AgenticGeneticAssessment: A Multi-Agent Workflow-Based Framework for Genetic Health Assessment

AgenticGeneticAssessment is a prompt engineering framework based on multi-agent workflows, designed to convert genetic data into personalized health insights. Through the collaboration of 7 specialized agents, it achieves an end-to-end process from genotype analysis to clinical relevance analysis and action plan formulation. It supports the analysis of over 40 key rsID loci, covering multiple biological pathways such as methylation and neurotransmitters, and integrates PubMed to ensure traceable evidence.

In the field of AI-assisted health analysis, traditional monolithic prompts struggle to handle the complexity and multi-dimensional nature of genetic data. A comprehensive genetic assessment needs to cover multiple links such as user interviews, data analysis, and variant interpretation. A single prompt easily leads to context overflow and reduces analysis quality. Therefore, AgenticGeneticAssessment adopts a multi-agent workflow, splitting tasks for parallel processing to improve efficiency and quality.

AgenticGeneticAssessment is not traditional software, but a set of structured agent definitions, knowledge bases, and orchestration rules (adapted for VS Code and GitHub Copilot Chat). The core knowledge bases include:

• variant-reference.md: Provides rsID reference tables by pathway
• lab-reference.md: Provides confirmatory laboratory test references by pathway
• pubmed-evidence-lookup.md: A curated PMID lookup table categorized by pathway The framework supports over 40 key rsID loci, covering multiple pathways such as methylation/one-carbon metabolism and neurotransmitters.

The workflow is divided into 6 stages (including parallel initiation):

1. Parallel Initiation: The interview agent collects user goals/health history; the data engineer parses genotype files to generate cleaned indexes;
2. Variant Interpretation: The geneticist extracts variants and interprets their functions, using PubMed to verify high/medium evidence claims;
3. Clinical Correlation: The clinician integrates interview and variant data, generates phenotype-genotype hypotheses, and verifies them via PubMed;
4. Action Plan: Formulates phased (immediate/30-day/90-day) actions and laboratory recommendations;
5. Report Compilation: Integrates all outputs into a 7-chapter report;
6. Quality Audit: Checks evidence quality/safety; if revisions are needed, returns to the compilation stage.

The project uses XML format to define agent roles, input/output specifications, and handover protocols; VS Code code snippets support quick insertion of agent definitions (e.g., /interviewer); PubMed literature retrieval is implemented via MCP server configuration for evidence lookup and citation verification.

The final report includes 7 chapters:

1. Executive Summary (10-20 key findings + highest ROI follow-up steps);
2. Personalized Content (goals, constraints, symptoms, hypotheses);
3. Variant Table (pathway, gene, rsID, genotype, etc.);
4. In-depth Pathway Analysis (genetics + clinical relevance + interactions of 9 pathways);
5. Phased Action Plan;
6. Conditional Laboratory Recommendations;
7. Missing Data Needed for Follow-up.

AgenticGeneticAssessment enhances the systematicness and reliability of biomedical analysis with strong evidence traceability, and has important reference value for personal health management, clinical decision support, and preventive medicine research. As the cost of genetic sequencing decreases and LLM capabilities improve, such agent-driven tools will play a greater role in the field of precision medicine.