AMESA rebrands, expands platform for AI agent training and simulation

Everyone's racing to build AI agents. But here's what most companies are missing: agents that actually know what they're doing before they touch production systems. AMESA (formerly Composabl) just announced their rebrand and expanded platform—and they're tackling the practice problem. While other platforms focus on connecting agents, AMESA gives agents a proving ground where they can learn, fail safely, and gain expertise through simulation and feedback before deployment. Think about what happens when an AI agent makes a mistake in a chemical plant, manages energy distribution across a power grid, or controls warehouse robotics during peak season. Enterprises don't need more agents—they need agents that perform reliably in complex, high-stakes environments where errors cost millions or put safety at risk. AMESA's approach comes from years of proven work in industrial automation, where mistakes are expensive and reliability isn't optional. Their platform lets agents train against digital twins and real-world complexity, then links performance directly to operational metrics like yield, energy efficiency, and downtime. The gap between building an agent and deploying one that performs is where most enterprise AI initiatives stall. AMESA bridges that gap with infrastructure that treats agent competence as seriously as agent connectivity. Learn more: https://lnkd.in/gX_CZahx #AIagents #EnterpriseAI #Automation #MachineLearning #RidgelineBacked Andrew McMahon Kence Anderson

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Kence Anderson

Deploying Multi-Agent AI Systems for Fortune 500 Since 2017

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I couldn’t have said it better myself, Ridgeline! If your #ai agents can be trusted to perform high-value, safety-critical skills and functions, they can be trusted to do anything in your enterprise. And, visa-versa.

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