Notes on governed agents
Short notes on scope, holds, spend limits and records, written for the people who have to sign off on an agent.
How much does an AI agent cost?
Three numbers: what it costs to build, what each run costs, and what it can commit in your name. Most quotes only cover the first.
Should you build or buy AI agents?
Building one agent is quick. Running many for years is the real cost, and teams often misjudge it at the start.
What is human in the loop AI?
Human in the loop works when a named person reviews the cases the system is unsure about, at a threshold you choose.
AI agents vs RPA: what is the difference?
RPA follows fixed steps and an agent makes judgements. Most back offices need both, each in its own place.
What should you ask an AI vendor before signing?
Twelve questions that separate a system you can run from a demo you cannot govern. Ask us the same ones.
Why do AI projects fail?
An MIT NANDA report found most generative AI pilots showed no measurable return. This note covers the work around the model.
What should an AI audit trail contain?
Seven fields, and why logs are not an audit trail. What your buyer's risk function will ask for, and what most systems cannot produce.
How to set a spend limit on an AI agent
Set it in money rather than tokens, at three levels, enforced in the system. How to derive the number from the process you are replacing.
How to monitor AI agents in production
Four signals worth watching: quality drift, hold rate, spend per run and latency. Uptime alone does not show whether the answers are still good.
What is AI governance?
Six controls that decide what an AI system may do, on whose authority, at what cost, and with what evidence. A practical guide rather than a policy template.
The controls in these notes are what Gatehouse, Surehand’s control plane, enforces at runtime.
See Gatehouse