Agents

Open a chat with an LLM and ask it to fix a bug, and the best you get back is a suggested diff you copy into your editor by hand — the model never touched your …

RAG taxonomy gets confusing because people mix three different ideas: architecture levels, retrieval tricks, and production maturity. Naive RAG, Simple RAG, Gra…

The infographic is useful because it names the eight shelves most agentic AI systems touch: deployment infrastructure, evaluation and monitoring, foundation mod…

A single AI agent session has one context window, and that window is the scarcest resource it has. Ask it to grep forty files, read a long log, and also hold a …

repo-agent is 34 turns into a routine rename: swap calculate_tax for compute_tax everywhere, except inside legacy/. Turn 1 stated that exception clearly. By tur…

Tell an agent to book a one-way flight from Seattle to Austin and the airline has no public booking API — the only way in is the same website a human customer w…

Vibe Coding is for builders who need the term to survive contact with real products, tools, and failure modes. The goal is a practical mental model you can use …

Copilot vs Agent is for builders who need the term to survive contact with real products, tools, and failure modes. The goal is a practical mental model you can…

Jailbreaking vs Prompt Injection is for builders who need the term to survive contact with real products, tools, and failure modes. The goal is a practical ment…

Human-in-the-Loop Design Patterns for AI Agents is for builders who need the term to survive contact with real products, tools, and failure modes. The goal is a…

AI Agent Memory is for builders who need the term to survive contact with real products, tools, and failure modes. The goal is a practical mental model you can …

ReAct Pattern is for builders who need the term to survive contact with real products, tools, and failure modes. The goal is a practical mental model you can us…

An AI agent becomes risky the moment it can read private data, call tools, write files, send messages, or trigger business workflows. A better prompt helps, but…

Running one AI coding agent on a task is easy. The moment you want three or thirty of them working at once — without two agents editing the same file, without l…

Three purple columns labeled IDE, Plugin, and CLI look like a product ranking. They are not. They are a delivery-surface map: where the agent lives relative to …

Timeline slides about Claude Code usually stack five gold dates and call it a rise. Dates are useful only when you ask what capability boundary moved at each ma…

An issue says ORD-142: checkout-api resets after 30 seconds. Every AI coding product claims it can fix the bug, run tests, and open a pull request. The useful q…

The popular "learn AI coding in 3 weeks" roadmaps compress a real skill progression into a grid of buzzwords: vibe coding, vibe engineering, agentic engineering…

A checkout timeout bug lands on your desk. Do you approve every agent edit, write a spec and verify, let the model YOLO the fix, or run an overnight Ralph loop …

A support ticket that needs docs, a tool call, and a model reply does not need twelve equal "frameworks." It needs an orchestration lane, a retrieval lane, a se…

Teams often treat every LLM quality problem as a prompt problem. Often the real issue is what entered the context window, or whether the product needs a harness…

Claude Code is a terminal coding agent — useful only when the repo teaches it how you work. That teaching lives in CLAUDE.md, layered memory files, skills, hook…

AI agents rarely work alone. They read files, query databases, call business APIs, and sometimes delegate work to other agents. Without shared contracts, every …

An LLM predicts tokens from the context it receives; by itself it has no durable application memory or permission to call your systems. A product can add capabi…

A chat demo with an API key is not an LLM product. LLMOps is the set of tools that make models behave like services you can ship: versioned prompts, evals, guar…

A prompt is only one part of a production AI system. Engineers also need vocabulary for execution loops, tool connections, model access, cost, evaluation, safet…

A chatbot answers one prompt at a time. An agentic AI system accepts a goal, selects actions, calls tools, observes results, and loops until it reaches a termin…

This guide is for developers who can call a model API and want to evaluate open-source AI projects without treating stars as a ranking. By the end, you can shor…

Retrieval-Augmented Generation (RAG) grounds LLM answers in your data, not only model weights. Four levels show up in production: Classic (fixed retrieve → gene…

This guide is for engineers who can already explain prompts, models, and API calls but need to turn an agent demo into an owned service. By the end, you can tra…

Artificial intelligence is not here to replace .NET developers. It is here to extend what they can build. The fundamentals you already know — services, APIs, da…