Set Goals, Constraints, and Success Criteria

The performance goal is the most important KPI or metric you will use to evaluate your agent system's success. This goal directs your agent as it trains. The AI learning technology within the agent will reward the agent system when it gets closer to the goal, helping it to improve.

For many business use cases, the top-level goal will be expressed in terms of profit or ROI. There are many factors to consider in ​the production scheduling use case, but profit is the ultimate goal, so it is the most important KPI.

Some use cases will have multiple goals the agent system must meet simultaneously. In fact, the need to balance more than one goal is a sign that a use case is a good fit for Machine Teaching. In the industrial mixer use case, the agent system needs to get the right chemical product concentration and keep the tank's temperature from becoming dangerously hot.

Use the Guidance Library for Reusability

Composabl now supports a Use Case Guidance Library—a reusable set of:

• Goals

• Constraints

• Success Criteria

This feature allows you to save commonly used elements and apply them across multiple skill agents. Instead of recreating the same definitions every time, you can quickly load goals, constraints, and success criteria from your shared library.

Use this to:

• Maintain consistency across agents

• Speed up setup for similar workflows

• Capture expert-defined logic once and reuse it across Skill agents

Setting Goals, Constraints, and Success Criteria in the UI

To edit a goal created by the copilot or to create a new goal, follow these steps:

1. Navigate to the use case page

2. Click Set up goal and enter a name and description for your goal.

3. Click Add condition to define the goal.

Use the dropdown menus to select the variables and then define the parameters for each goal, constraint, or success criterion you want to include.

Configure Goals

Goals define what a skill agent system should do. Goals apply to one of the sensor variables and are defined using one of three possible directives:

• Maximize: Maximize the value of the variable

• Minimize: Minimize the value of the variable

• Maintain: Keep the variable at a specified value

For example, for the industrial mixer, we want to maximize the concentration of the product, Ca.

Configure Constraints

Constraints set the boundaries for the skill agent. They define rules that the agent system must follow during operation. Constraints are defined using one of two possible directives:

• Avoid: The skill agent learns to keep the variable from reaching a specified value or range through withholding rewards

• Terminate: When the actions of the skill agent lead to certain conditions within a variable, the skill agent has failed and must stop and start a new episode

For example, we want to avoid the temperature, T, getting above 400 degrees Kelvin in the industrial mixer use case.

Configure Success Criteria

Success criteria tell the skill agent when it's doing something right. They are defined using one of two possible directives:

• Approach: The skill agent learns to get close to a specified value by getting an increased reward

• Succeed: When the success criteria are achieved, the session ends, and a new one begins so that the skill agent can keep practicing and learn to win every time

Using Goals, Constraints, and Success Criteria for Multi-Agent AI System Design

Goals, constraints, and success criteria transform complex AI agent building into an intuitive process that eliminates the need for programming expertise.

You directly apply your domain knowledge without coding by simply defining what you want the agent to achieve and what conditions to respect. This approach reduces development time while ensuring AI agents embody real-world operational wisdom, bridging the gap between technical AI capabilities and practical industrial knowledge in complex physical environments.