A plain guide to AI agents: how they plan, act and check their work

Agents split a goal into steps, use tools, and verify the outcome. This is how each part works, without the hype.

Five questions to ask before an agent touches your data

Access, limits, logs, rollback and who signs off.

Why context matters more than model size

A smaller model with the right information often beats a bigger one without it.

Open models or closed models: the real trade-offs

Cost, control and speed pull in different directions.

What an AI benchmark actually measures

A high score is useful. It is not the same as being good at your task.

Small models, real results: running AI on your own servers

When a smaller model is the better choice, and what it costs to run one.

How AI coding tools change a developer’s day

Less typing, more reviewing. What teams say works and what does not.

What ‘reasoning’ means in an AI model

A plain look at how models work through a problem step by step.

The real cost of running AI at scale

Chips, power and people. Where the money goes after the demo.

A short history of AI assistants

From simple chat windows to tools that act on their own.