204 tested guides on AI agents, n8n, RAG, and automation — newest first.
The first 429 usually gets blamed on request volume, and the real cause is usually a token ceiling nobody tracked. Which dimensions providers enforce, why token-bucket replenishment rejects bursts that look well under the limit, and why your output ceiling quietly spends quota.
You have 8,000 outputs, no labels, and no appetite for a labelling project. Reference-free evaluation is possible, and it does not measure what most people assume it measures.
Somebody will ask what good looks like, and there is a number ready for them. The ranges disagree because the denominators disagree, not because the industries do.
The dashboard says the bot handled 4,000 conversations last month and cost very little. Cost per resolved ticket is a different number, usually several times larger.
Somebody asks what your agent did on 14 March for one specific customer. Most teams find out the answer is no on the day the question is asked, not the day the log was designed.
The build estimate was 3 weeks and it was roughly right. The maintenance is the part nobody quoted, and it is predicted by a number you can count before committing.
Nobody ships an agent that is right every time, so the only honest question is how often it may be wrong. The version borrowed straight from web reliability breaks on the first day.
An agent you deployed emailed a customer the wrong refund amount. The answer is always a person or a company, never the software, and which one is decided long before the incident.
Your provider bill is one number and your pricing page charges per seat. The arithmetic connecting them is usually done once, with an average, and the average describes a user who does not exist.
A pilot that answered 40 questions correctly in a demo is not evidence of anything yet. It is a controlled test on data somebody picked, and production asks a different question entirely.
A search tool turns post-cutoff questions from hallucination bait into retrievable facts, and it brings 3 problems with it: choosing sources, paying for pages that become tokens, and reading text that may be aimed at the model rather than the user. This is the wiring guide: the
Most tutorials get you a working agent in ten minutes and skip what breaks it in week two. Sixteen lessons covering the loop, tool descriptions, memory, fallbacks, guardrails, evaluation, and the cost levers that decide your bill.
