#agent-trust

The Year We Taught a Machine to Tutor

A team built an ambitious AI tutor, watched it slowly degrade under its own safeguards, paused the pilot, and rebuilt it twice — the stubbornly simple rebuild won, decisively.

The Agent Trust Framework — an overview

A horizontal architecture for buyers, users, builders, vendors, and regulators of AI agents. Three layered frameworks — ATLAS, SAFE-A, TRACE — each descended from a different lineage of trust primitives.

ATLAS — buyer-facing verification for AI agents

ATLAS is the buyer- and user-facing layer of the Agent Trust Framework. It answers a single question: how does someone who didn't build this agent decide whether to trust it?

Measuring agent reliability in production

Offline eval suites tell you whether your agent is good on the problems you thought to write down. Production telemetry tells you whether it's good on the problems you didn't.

SDK patterns for trusted agents

Where the SDK ends and the trust layer begins — and why putting the guardrails inside the SDK is usually the wrong default.

The state of agent benchmarks, 2026

A field guide to the benchmarks people cite, the benchmarks people ought to cite, and the gap between what they measure and what matters in production.

Welcome to Manzia Research

An introduction to Manzia's editorial line on Trusted Agents and the Agent Trust Framework family.