Deep dives into Agentic Workflows, distributed systems, and the architectural rigor required to move AI from experimentation to enterprise-grade production.
Dynamic retrieval is a systemic capability, not a feature toggle. In production AI where agents plan, reason, and act on streaming data, fixed Top-K retrieval yields inconsistent relevance as data, contexts, and goals change.
Real-time port congestion is a stubborn constraint in modern freight networks. The answer isn't a wholesale rewrite; it's a disciplined, agentic approach that threads perception, decision, and action into a responsive loop.
Dynamic Tool Selection with LLMs: Routing explains practical architecture, governance, observability, and implementation trade-offs for reliable production systems.
Dynamic pricing for inventory in production environments is no longer a set of ad hoc rules. It requires fast data, strong governance, and auditable, reversible decisions.
Edge-native driver coaching unlocks real-time safety gains by keeping computation close to the vehicle, reducing latency, and enabling rapid policy iteration.
Edge AI agents that run small language models locally provide privacy-by-design, lower latency, and resilience for critical workflows.
Edge AI for robotics demands that perception, planning, and action complete within tight timing budgets.
Edge AI orchestration enables reliable, policy-driven control of fleets of software agents across distributed industrial environments.
Small language models on the edge unlock real-time language understanding and decision-making without sending raw data to centralized clouds.