INSIGHTS
Deep dives on AI infrastructure architecture—real systems, failure patterns, and blueprints for resilient scale.
A methodology for identifying AI integration points within existing operational workflows. Three layers of analysis — signal flows, decision checkpoints, and infrastructure constraints — and an integration scorecard that resolves to refine, pivot, rebuild, validate, or scale.
A maturity model for taking AI from one-off pilots to durable production infrastructure. Five stages, the binding constraint at each gate, and the failure modes most teams find only after they're in production.
Four orchestration patterns — router, pipeline, debate, plan-and-execute — and the arbitration and safety layers that keep humans accountable for outcomes the agents are not qualified to own.
Practical architecture patterns for retrieval-augmented generation, the three tradeoff axes (latency, relevance, governance), and what production-grade RAG actually looks like compared to the demo version.