Insights
Thinking about the future of enterprise intelligence.
Perspectives on AI engineering, data platforms, agentic systems, infrastructure, governance, observability and the systems required to make enterprise intelligence operational.
AI Engineering
01Designing enterprise AI systems that can actually operate at scale
Enterprise AI becomes difficult when models move from experiments into production. The architecture must account for data, tools, agents, governance, evaluation and observability as one operating system.
Agentic AI
02From LLM applications to reliable agentic systems
Moving from a conversational application to an autonomous workflow requires explicit execution, tool, memory, evaluation and governance layers.
Observability
03Why AI observability becomes critical as agents become autonomous
As AI systems gain the ability to retrieve information and take actions, conventional application telemetry needs to be extended with AI-specific operational signals.
Data Engineering
04The convergence of modern data platforms and AI infrastructure
Enterprise AI depends on the same data foundations that support analytics, but intelligent applications introduce new requirements around context, freshness, provenance and access.