Launching today

Theseus Guard

Launching today

The World's First Stateful AI Governance Platform

2 followers

The World's First Stateful AI Governance Platform

2 followers

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Compliance software

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AI Infrastructure Tools

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Security software

Theseus Guard is a stateful AI governance platform built for the Agentic Internet. Unlike stateless guardrails that evaluate interactions in isolation, it tracks agent identity and behavior across multi-turn sessions, nested agents, and lifecycle changes. It combines real-time runtime enforcement, behavioral drift detection, and tamper-evident cryptographic provenance—so enterprises can govern autonomous AI continuously, enforce policies at inference time, and prove what happened.

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Launch tags:Privacy•Artificial Intelligence•Security

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LightfieldAI-native CRM that builds itself and does work for you

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Hey folks, We built Theseus Guard around a problem we kept seeing with AI agents: the more autonomous they become, the harder it gets to understand what they’re doing, enforce the right policies, and establish trust. Most existing approaches look at individual prompts or interactions. We take a different approach by continuously tracking agent identity and behavior across its lifecycle, enforcing policies at runtime, and providing verifiable provenance of what happened. We’re launching Theseus Guard on Product Hunt today and would genuinely love your feedback. If you’re building or deploying AI agents, what’s the hardest part of keeping them secure and under control?

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Product Story

In 2025, I was digging into attack vectors in the AI-for-AI space, and something didn't add up: every "solution" I found could be broken with a fragmented, multi-step attack. That sent me down a rabbit hole.

I dug into the internal guardrail architectures of open-source LLMs. For a while I assumed I just hadn't found the right product yet surely someone had solved this. So I started talking to governance and AI security experts. They showed me what enterprise vendors were shipping. The problem was always the same: risk detection stayed locked to the rules defined at setup. Nothing self-corrected. Nothing adapted across departments or use cases. The system either blocked everything (killing productivity) or someone had to manually retune the rule set constantly (not sustainable at scale).

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