Practical writing on custom AI agents, workflow automation and what it takes to run them in production — costs, failure modes and where to start.
A small team feels every hour spent on repetitive work. Here are five everyday jobs an AI agent can take over today, what each one looks like in practice, and how to pick the one that pays off first.
Hiring an AI agent agency for the first time raises fair questions: what happens first, how much of your team’s time it takes, and what you get at the end. Here is the process from the first call to an agent running in your business.
Agent security is not mostly about the model. It is about what the agent can reach, what it may decide alone, and whether you can reconstruct what it did. Five questions to ask before signing off, and what a good answer sounds like.
These three get sold as competitors and they are not — they solve different problems, and two of them are cheaper than the third. Here is the single question that decides which one your process needs, and what it costs to get the answer wrong.
A working glossary for anyone reading an AI automation proposal. Thirty terms defined in plain English, grouped by where you will meet them, with a note on which are load-bearing and which are decoration.
This question is usually asked backwards. Fine-tuning is requested to solve a knowledge problem, which is the one thing it does not reliably do. Here is what each approach actually changes, and the test that tells you which one your case needs.
The word “agent” now covers everything from a support chatbot to a scheduled script, which makes it nearly useless in a sales conversation. Here is a definition narrow enough to be worth something — and an honest account of when you do not need one.
Every quote for a custom AI agent is a different number, and the reason is rarely the model. Here are the five things that actually move the price, why the running cost should fall rather than rise, and what makes an estimate balloon after work starts.
Picking the wrong first workflow is the most common way an automation programme dies — not because the automation fails, but because it succeeds at something nobody cared about. Here is how to score the candidates.
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