Zynomi Agent Fabric (ZAF)
Zynomi Agent Fabric (ZAF) — the platform a fleet of Zynomi agents runs on, in every department, grounded in a clinical knowledge engine built from a clinical ontology and GraphRAG, with every agent output carrying provenance and an accept/reject disposition.
ZAF is the platform Zynomi is building through 2026 — the AI-native rebuild year. This page describes architecture and design intent, not shipped features. Nothing on this page is generally available yet.
Four architecture facts
| Fact | What it means |
|---|---|
| Agent + human approval | Every workflow pairs an agent with a human approval step. Agents do the data work; humans approve every result. |
| Ontology + GraphRAG grounding | Agents are grounded in a clinical knowledge engine built from a designed clinical ontology and GraphRAG over a temporal knowledge graph — not free-form generation. |
| Semantic layer + MCP interface | The semantic layer (governed metrics) and the Model Context Protocol are the cross-system interface. Tools compute; the model narrates. |
| Provenance + disposition | Every agent output carries provenance (what it was derived from) and an accept/reject disposition recorded by the reviewing human. |
The third data layer
Clinical platforms already have two data views. ZAF adds a third:
| Layer | Question it answers | Store |
|---|---|---|
| Transactional | What is happening now? | OLTP (Frappe / PostgreSQL) |
| Analytical | What happened, at scale? | Lakehouse + semantic layer (dbt / Cube.dev) |
| Temporal knowledge graph | What is connected, how did it evolve, and why? | Graphiti → FalkorDB |
The graph is built from the audit trail: business events flow through a change-data-capture (CDC) stream into Graphiti, which extracts and resolves entities and relationships into a FalkorDB temporal graph. Every edge carries valid-from / valid-to timestamps, so the graph can answer as-of questions — for example, an auditor's "explain everything around Amendment 4" in one traversal.
Designed ontology, not inferred
The ontology is two-tier and owned by the enterprise, populated by AI:
- Tier 1 — first-class trial entities (study, site, subject, visit, form, query…)
- Tier 2 — entities promoted from the graph: Protocol, Amendment, Change Request, Review, Approval, Deviation
CDISC SDTM/ODM exports are modeled as export-artifact nodes with derived-from provenance back to the source records.
A fleet of bounded agents
A supervisor agent owns no data tools. Bounded specialist agents each see exactly one engine, and are read-only by construction:
| Agent | Engine it sees |
|---|---|
| Trial Metrics Analyst | Semantic layer (governed metrics) |
| Study Historian | Temporal knowledge graph |
| Operations Agent | Transactional reads |
| Data Steward | Data quality and lineage |
Agent runtime targets Amazon Bedrock — consistent with the platform's AWS-only hosting.
Related
- Technology Stack
- Cube Semantic Layer
- MCP Server
- Glossary — definitions for ZAF, GraphRAG, temporal knowledge graph, and related terms