How CrossnoKaye grounds physical AI with Amazon Bedrock AgentCore

Amazon Bedrock AgentCore — 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. CrossnoKaye reports over 9,000 control software deployments per year and more than 1 million run hours with zero safety incidents. Amazon Simple Queue Service (Amazon SQS) FIFO queues order every schedule and state update by facility before dispatching setpoints to the local base control narrative on the edge cluster, which CrossnoKaye reports runs 650,000 closed-loop control actions per day.

Agent or tool: Amazon Bedrock
Topic: Software Development
Reported on: Blog (AWS)
Added: 2026-11-11

Why it matters

Coding agents are moving from autocomplete to doing whole tasks: reading a codebase, making a plan and changing many files. This case is a concrete example of where that works in practice. It is one of 221 sightings filed under Software Development on Agent Sightings (number 51 by interest), and one of 81 involving Amazon Bedrock.

How you could try it

Here is a reusable prompt, written by us in our own words, that captures the task. Paste it into Amazon Bedrock or any similar agent and adjust the details to your situation.

I want a coding agent to plan first, work on a branch, and show me the diff before anything is merged. The tool in the source case was Amazon Bedrock.

Saved

Tip: Work on a fresh branch, ask for a plan before edits, and review the diff and test results before merging.

Source

Read the original first-hand account on Blog (AWS): https://aws.amazon.com/blogs/machine-learning/how-crossnokaye-grounds-physical…. Our summary is a paraphrase; details are in the source.

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