Field Notes · Vantage Perspectives

AI Agents: The Budget is The Design

Recently, a handful of our agents' analyses came back wrong, and the trace didn't lead to errors in the models. It led to the limits around them, most we had set and one we hadn't. Every AI agent lives inside a set of budgets, and tuning them turned out to be some of the most consequential engineering we've done.

Ben WaldOCT 5 202614 MIN READ

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05Field Notes

Show Your Work: What it Takes to Trust a Machine

A partner is about to lean on an AI-drafted memo to decide whether to put millions into a company. That moment rests on a question the industry has trained us not to ask: how did the machine get here?

Ben WaldSEP 6 20269 MIN READ

05Field Notes

AI Model Temperature: What it is and What it means for Judgment

Every AI agent runs at a temperature. Here is what that means, why it matters, and how each Vantage agent gets its own.

Ben WaldSEP 6 20268 MIN READ

05Field Notes

Building the Learning Loop: The Architecture of an Agent That Actually Improves

Most of the argument about AI agents is still about whether they can act. Inside our fleet that question is settled. The harder one is whether an agent gets better, and the answer turns out to be an argument about where a lesson should live.

Ben WaldJUL 29 202614 MIN READ

05Field Notes

Why Our Agents Have No Reward Function: Score Everything, Optimize Nothing

We score every claim our agents make, and we never let a score change an agent's behavior without a person in between. That second rule is the most consequential design decision we've made.

Ben WaldJUL 29 202611 MIN READ