SAN FRANCISCO, CA – October 27, 2026 — AMESA today announced AutonomyStack, an enterprise AI platform purpose-built to bring trusted, accountable and human-verified autonomy to critical industrial process controls and supply chains. AutonomyStack gives organizations an end-to-end environment for identifying high-value opportunities for autonomy, building and teaching AI agents, validating their performance through simulation, and deploying them within the infrastructure and operating environments enterprises already trust. 

The platform addresses two challenges converging across industrial organizations. Manufacturers and supply chain operators have spent decades digitizing their operations, generating enormous volumes of data across control systems, sensors, enterprise applications and other operational infrastructure. Much of that data remains underutilized. At the same time, experienced operators who know how to interpret that information and make complex decisions are leaving the workforce, taking with them expertise that is often undocumented and difficult to transfer. 

Advances in AI now make it possible to preserve and scale that expertise, but decisions in these environments affect production, quality, energy, logistics and safety. Enterprises need to validate AI performance under changing conditions and maintain clear human accountability before giving agents responsibility. 

“Industrial companies have accumulated two incredibly valuable assets: decades of operational data and decades of human expertise,” said Alistair Fulton, CEO of AMESA. “AutonomyStack brings those assets together so organizations can determine where autonomy will create real value, capture how their best people make decisions, and prove that AI can perform before giving it responsibility in a live operation. Trust in industrial autonomy has to be earned, and enterprises need the tools to establish that trust on their terms.” 

The End-to-End Platform for Trusted Operational Autonomy 

AutonomyStack supports the lifecycle of industrial autonomy, from identifying the first high-value opportunity through deployment and ongoing operation. Enterprises use it to find processes in their operational data with strong potential for measurable ROI, to capture how experienced operators make decisions, and to train AI agents that apply that expertise within defined constraints. The platform has three core components: 

• Autonomy Studio is the development environment where teams build and manage agents. Here they define the scenarios, strategies and skills that capture how experts make operational decisions. 

• AMESA Compute is the compute layer that powers agent training and operational AI workloads across enterprise environments. 

• AMESA Connect is the integration layer that links agents to enterprise data, applications and operational systems through APIs, a command-line interface and MCP. 

Agents practice in simulated operating environments, where enterprises test scenarios and measure performance against operational KPIs before any agent reaches a live process. Human experts evaluate and validate agent behavior, set the appropriate level of autonomy, and define when an agent should act on its own or escalate a decision. 

Operationalizing Data to Autonomy 

AutonomyStack was built to operationalize AMESA’s Data to Autonomy methodology, introduced earlier in 2026. The methodology uses existing enterprise data, process dynamics, business value and human expertise to identify where autonomy has the greatest likelihood of delivering measurable results. Experts then teach and evaluate AI agents in simulation, testing their strategies across changing conditions and validating performance against operational objectives before deployment into live environments. 

Built From Industrial Experience 

AutonomyStack reflects more than a decade of work at the intersection of AI, industrial operations and supply chain led by AMESA founder, CTO and Chief Product Officer Kence Anderson, a pioneer in machine teaching and autonomous AI systems. Before founding AMESA, Anderson designed more than 200 autonomous agents and worked with dozens of Fortune 500 companies across complex industrial and supply chain environments. That experience, combined with three years of AMESA working directly with major enterprises on critical operational processes, has shaped the architecture, methodology and capabilities built into AutonomyStack. 

“Experienced operators understand thousands of things about a process that will never appear in an operating manual,” said Anderson. “They know which variables matter under different conditions, which tradeoffs are acceptable, when a strategy that normally works should be abandoned, and when a decision needs human judgment. AutonomyStack gives those experts an environment to transfer that knowledge to AI and evaluate its performance in simulation until the organization has the evidence required to determine how much autonomy it is prepared to give it.” 

Designed for Enterprise Choice and Control 

AutonomyStack is designed to work with the technology investments enterprises have already made. Organizations can incorporate their preferred AI models, existing agents and scripts, digital twins and simulation platforms, enterprise systems, operational data sources and compute infrastructure. 

Organizations also choose where their agents run and where their data is stored, analyzed and used. AMESA Agent Cloud provides a fully managed option, running AMESA Compute on dedicated high-performance computing infrastructure operated by AMESA. Enterprises can also deploy AutonomyStack in their own cloud, at the edge, or on premises, keeping critical process data and operating expertise within the four walls of their own operations and inside the security and data governance environments they already maintain. 

AutonomyStack is available for enterprise deployments across industrial process controls and supply chain and logistics. More information is available at AMESA.com.