Gartner says 40% of enterprise apps will embed AI agents by end of 2026. But here’s the thing nobody’s talking about: agents aren’t just better tools—they’re a new category that breaks how we organize work. The MIT/BCG 2025 research reveals four tensions every leader will face, and most aren’t ready.
Natural language interfaces for data are no longer experimental—they’re becoming essential enterprise tools. But building a reliable, production-grade data agent requires moving beyond simple prompt engineering. This post shares architectural lessons learned from building an enterprise natural language to SQL (NL2SQL) agent. The Problem with Pipeline Architectures Most NL2SQL systems start with a pipeline approach: User Question → Intent Detection → RAG Retrieval → SQL Generation → Validation → Response This works for demos but creates problems at scale: ...
The Wall Street Journal recently reported on the rapid rise of Claude Code and how quickly it has spread beyond the usual circle of AI enthusiasts. That article should cause many CEOs and technology leaders to pause and reconsider the AI strategies they have already committed to. For the last couple of years, most enterprises have followed a very familiar playbook. They selected an approved corporate platform, standardized on a vendor stack such as Gemini Enterprise or Microsoft Copilot, launched formal transformation programs, and brought in outside partners to implement carefully defined use cases. This approach made sense because it mirrored how organizations adopted cloud computing, analytics, and ERP systems. It was orderly, governable, and predictable. ...