Deep dives into Agentic Workflows, distributed systems, and the architectural rigor required to move AI from experimentation to enterprise-grade production.
A/B testing ML models in production lets you quantify improvements under real user data, limit risk, and build trust with stakeholders.
A/B testing prompts in production AI treats prompts as versioned artifacts and exposes variants to defined user sessions without changing the underlying models or data sources.
SDM is not hype about replacing physical assets with software; it is a disciplined approach to orchestrate heterogeneous assets through software agents that encapsulate each device’s capabilities.
In production AI systems, managing long-context windows is a core constraint that governs latency, throughput, and cost.
Time to First Value (TTFV) in complex enterprise data platforms is not a single feature or shortcut; it’s the outcome of an integrated architecture that delivers measurable business insights quickly.
Frontend agents operate at the intersection of user intent, data access, and tool orchestration. In production, their decisions impact what users see, what data gets surfaced, and how safety and privacy constraints are enforced.
Product-Agent Fit in crowded B2B verticals is not speculative—it is the ability to run autonomous workflows in production that are auditable, governable, and demonstrably tied to business outcomes.
Scrum can thrive in AI-powered production environments when uncertainty is treated as a first-class constraint.
AI on a company website should be treated as a programmable platform, not a single feature rollout. A production-grade approach decouples AI from the core web.