What happens when a wrong answer isn't a bad response, it's a safety incident?

Construction is one of the least forgiving places to deploy AI. The data is scattered across drawings, site diaries, forms, and credentials, and getting it wrong can mean a defect, a blown budget, or an injury on site. BuildPass is putting AI into that environment anyway, without taking the human out of the loop.

In this episode of Agents After Dark, Matt Doughty sits down with Aaron Vanston, Co-Founder & CTO of BuildPass to unpack how you earn trust in AI agents when the downside is life and death.

Together they discuss:

• Why Aaron went from strategic founder back "on the tools," and how AI lets context-rich, time-poor leaders ship ideas again
• The case that "SaaS is dead", and why the real opportunity is building the system of record and tools (MCP included) for customers to build on, not another dashboard
• Why human-in-the-loop is non-negotiable in construction: no hidden multi-step chains, every generative output shown to the user
• Trust as one-way vs. two-way doors: confidence scores, sources, and impact analysis before an action graduates from "always ask" to autonomous
• Why delegating control to users beats deciding for them, and how to avoid the "accept all" fatigue that made everyone skip permissions
• Why evals are a moat: the tests and captured intent-vs-outcome data that tell you whether a new model is actually better for your users
• How to catch agentic drift when models change underneath you, and why an eval can be as informal as a vibe check

A practical guide to running AI agents in a regulated, safety-critical industry, covering human oversight, trust, evals, and quality loops, for any enterprise deploying agents where mistakes actually cost something.

About Aaron

Aaron Vanston is Co-Founder & CTO of BuildPass, an AI-native construction platform used by hundreds of builders and over 150,000 workers across Australia, New Zealand, and the US. He previously led engineering teams at REA Group and SEEK.

About Prefactor

Prefactor helps enterprises trust AI in production. Prefactor gives engineering, product, and security teams a single platform to monitor AI systems, evaluate outcomes, identify risks, and take action when things go wrong.

Learn more at prefactor.ai