how to build an agent in copilot

Hello Lei Ying,
An AI agent for disease evaluation can be designed to systematically collect structured and unstructured patient data—such as symptoms, risk factors, demographics, and lab results—and process this information through a combination of rule-based logic and AI-driven reasoning to assess disease severity or likelihood. The system uses predefined scoring criteria to assign numerical values to specific inputs, aggregates them into an overall risk score, and classifies the outcome into categories like low, medium, or high risk. It can further enrich results with AI-generated explanations to improve interpretability and trust. The final output is presented as a standardized scorecard, including the total score, risk level, justification, and recommended next steps, which can be automatically formatted into reports (e.g., Word or PDF) for easy review. This approach ensures consistency, explainability, and scalability while supporting decision-making workflows.

You might consider Foundry and Copilot Studio as two alternative approaches for building your agent. Foundry is an enterprise data platform designed for integrating, cleaning, and analyzing complex datasets at scale, ideal for teams handling large or heterogeneous data. Copilot Studio might be more suitable for your specific use case (depending on how comfortable you are with programming agents). It is a low-code environment that allows non-programmers to ingest data, generate calculations, summaries, dashboards, and scorecards using natural language prompts without writing code.

In Copilot Studio, the high-level steps are to first connect your disease data sources (Excel files, Power BI datasets, or external APIs) and then clean, normalize, and calculate key metrics such as incidence, severity, and treatment effectiveness.

More at https://learn.microsoft.com/en-us/microsoft-copilot-studio/

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hth

Marcin

An AI agent that takes user symptoms, age, and duration, then analyzes the data using AI and trusted medical sources to generate a risk scorecard (Low/Medium/High risk) with basic health suggestions.

It helps users understand possible risk levels, not medical diagnosis.