Deep dives into Agentic Workflows, distributed systems, and the architectural rigor required to move AI from experimentation to enterprise-grade production.
In production AI, duplicated business logic is a silent drag on velocity and governance. Rewriting the same prompt contracts, evaluation criteria, and safety checks for every service creates drift, increases risk, and slows delivery.
In production AI, immutable compliance evidence means every decision, data point, and model action is recorded in an append-only log.
AI can enforce zoning and building code compliance at scale by encoding regulatory rules into machine-checkable constraints, ingesting authoritative data, and executing checks in production-grade pipelines.
Building a market radar for emerging technologies is a production-grade exercise that blends data engineering, graph-based knowledge representation, and governance to deliver timely decision-ready signals.
Autonomous agents are software systems that perceive inputs, reason over available signals, and take actions with minimal human intervention.
Big 4 firms are moving from episodic reports toward continuous, auditable oversight. By orchestrating agentic workflows - observation, reasoning, planning.
In production AI systems, test discipline is a competitive advantage. Skill files—reusable AI-assisted development assets—codify testing logic into templates that AI assistants consult during build and deploy.
Codex and other AI assistants shine when they can see the actual shape of your repository. In production AI workflows, the directory layout, configuration files, and service interfaces define how data flows, how models are tested, and how deployments happen.
Cursor rules act as programmable guardrails for AI-assisted development, constraining how endpoints are generated, wired, and deployed.