# Sample Use Cases The examples and code samples in this documentation refer to our sample use cases. These examples are real-world use cases with complex goals and constraints. In each case, the AMESA team has built agent systems that exceed the benchmark control technology by orders of magnitude. ## Industrial Mixer
### About the Use Case Learn more: [Read the AMESA whitepaper about the production scheduling use case.](https://cdn.prod.website-files.com/65973bba7be64ecd9a0c2ee8/663a811ded2167215bf3b9cf_Industrial%20Mixer%20Whitepaper.pdf) The industrial mixer use case is a realistic case study of a chemical process control agent system controlling a continuous stirred tank chemical reaction. The agent system controls the temperature in a tank where a chemical reaction occurs to create a product. As the chemicals are stirred together in the tank, the reaction produces heat at a nonlinear, unpredictable rate. If the tank isn’t cooled enough, it can reach dangerous temperatures, a condition called thermal runaway. If it’s cooled too much, not enough product will be produced. The agent system needs to balance these two goals, keeping the tank at the right temperature at every moment to optimize production while ensuring safety. ### Explore Agent System Components [Access perceptors, skill agents, and selectors for this use case.](https://github.com/AMESA/industrial-mixer-tutorial) ## Production Scheduling
### About the Use Case Learn more: [Read the AMESA whitepaper about the production scheduling use case](https://cdn.prod.website-files.com/65973bba7be64ecd9a0c2ee8/66d956fccd689331aa3ce1ba_Production%20Scheduling%20Use%20Case.pdf). The production scheduling use case is an complex production planning problem set in an industrial bakery. The agent system must determine the right amount of cookies, cakes, and cupcakes to make each day, directing teams of workers and equipment and responding to fluctuations in costs, pricing, and demand. The case study, developed in partnership with AMESA partner [Rovisys](https://www.rovisys.com/), requires the agent system to make a choice every minute between 24 possible combinations of equipment, task, employee and product, over the course of a 400-decision day, with the ultimate goal of maximizing profit. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.amesa.com/reference/sample-use-cases.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.