… Read more ⇢
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