Deep dives into Agentic Workflows, distributed systems, and the architectural rigor required to move AI from experimentation to enterprise-grade production.
In production AI, adopting unfiltered open-source models in a B2B context carries material liability that surfaces across data protection, reliability, and regulatory compliance.
In production AI, metadata is not an afterthought; it is the backbone that lets agents reason across contracts, data contracts, and real-time signals.
Yes—micro-credentialing is essential for operators and builders of agentic systems. By tying credentialing to concrete, observable tasks and auditable outcomes, organizations can scale safe autonomy without sacrificing governance.
The MVP approach to internal AI agents is a disciplined pattern for delivering a secure, measurable automation layer quickly.
The New Curriculum for production-grade AI design delivers a practitioner-first program that blends prompt engineering discipline with distributed systems engineering.
Factories are migrating from experimental GenAI pilots to production-grade agentic workflows. This article provides a practical, architecture-first blueprint.
In production AI, choosing a North Star metric is a governance decision that aligns incentives across data teams, ML engineers, product managers, and operators.
In production AI, designing for non-human users means building interfaces and governance that enable agents to operate reliably, transparently, and within business boundaries.
In the Post-SaaS world, the real value lies in decoupling user-facing interfaces from the automation engines that plan, reason, and act across ecosystems.