Policy & Data Boundaries

Sovereign AI is about effective control over models, data, infrastructure, software, operations and strategic dependencies — not simply where an AI model is hosted.

Model Context Protocol connects AI applications to external tools, resources and prompts through a standard client-server boundary. Learn what MCP does, what it does not do, and where it fits in agent architecture.

RBAC controls what a user may do; tenant isolation controls which tenant’s resources that action may reach. Learn why multi-tenant SaaS security requires both boundaries.

A Source of Truth defines which source is authoritative for a specific fact or state. Learn how it differs from RAG, provenance, memory, context, vector databases and systems of record.

AI governance defines who can approve, operate, change and audit AI systems across models, providers, data, permissions, risk, evaluation and the full lifecycle.

An AI Platform Architect designs reusable AI foundations across models, providers, retrieval, agents, identity, security, evaluation, observability and operations.

An AI Solution Architect turns business requirements into a production-ready AI system across data, models, tools, security, runtime, evaluation and operations.

Generative AI is more than a model. Learn how models, retrieval, tools, context, runtimes and applications fit together in production AI systems.

Agentic AI uses models inside multi-step execution loops where they can choose tools, observe results, update state and adapt their next action within explicit runtime and permission boundaries.

Enterprise AI architecture explains how AI changes company systems across data authority, identity, permissions, providers, risk, governance, evaluation, compliance and operations.

MCP, A2A, UCP, AP2 and A2UI are often presented as competing agent standards. They mostly solve different interoperability problems. This guide maps each protocol to the boundary it actually standardizes—and shows how they can work together in one production system.

An AI agent can cite sources and still use the wrong evidence. This article introduces a practical method for checking claim support, source authority, applicability, provenance, and whether the evidence actually influenced the answer.