Build unambiguous data models as foundations for reliable agentic AI, with examples from regulated domains. Build unambiguous data models as foundations for reliable agentic AI, with examples from regulated domains. Emerging 2026 trend in agent foundations. Includes controls, pitfalls, and a phased implementation path. Build unambiguous data models as foundations for reliable agentic AI, with examples from regulated domains. Why this matters Teams are under pressure to deliver AI capability quickly, but speed without control creates operational and governance risk. This guide focuses on practical execution patterns that hold up in production. Prerequisites Clear ownership for delivery and risk decisions. Baseline observability for model and tool behaviour. Defined quality and security acceptance criteria. Practical approach Define the business decision this capability supports. Limit the first release scope to one workflow and one owner. Add measurable contro...
Practical AI engineering guidance on agents, MCP, frameworks, security, ethics, and AI in insurance — implementation-first, production-ready.