Trust Infrastructure for AI Agents

Six projects in two months. All focused on one problem: AI agents that say one thing and do another, and nobody's checking.

An auditing framework that compares what an AI agent claims it's doing against what it actually does. It watches the gap between stated intent and real behavior — the space where trust breaks down.

Most agent frameworks assume good faith from the agent. RoboTruth assumes nothing.

Red-teaming for agentic workflows. Sentinel stress-tests AI agent pipelines by probing for failure modes, misalignment, and edge cases that only surface under adversarial conditions.

Built to answer the question nobody wants to ask: what happens when the agent is wrong and confident about it?

An open-source agent CLI. Aion strips the agent interaction model down to the terminal — no GUI, no dashboard, just direct command-line control over agent behavior.

Designed for developers who want to build, test, and deploy agents without the overhead of a full platform.

Every project circles the same core problem: trust. Not trust as a marketing word, but trust as an engineering constraint. How do you verify that an autonomous system is doing what it says? How do you catch it when it isn't? How do you build the infrastructure so that verification is the default, not an afterthought?

The stack is Python end to end. The philosophy is open source. The assumption is that if you can't audit it, you can't trust it.