AI

What a decision model changes about agent safety

A safety check that answers in under half a second and costs a fraction of a cent can run on every message and every tool call. Here is what that changes for an agent in front of real users, and the one thing it does not change.

How autonomous are AI coding agents, really?

Engineers at the leading AI labs now say a model writes one hundred percent of their code. Read the quotes closely and a person is still involved in every one of them. Here is what the 2026 evidence supports, and what it does not.

What founders get wrong about AI agents

An impressive agent demo and a reliable agent are two different things. Most of the work, and most of the risk, is in the final step before production, which nobody shows in the demo.

RAG vs fine-tuning, explained for product teams

RAG and fine-tuning sound like a deep ML choice, but the decision is simpler than it looks. Here is what each is good for, and why retrieval is almost always the right first step.

How to tell if an AI feature idea is worth building

Most AI feature ideas look good in a demo and fail during the work of making them reliable. Here are the four questions we ask to tell the ones worth building from the ones that just look good in a demo.

What AI coding assistants actually change on an engineering team

AI coding assistants speed up real work, but not the work that matters most. Here is what they help with, what they do not, and how to adopt them without lowering your quality standard.

RAG enterprise search development: what it actually takes to build

A model that can search your company's own documents sounds simple until you build one. Here is the real engineering work behind RAG, and how to tell if it is actually working.

How to add LLM integration to an existing product without breaking it

Most teams do not need a complete AI rebuild. They need one strong feature, added in a way that cannot cause the rest of the product to fail.

How to choose an AI development company

Almost anyone can set up an API call and show you a working demo. The company worth hiring is the one that can tell you, in plain terms, what happens when the model is wrong.

AI evaluation and guardrails for production: how to know your AI actually works

"It seems to work" is not a production standard. Here is how we turn that feeling into a number, and how we stop the system from doing damage on the days the number is bad.

How to choose an AI agent development company

Anyone can connect a model to a few tools and call it an agent. Here is what actually separates an AI agent development company from a team that will give you a demo and then leave.

Agentic AI framework design: how to architect a system that acts, not just answers

An agent does more than a chatbot. It is a system that plans, calls tools, and acts across multiple turns, and every one of those turns is a place it can go wrong.