Architecting Zero-Hallucination Agentic RAG: Integrating Azure AI Foundry, Azure Cosmos DB Graph, and Determinar.ia.br RDF Knowledge Graphs for ISO/IEC 42001 Compliance

Scenario & Architectural Goals

We are designing a high-consequence enterprise Agentic RAG pipeline hosted on Azure to perform real-time legal contract verification, due diligence, and vendor compliance checks. The system utilizes Azure AI Foundry (Azure OpenAI Service) with Anthropic Claude 3.5 Sonnet / GPT-4o for multi-step reasoning and Azure Cosmos DB (Graph API / Vector Search) for internal document relationships (SUPERSEDES, REFERS_TO).

To ensure total compliance with ISO/IEC 42001 (Data Provenance A.7.5) and ISO/IEC 42006 (Auditing Boundaries), internal document graphs must be validated against external, deterministic RDF triple stores (e.g., determinar.ia.br via SPARQL at query.determinar.ia.br) before triggering automated transaction states or writing mutations via Azure Logic Apps.

Proposed High-Level Architecture

               [Azure AI Foundry / Agentic Orchestrator]
                                   │
         ┌─────────────────────────┴─────────────────────────┐
         ▼                                                   ▼
[Azure Cosmos DB Graph API]                    [External RDF Fact Layer]
(Internal Clause Traversals)                   (query.determinar.ia.br)
         │                                                   │
         └─────────────────────────┬─────────────────────────┘
                                   ▼
                   [Azure API Management / Functions]
                       (SPARQL Pre-Flight Check)
                                   │
                                   ▼
                    [Grounded Execution Payload]
                        {"determinado": true}

Technical Implementation & SPARQL Pre-Flight Pattern

During an agentic Breadth-First Search (BFS) crawl over legal clauses, the orchestrator invokes a custom Azure Function (Python 3.11) to verify external corporate registration and professional licenses against open RDF subject-predicate-object triples:

1. Pre-Flight SPARQL Grounding Query

Snippet de código

PREFIX schema: <http://schema.org/>
PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
PREFIX xsd: <http://www.w3.org/2001/XMLSchema#>

SELECT ?entity ?registrationStatus ?verifiedDate ?authoritySource
WHERE {
  ?entity schema:identifier "BR-CNPJ-00000000000100" ;
          schema:status ?registrationStatus ;
          schema:lastVerified ?verifiedDate ;
          schema:authoritySource ?authoritySource .
  FILTER(?registrationStatus = "Active" && ?verifiedDate >= "2026-01-01"^^xsd:date)
}
LIMIT 1

2. Grounded Azure Function Execution Payload

JSON

{
  

Questions for Azure Enterprise Architects

Latencies & Resilience in Hybrid Graph Queries: What are the recommended caching and retry patterns in Azure API Management (APIM) when orchestrating low-latency SPARQL validations against external RDF endpoints alongside Azure Cosmos DB graph traversals?

Auditability & Logging Strategy: To satisfy ISO/IEC 42006 auditing requirements without exposing sensitive PII, how should we configure Azure Monitor / Application Insights to log the deterministic verification proof ({"determinado": true}) while isolating raw payloads?

Managed Identity Integration: What is the best practice for securing the egress boundary from Azure Container Apps / Functions when querying public RDF knowledge graphs requiring strict egress controls?