> For the complete documentation index, see [llms.txt](https://docs.amesa.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.amesa.com/tutorials/material-fusion.md).
# Material Fusion
This tutorial will take you through the process of building agents for a realistic use case.
1. Learn about the use case (this page)
2. [Create a simulation from data](/tutorials/material-fusion/create-a-simulation.md)
3. [Generate activity clusters and scenarios](/tutorials/material-fusion/generate-activity-clusters-and-scenarios.md)
4. [Configure and train your first agent](/tutorials/material-fusion/configure-and-train-your-first-agent.md)
5. [Build a multi-agent system](/tutorials/material-fusion/build-a-multi-agent-system.md)
6. Evaluate agent performance
Read a white paper about this use case, with detailed end to end steps from data to autonomy.
{% file src="/files/8mhXQxMzn4UJAjfGEEmo" %}
## About the Use Case
Material fusion is like welding for plastics. Plastic materials are heated and pressed together, creating a new substance.
The critical control actions are temperature and pressure. Heat makes the plastic hot enough to fuse. Pressure presses the materials together.
A quality score represents how completely and successfully the materials are fused together.
The optimization goal is to maximize the quality metric by controlling the conditions of manufacturing.
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# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.
## 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, and the optional `goal` query parameter:
```
GET https://docs.amesa.com/tutorials/material-fusion.md?ask=&goal=
```
`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.
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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.