AI Native Architecture: Intelligence by Design (sumant.bearblog.dev)

The piece argues that the era of “bolting on” ML is ending and promotes an AI‑native architecture — systems designed from the ground up with intelligence as a first‑class citizen. Rather than retrofitting models, AI‑native designs embed continuous learning, semantic layers (tokenization, embeddings, vector stores), real‑time inference, feedback loops and model lifecycle management into the core architecture. Ericsson and Splunk are cited to frame AI as pervasive and trustworthy across design, deployment, operation and maintenance. The article also provides a practical roadmap: clarifying which components should be adaptive, building ingestion-to-vector pipelines, planning for where inference runs (cloud, edge, hybrid), and instrumenting robust retraining, versioning and rollback mechanisms. This shift matters because GenAI workflows (LLMs, RAG, multimodal inference, agent orchestration) require end‑to‑end data/knowledge flows, specialized compute (GPUs/TPUs/accelerators), fast vector search, and governance baked in from day one. Key technical implications include designing for distributed and composable inference, observability for model drift/bias, low‑latency edge options, cost and COGS management, and phased migration strategies for legacy systems. The tradeoffs are increased complexity, operational risk and cost, but the payoff is platforms that continuously adapt and deliver AI as an intrinsic capability rather than an afterthought.