Monitor Decision

Prerequisites: Before you can monitor decision executions, decisions must be executed via API calls or through Workflows.

Decisions are typically triggered through API calls or through Workflows. See Regression Test Decision for testing decision logic with test suites, or execute decisions through Workflows or API Execution.

Decision monitoring is the process of tracking the execution of decision logic in real time. It allows users to follow the status of each step, view decision traces, and identify where errors occur within a process. This ensures transparency, accountability, and faster troubleshooting.

Why is it Important?

• Improves visibility: Provides a clear view of decision outcomes.
• Speeds up troubleshooting: Helps pinpoint exactly where an error occurs in the flow.
• Ensures accountability: Allows teams to audit decision logic and track execution history.
• Supports reliability: Builds confidence that decisions are functioning as expected.

How It Works

• Track Execution: Monitor decision service and its outcome for every execution.
• Trace Decisions: Record rule evaluations to understand why a decision was made.
• Identify Errors: Highlight the exact step or rule where failures occur.
• Review and Resolve: Use monitoring insights to correct errors.

Monitoring Decision

In the demo, users can view the entire decision flow at a glance, seeing how each step progresses. When they want to investigate an issue, they can click on a specific node to jump directly to the execution row and review its execution details.

From there, users can also open the node settings to drill down further, viewing the exact configuration and decision logic that was applied. This makes it easy to trace issues, understand errors, and validate how rules are executed in context.

Next Steps

Once you’ve monitored and verified your decision executions in staging:

Perform Regression Testing - Before publishing changes, validate that your decision modifications have not broke existing behaviours. Regression testing helps detect unintended issues, maintain quality, and build confidence before publishing changes to production.