How CrossnoKaye grounds physical AI with Amazon Bedrock AgentCore

What happened

CrossnoKaye took a different approach to bring physical AI to heavy industry using Amazon Bedrock AgentCore . Its software-defined automation platform, ATLAS, connects live plant data and AI reasoning to closed-loop control of the equipment running food and beverage plants, industrial refrigeration facilities, aggregates quarries, and manufacturing sites.

Its ATLAS platform and AURA agentic system ground AI reasoning in live plant data and run verifiable Python in isolated AgentCore sandboxes, giving plant engineers checkable, math-backed answers instead of model guesses. Industrial operations present AI challenges that don’t show up in typical enterprise software.

Bolting an external AI agent onto that stack rarely works well in practice. Each system operates with its own naming conventions.

The logic that actually governs how equipment behaves (the control sequences, state machines, and safety interlocks) is locked inside proprietary controller code that an external tool can’t inspect. Giving a bolt-on agent access across multiple operational technology (OT) systems also means distributing credentials across the plant network.

Artificial Intelligence Industrial operations present AI challenges that don’t show up in typical enterprise software. Because ATLAS already manages the facility model, the base control software, the historian, and the optimization applications in one platform on AWS, CrossnoKaye’s agentic intelligence system, AURA, has native access to the full operational state of the plant within a single security boundary.

Having the plant context is only half the problem, though. Large language models (LLMs) are good at interpreting engineering context and planning an investigation, but they cannot reliably compute statistics or physical balances over millions of sensor readings in a prompt.