We built Lamdis because we kept seeing the same pattern: teams would ship an AI agent that looked great in demos, then it would fail in the messy real world. Not with obvious stuff like profanity, but with subtle safety and compliance issues: misleading guidance, unauthorized advice, missing disclosures, risky escalation handling, or “helpful” behavior that crosses a line. And once agents started taking actions, calling tools, or touching real systems, the stakes jumped fast. When something went wrong, there was rarely a clean way to reproduce it, measure it, or prove what happened for audit and incident response. The problem we’re solving is bigger than chat. AI is moving into real workflows and real environments: customer support, lending, healthcare ops, security, but also robotics, IoT, and cyber physical systems where an agent can trigger actions in the world. If an agent can open a ticket, move money, change a setting, unlock a door, or control a device, you need the same thing you need in regulated software: control, evidence, and accountability. Our approach evolved during the build. We started with basic prompt testing and quickly realized it wasn’t enough. You need a repeatable system: policies turned into checks, consistent runs with transcripts and scoring, and then a production layer that continuously validates real interactions and action traces. That’s why Lamdis became two motions: Runs for shift left testing, and Assurance for production validation and reviewer workflows. The goal is simple: prove what the agent did, why it did it, and whether it followed the rules, across any environment where AI operates.

Lamdis

Lamdis is the control plane for AI in the real world.

Lamdis is the control plane for AI in the real world.