> 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/configure-and-train-your-first-agent.md).
# Configure and Train Your First Agent
Once you have set up your use case with scenarios, your next step is to configure and train your first agent.
## Configure Agents
In the Agent Orchestration Studio, you can configure agents with goals, constraints, and success criteria.
### Configure Agents with the No-Code UI
Agents can pursue three goal objectives: maximize, minimize, and maintain.\
Goal statements can apply to any sensor variable.
For the Material Fusion example, the overall goal of the process is to maximize the quality rating variable. So your agent should be configured with one goal: maximize quality.
For material fusion, you do not need to use constraints or success criteria.
### Use Coding Agents to Create Agents
Alternatively, you can use the coding agent workflow to create and configure agents for the Material Fusion use case. Prompt your coding agents to create an agent with a goal to maximize the variable quality\_rating and publish it to your organization and use case.
> Create a teacher agent with a goal to maximize quality\_rating. Include all required files and dependencies. Use the MCP server to publish it to \ and \.
After the MCP server publishes the agent, it will be visible in your list of agents in the Agent Orchestration Studio.
## Build a Single-Agent System
Eventually, you will build a multi-agent system with a different agent for each scenario. For now, you will build a single-agent system with one agent that learns to specialize in one scenario.
Drag your agent into a blank template within the Agent Orchestration Studio. Then select one scenario from the scenarios list on the right.
## Train Agent
Click Train Agent to begin training. For this problem, select 50 training cycles. (For your own industrial control use cases, you will likely need more cycles.) Each training cycle is about 1,000 decisions.
On the Training Sessions page, you'll see the training in progress. A successful training session shows a learning curve with a steep initial climb followed by a plateau, indiciating that the agent has successfully learned.
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# Agent Instructions
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