How CrossnoKaye grounds physical AI with Amazon Bedrock AgentCore

CrossnoKaye deployed physical artificial intelligence across heavy industry by connecting its ATLAS automation platform and AURA agentic system with Amazon Bedrock AgentCore. According to AWS Machine Learning Blog's report, the architecture links live telemetry with isolated microVM code interpreters, letting models verify math on plant data instead of estimating outcomes. The platform operates across more than 100 facilities and 13,000 assets, ingesting over 50 billion daily readings.

This report covers how CrossnoKaye architected its control platform on AWS and how AURA executes sandboxed Python routines across interactive chats, repeatable tasks, and automated routines. It is written for plant managers, operational technology specialists, and enterprise architects evaluating physical artificial intelligence in mission-critical facilities. The analysis examines how edge redundancy, unified equipment models, and isolated execution preserve safety while improving efficiency.

01Edge clusters and local control narratives

Industrial facilities disperse operations across programmable logic controllers, distributed control systems, and supervisory software using conflicting naming conventions. That fragmented arrangement locks state machines and safety interlocks inside proprietary controller code that external tools cannot inspect. CrossnoKaye addresses this barrier by deploying on-premises edge panels with redundant hardware running contract-first microservices on local Kubernetes clusters.

In the cloud, ATLAS authors, validates, and versions base control narratives defining operating bounds, equipment sequences, and alarm thresholds before pushing them to edge appliances. Local hardware executes these routines against chillers, crushers, ovens, and compressors, maintaining control if cloud connections drop. This architecture supports over 9,000 yearly control deployments across 1 million run hours with zero incidents.

Each edge appliance acts as an outbound-only gateway, opening zero inbound firewall ports inside the factory network. Plant telemetry streams to AWS through Amazon VPC Lattice and Amazon Managed Streaming for Apache Kafka using IAM SigV4 authentication. The platform stores over 22 trillion machine data points while protecting operational networks from external access.

02Synthetic readings and closed-loop optimization pipelines

Above base controls, CrossnoKaye runs continuous scheduling pipelines deployed from versioned Python packages in Amazon Simple Storage Service. These pipelines run synthetic reading algorithms computing derived engineering metrics, including heat-flow distributions, asset health indices, energy intensity, and mass-balance deviations. The system records these synthetic values into the historian alongside physical sensor measurements.

Advanced process control applications combine synthetic signals with physics models, model predictive control, reinforcement learning, utility tariffs, and weather forecasts to compute setpoint schedules. CrossnoKaye operates over 1,000 live advanced process control applications across industrial sites. These applications improve production throughput by 11 to 17 percent while reducing facility energy costs by an average of 21 percent.

To ensure deterministic execution, setpoint schedules pass through Amazon Simple Queue Service FIFO queues ordered strictly by facility before edge dispatch. Local base control narratives receive these verified instructions and execute them against plant machinery. CrossnoKaye reports that this infrastructure processes 650,000 closed-loop control actions every day without race conditions.

03Durable workflows and unified plant topology

CrossnoKaye hosts its AURA agentic intelligence system on Amazon Elastic Kubernetes Service, deploying it as microservices coupled with durable Temporal workflows. Because large language models cannot reliably calculate complex physical balances over millions of sensor tags in raw prompts, AURA utilizes structured platform queries. Ingress traffic passes through an Application Load Balancer and AWS WAF, validating enterprise single sign-on or Model Context Protocol credentials.

AURA inspects the operational state of a facility through the internal Atlas Data gateway within a single security boundary. This interface exposes equipment topology, active interlocks, deployed base control logic, physical sensor readings, and synthetic metrics. Agents also reference historical alarm records, setting modifications, and facility documentation in Amazon Bedrock Knowledge Bases.

Queries route through an internal Model Gateway bounded by Amazon Bedrock Guardrails, restricting reasoning to validated operational contexts. Integrating equipment topology directly with control narratives allows the agentic system to evaluate machinery interactions rather than viewing sensors as disconnected streams. This grounded contextual access enables AURA to trace complex industrial anomalies back to underlying mechanical relationships.

04Rock crusher diagnostics and interlock tracking

The operational advantage of unifying equipment layout with control interlocks appeared during a disruption at an aggregates quarry operating primary, secondary, and tertiary rock crushers. The back half of the crushing circuit tripped offline twice during one shift. Control operators observed secondary crusher fault alarms, cleared the notifications, and restarted operations, only for the line to trip again hours later.

Historian timestamps showed the secondary crusher faulting seconds after the tertiary crusher, leading operators to suspect an internal crusher failure. Because AURA analyzed conveyor topology alongside deployed base control interlocks, its investigation traced the actual sequence. A recirculating belt beneath the tertiary crusher was slipping, which triggered that unit's overload interlock.

The tertiary interlock's upstream safety control immediately shut down the secondary crusher to avoid burying the stalled downstream crusher in stone. Rather than troubleshooting the secondary crusher, plant managers sent operators directly to the recirculating belt. The crew confirmed and repaired the belt slippage in minutes, preventing further line shutdowns.

05Isolated microVM sandboxes and guarded runtime execution

When engineering investigations require mathematical balances, thermodynamic evaluations, or multi-dataset statistics, AURA streams data over gRPC to an internal Sandbox Orchestrator. In interactive sessions, the orchestrator tracks active session identifiers and streams live progress updates through Amazon ElastiCache for Valkey. The system coordinates these executions across interactive chats, repeatable diagnostic tasks, and automated monitoring routines.

The Sandbox Orchestrator invokes Amazon Bedrock AgentCore runtime endpoints using the AWS SDK, staging datasets and running model-authored Python code inside isolated microVM sandboxes. Running scripts inside these AWS-managed virtual machines prevents untested code from affecting plant operations while ensuring arithmetic accuracy. The sandbox executes verifiable calculations over sensor readings, providing engineers with checkable, math-backed answers instead of probabilistic language model guesses.

Deploying Bedrock AgentCore sandboxes alongside Amazon Bedrock Guardrails and local edge controls ensures that artificial intelligence recommendations cannot bypass physical safety interlocks. Plant operators can verify computed thermodynamic and statistical findings directly before applying recommendations. By anchoring artificial intelligence in deterministic control code and sandboxed runtime computation, CrossnoKaye delivers a repeatable framework for physical automation in heavy industry.