Agent Harness Explained: What It Is, When to Use One, and How to Build Your Own
A first-principles guide to the AI agent harness: the layer around the model that decides whether your agent actually works.
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Stratos Louvaris
Agentic AI Engineer. I build AI agents that work in production and document what breaks along the way. RAG, MCP, agent evals, agent security, self-hosted LLM infrastructure.
Building an Eval Stack That Catches Regressions: Judges, RAG Metrics, and CI Gates (Part 2 of 2)
How to grade what code can't assert, evaluate RAG without debugging blind, and wire a gate that survives production.
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Evaluating AI Agents: Why 95% Per-Step Accuracy Is a Failing Grade (Part 1 of 2)
Why agent evaluation breaks the tools built for prompts, and how to measure outcomes instead of vibes.
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Agent Skills Explained: What They Are, How to Use Them, and How They Differ From MCP
A first-principles guide to Agent Skills: how they work, how to build one with skill-creator, and how they differ from MCP servers.
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Running MCP in Production: Where Demos Break and How to Harden It (Part 2 of 2)
What goes wrong when MCP meets real users — context overload, smelly tool descriptions, prompt-injection-as-RCE — and the infrastructure work that prevents it.
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What MCP Actually Is: Architecture, Primitives, and Why It Exists (Part 1 of 2)
Large language models are brilliant but isolated. The Model Context Protocol (MCP) fixes this by standardizing AI integrations. Just as LSP did for IDEs, MCP lets you write a server once using three primitives, Tools, Resources, and Prompts, so any compliant AI assistant can seamlessly use your
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11 Advanced RAG Strategies That Separate Demos From Production Systems (Part 3 of 3)
Basic RAG works for demos, but production demands more. Explore 11 advanced strategies, from re-ranking and multi-query to agentic and self-reflective RAG, that solve specific failure modes. Learn how to stack these techniques to build accurate, production-ready AI systems.
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RAG Embeddings, Vector Databases & Chunking — Part 2
Embeddings, vector databases, and chunking are the foundation of every RAG system. Compare 6 vector DBs and 7 chunking strategies for your stack.
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What Is RAG and Why Your LLM Needs It (Part 1 of 3)
Learn what Retrieval-Augmented Generation is, why LLMs fail without it, and how the complete RAG pipeline works from ingestion to answer generation.
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How to Use Google's NotebookLM as Your Personal Learning Assistant
NotebookLM tutorial: Learn how source-grounded AI solves documentation hallucination. This AI tool only uses your uploaded documents and provides citations.
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AI for Sysadmins and Developers (Part 2): A Guide to Self-Hosting LLMs with Ollama
Choosing the right AI model can be challenging. In this comparison, we break down open-source and proprietary models by cost, control, privacy, and performance, helping you make an informed and strategic decision for your specific needs.
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The Ultimate Guide to AI for Sysadmins and Developers (Part 1): From Neural Networks to the MLOps Factory Floor
Tired of treating AI like a magic black box? This practical guide for sysadmins and developers demystifies neural networks and the MLOps lifecycle. Learn to build, scale, and manage the engineering backbone of modern AI. Part 1 of our ultimate guide.
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Building a Secure Home Network: My Journey from Chaos to Control
So your network is a chaotic mess? Learn how I transformed my flat, vulnerable home network into a secure fortress using OPNsense, Pi-hole, and VLAN segmentation. This guide details the journey from chaos to complete control.
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Building My Personal Server: A Complete Journey
So, are you ready to take control of your data and learn valuable tech skills? This guide details my personal server build, from choosing hardware to self-hosting a secure website with Docker, a VPN with WireGuard, and more. It's a journey into digital ownership.
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