About Composabl
Last updated
Last updated
The Composabl 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 Composabl, 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.
Agents train in simulations of the real system. Composabl allows multiple ways to train agents, including several cluster compute options for training at scale. As part of training, the Composabl historian allows you to evaluate agent behavior to improve the design and get better performance. Once agents are trained, the Composabl runtime connects to your system for deployment.
To get the most out of Composabl, 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:
Read the book: Designing Autonomous AI (O'Reilly, 2022)
Take the online course: Machine Teaching for Autonomous AI
