DNotifier Blog

Category: Use Cases & Case Studies

  • Agent Harness or Control Plane: Which?

    You’re in a vendor demo. The slide says “agent platform.” The agent runs, the tools fire, and everything looks fine. Then you ask who manages ten agents in production, and the room goes quiet. That gap is the whole story. Many teams buy an agent harness and expect a control plane. The two sound alike,…

  • Harness Engineering Patterns

    Your agent nailed the demo. Then real users showed up. It picked the wrong tool, looped for ten minutes, and nobody could tell why. Most of the time the model isn’t to blame. The code wrapped around it is. That code is the harness, and building it well is harness engineering. This post walks through…

  • AI Agent Harness Explained

    Your agent looked great in the demo. Then real users showed up. It looped on one step, forgot something obvious, and called the wrong tool. The model didn’t get worse. Everything around it just wasn’t ready for the job. That “everything” is the agent harness. So what is it, what does it do, and how…

  • What Is an AI Infrastructure Platform?

    You ship one AI feature. It works. Then you add a second, and things get messy fast. Now you’re juggling model keys, message queues, chat storage, a search index, and glue code nobody wants to touch. That mess is why the AI infrastructure platform exists. What It Is An AI infrastructure platform is the backend…

  • How Does an Agent Event Bus Work?

    Your agents are supposed to work as a team. Instead, they keep stepping on each other, missing updates, or waiting on calls that never come back. That mess is exactly what an agent event bus is built to fix, and once you see how it works, it’s hard to build agents any other way. What…

  • How Multi-Agent Automation Changes Business

    Your team still runs on handoffs. Someone checks the inbox. Someone else updates the spreadsheet. A third person finally acts on whatever’s inside it. Every step waits on the one before it, and by the time something actually reaches a decision, the moment’s usually gone. Multi-agent automation actually changes something specific: the mechanics. Not just…

  • How DNotifier Handles Persistence & Document Loading

    Building an AI agent framework in production often hits a wall when agents lose context between steps or choke on massive external files. State disappears when servers restart, and loading raw unstructured data into a RAG pipeline usually requires stitching together half a dozen standalone tools and parsers. The dnotifier framework solves both problems natively…

  • What Is a RAG Agent?

    Ask an LLM about something that happened last week and it’ll either guess or just make something up. Confidently, too. That’s the problem a RAG agent solves — it gives your AI a way to actually look things up before it answers, instead of running purely on whatever it memorized during training. If you’ve ever…

  • What Vector Database Should You Use for RAG?

    Picking a vector database for RAG feels harder than it should be. Everyone online is arguing about which one is “best,” and honestly, most of that debate misses the point. The truth is simple. The right vector database depends on your data size, your latency needs, and how much infrastructure you actually want to manage…