Integration Proposal: SDC Agents — Deterministic Semantic Data Artifacts from Enterprise Datastores

[twcook]

🔴 Required Information

Is your feature request related to a specific problem?

ADK developers working with enterprise data sources (SQL databases, CSV, JSON, MongoDB, BigQuery) lack tooling to produce deterministic, W3C-compliant semantic data artifacts. Current integrations focus on retrieval and generation — none produce validated schemas (XSD, SHACL, JSON-LD) or signed XML instances from structured data.

Describe the Solution You'd Like

We maintain SDC Agents (sdc-agents on PyPI), a suite of 9 purpose-scoped ADK agents with 32 tools that transform enterprise data into validated, multi-format semantic artifacts.

What it does:

• Introspect legacy datastores (SQL, CSV, JSON, MongoDB, BigQuery — read-only) and extract structure
• Discover published schemas from a catalog of 6,400+ components (FHIR, NIEM, NIH CDEs, X12, SUS, CIHI)
• Map source columns to semantic components with ontology links
• Generate XML instances from mapped data
• Validate and sign instances via a validation-as-a-service API
• Distribute artifact packages to triplestores (Fuseki, Neo4j, GraphDB), REST APIs, and filesystems
• Assemble new data models from component libraries

Architecture:
Each agent is an LlmAgent with a single BaseToolset. No agent has both datasource access and network access (security isolation by design). All tools are async, audited (JSONL), and cache-aware.

Usage:

from sdc_agents.agents import create_introspection_agent, create_catalog_agent

# Introspect a PostgreSQL database
introspect = create_introspection_agent()

# Search the published catalog of 6,400+ semantic components
catalog = create_catalog_agent()

Already supports:

• ADK BaseToolset / FunctionTool / LlmAgent patterns
• MCP export via adk_to_mcp_tool_type()
• Docker image, CLI, PyPI package (pip install sdc-agents)
• 184 tests, 82% coverage

We propose contributing:

1. A thin wrapper module under contributing/samples/ with usage examples
2. A documentation page for the adk-docs integrations directory

Impact on your work

Enables ADK agents to produce deterministic, standards-compliant data schemas and validated instances from enterprise data sources — closing the gap between agentic AI and formal data governance. Targets healthcare, government, financial, and research domains where data provenance and schema validation are mandatory.

Willingness to contribute

Yes — we have the integration ready. Corporate CLA for Axius SDC, Inc. is signed (2026-03-11).

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🟡 Recommended Information

Additional Context

• PyPI: https://pypi.org/project/sdc-agents/
• GitHub: https://github.com/SemanticDataCharter/SDC_Agents
• User Docs: https://github.com/SemanticDataCharter/SDC_Agents/tree/main/docs/user
• Standards: W3C (XSD 1.1, RDF, OWL 2, SHACL, SPARQL), ISO 21090, ISO/IEC 21838-2 (BFO 2020)
• Production platform: SDCStudio (deployed on Google Cloud Run)