Archive | Artificial Intelligence

In this article, you will learn what prompt compression is, why it matters for agentic AI loops, and how to implement it practically using summarization and instruction distillation.

This article illustrates how to implement a permission-gated tool in a Python agent, resulting in a robust, cost-free interception mechanism based on a simple decorator pattern.

In this article, you will learn how to design, scale, and secure tool calling in AI agents so that the layer connecting model reasoning to real-world action holds up in production.

Describing and implementing two simple yet effective approaches to ensure AI agent safety: semantic drift based of cosine distance and confidence thresholding based on log-probability entropy.

In this article, you will learn what agentic RAG is, how it differs from traditional RAG, and when to use it.

In this article, you will learn how to build production-ready AI agents in Python using Pydantic AI, with structured outputs, custom tools, and dependency injection.

In this article, you will learn what context engineering is and how to apply it systematically to keep AI agents reliable, cost-efficient, and accurate in production.

In this article, you will learn how to build a fully functional AI agent that runs entirely on your own machine using small language models, with no internet connection and no API costs required. Topics we will cover include: What AI agents and small language models are, and why running them locally is a practical […]

In this article, you will learn how AI agent memory works across working memory, external memory, and scalable memory architectures for building agents that improve over time.

In this article, you will learn how to build a local, privacy-first tool-calling agent using the Gemma 4 model family and Ollama.