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Foundations for Building Agentic AI Applications

Agentic AI is fundamentally different from traditional AI. These systems maintain state, run long-lived workflows, take real actions, and operate inside real organizations with real consequences. In this video, I break down the foundational principles required to build agentic AI systems that: remain reliable beyond demos scale without unpredictable costs avoid silent drift and hidden failure modes earn trust through architecture, not hope This is not a tutorial about tools, prompts, or the latest frameworks. Those change constantly. This is about the system-level decisions that age slowly: state management and orchestration memory, context, and data grounding safety, governance, and non-human identity observability and economic guardrails If you’re building agentic AI for real users, real workflows, or real business impact, foundations are no longer optional. They are the dividing line between success and failure. This video also connects directly to the Nine Essential Skills for Agentic AI Strategists, showing how foundational thinking supports every advanced capability in production systems. 📌 Companion codebase: A production-ready full-stack foundations repository is linked in the description, demonstrating these principles in real code. https://github.com/byrdter/full-stack... If this way of thinking resonates with you, subscribe to the channel. Future lessons go deeper into the specific mechanisms that make agentic AI systems reliable at scale. Foundations first. Always.