01. Executive Summary
The objective of this initiative was to validate agentic systems as a superior alternative to traditional statistical methods for synthetic data generation. Healthcare was selected as the pilot domain due to its high demand for data privacy and the structured nature of patient-practitioner interactions.
The Strategic Goal
Deliver a system capable of simulating complex interactions to produce realistic, standard-compliant (FHIR) data, serving as a stepping stone for the organization's pivot toward agentic workflows.
FHIR Compliant
Validated via fhir.resources
10k+ Records
Enriched Harvard Dataset
LangGraph Core
LLM-Driven Orchestration
02. Technical Architecture
The final Agentic System moved beyond the probabilistic limitations of our earlier Mesa prototypes. The quality of the simulation depended on a "Contextual Backbone" feeding into an intelligent orchestrator.
Enriched Harvard KB
10,000+ Records defining the "World State".
LangGraph Agents
LLM-driven decision making. Handles graph state memory and tool execution.
Standardized Data
FHIR-compliant JSON resources ready for ingestion.
03. Execution Roadmap
Phase 1: Validation (Rule-Based)
Established the Knowledge Base utility...
Phase 2: The Baseline (Mesa Simulation)
Developed a separate agent-based model using Mesa...
Phase 3: Intelligent Orchestration
The final evolution. Replaced statistical rules with LLM-driven Agents...
04. Outcome & Impact
Technical Success
Successfully validated that Agentic Workflows...
Ecosystem Fit
Demonstrated how agent-based modeling fits...
Ready to see the code?
The architecture is modular and ready for inspection.