About AMESA

Using AMESA

The AMESA platform has multiple access points. To build and train agents, you can use the no-code Agent Builder Studio, designed to make agent building easy and intuitive. To integrate ML models, LLMs, algorithms, and simulations with AMESA, or to create nuanced reinforcement learning algorithms to add to your agents, use the Python software development kit (SDK) to create agent components and simulators and publish them to your projects.

AMESA platform diagram

Agents train in simulations of the real system. AMESA allows multiple ways to train agents, including several cluster compute options for training at scale. As part of training, the AMESA historian allows you to evaluate agent behavior to improve the design and get better performance. Once agents are trained, the AMESA runtime connects to your system for deployment.

Machine Teaching

To get the most out of AMESA, you can use a method called Machine Teaching to design your agents. Machine Teaching breaks down tasks into skills that the agent can acquire piece by piece. This allows intelligent agents to train quickly and efficiently, enables different technologies to control different parts of the process as appropriate, and makes AI systems accessible and explainable.

To learn more about machine teaching and how to design and build intelligent agents:

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