Deep dives into Agentic Workflows, distributed systems, and the architectural rigor required to move AI from experimentation to enterprise-grade production.
Load testing concurrent LLM users is not optional in production AI systems. It reveals how latency, reliability, and governance behave when real user load hits the model and the coordinating services.
Low-code toolchains enable AI product teams to ship reliable, auditable agentic workflows without writing every integration from scratch.
Enterprise AI deployments demand distilled models that preserve essential task competencies while delivering predictable latency, strict data locality, and robust governance.
Practical Multi-Agent Systems for Global explains practical architecture, governance, observability, and implementation trade-offs for reliable production systems.
Platooning agents are production-grade, cooperative decision-makers embedded in a modern fleet to improve fuel efficiency, safety, and asset utilization.
Practical playbook to adapt AI at work explains practical architecture, governance, observability, and implementation trade-offs for reliable production systems.
Prompt injection vulnerabilities threaten production AI systems by letting adversarial inputs influence model behavior, override guardrails, or leak sensitive context.
SFDR Article 8 and 9 disclosures are best treated as production-grade data products, not a single regulatory memo. By codifying data contracts, automating.
In production AI, metrics are only as trustworthy as the craft that creates and validates them. Skill files—reusable AI-assisted development assets—provide guardrails that prevent metrics from being gamed, misinterpreted, or drifted due to deployment changes.