From LLM Experiments to Enterprise Agentic AI: Scaling Vision-Language Models, Digital Workers, and Multi-Agent Workflows

Coy (serverless AI infrastructure) describes enterprise-scale adoption as moving from isolated LLM experiments to production-grade systems optimized for real-time inference and customization. Demand is shifting from mostly LLM deployments toward vision-language models and, increasingly, agentic workflows. This changes infrastructure needs: beyond GPUs for model inference, organizations now need more “traditional” CPU-based compute for running agents plus secure, sandboxed execution for tool use. A notable regional difference emerges: US enterprises tend to adopt faster, while European enterprises move more cautiously, waiting for others to make mistakes first—despite caring primarily about “best product” outcomes.

SAP (enterprise software and customer transformation) frames the last several years as an evolution from classic ML and process intelligence (procure-to-pay, order-to-cash), through bots and conversational interfaces, into today’s agentic AI era that stitches tasks into end-to-end workflows. The key shift is from “curiosity” about new technology to “capability” questions: which business capabilities are targeted, what technical constraints exist in complex hybrid landscapes, and what measurable business value results. Time-to-implementation varies widely—from weeks to years—depending on organizational complexity and phased rollouts across regions, plants, and operating units.

Google Cloud AI (Vertex/Agent Space, ADK, A2A) highlights a major enterprise change: the buy-vs-build decision is tilting toward buying commoditized layers (RAG pipelines, connectors) so engineering can focus on differentiated, domain-specific agent workflows. Top blockers to scaling include bureaucracy and culture, skill gaps amid rapid model churn (e.g., version leapfrogging), and deployment realities where prototypes are easy but enterprise rollouts are hard. A thought-provoking tension: stakeholders demand “100% accuracy” from AI even though humans aren’t perfect—AI exposes quality gaps that were never measured before. The dominant boardroom theme forecasted is digital workers and multi-agent orchestration, forcing organizations to rethink structures, processes, and even business models.