1. An AI agent analyzes the dataset and identifies potential relationships between patient age, glucose levels, and insulin measurements. The agent evaluates whether an

   "Age Based Glucose Insulin Trends"

   pattern exists based on the available data.

2. Conditional Evaluation Step:

   A conditional node checks the agent’s output to determine whether

   "Age Based Glucose Insulin Trends"

   are identified as a relevant finding.

3. Agent Explanation Step:

   If the trend is identified, a second AI agent provides an explanation of why age-based glucose and insulin patterns are present, describing the observed relationship and supporting evidence.If the trend is not identified, the agent explains why this trend was not included, based on the lack of significant patterns or insufficient evidence in the dataset.