You have eleven versions of the same prompt in a doc, three of them named "final," and no real idea which one scores best on last week's failures. So you tweak it again by hand, read ten outputs, decide it seems better, and ship. That is not optimiza...

We support multiple LLM providers, and each one behaves differently when wired into the same QueryAgent pipeline. We wanted to understand what that difference actually looks like, in accuracy, in cost, and across query types. So we ran a structured b...

Every "build an AI agent" tutorial shows you the same twelve lines: a while loop, a call to the model, a check for tool calls, execute them, feed the results back, repeat. It runs, the agent uses a tool, you feel like a wizard. Then you put it in fro...

How to evaluate a voice agent before launch with simulation Evaluating a voice agent used to mean listening to recordings, then it meant scripts that replayed transcripts at the agent. Neither catches much. The approach that works now is to simula...

Most developers think of a folder tree as a way to organize files. For AI systems, it can be much more than that. The Interpret-able Context Methodology ICM, also known as the Model Workspace Protocol MWP, introduced by Jake Van Clief and David McD...

A Quick Intro I recently built a local RAG agenthttps://github.com/vkondi/knowledge-onboarding-agent that reads a bunch of documents stored as markdown files and lets you ask questions about them in plain English. It goes through all the documents ...

If you've been building AI applications recently, you've probably come across Retrieval-Augmented Generation RAG. It has become the go-to way of giving LLMs access to external knowledge. But RAG isn't the only option. As context windows continue to...

> Series: Building with 74 AI Personas - Part 10 > Tags: #ai #architecture #agents #localfirst #ux > > Note: In this series, a "persona" is not only a fictional character. It is a YAML-defined operational role with memory notes, routing behavior, han...