Agentic workflows are fast becoming the backbone of enterprise AI, enabling scalable automation that bridges on-prem systems and the cloud without adding complexity. Organizations adopting agentic workflows are increasingly turning to standard APIs and open-source platforms to simplify the deployment of AI at scale. By abstracting the hardware and infrastructure complexities, these workflows allow for seamless integration across diverse environments, giving companies the flexibility to shift workloads without rewriting code, according to Chris Branch, AI strategy sales manager at Intel. “With the agentic workflow combined with APIs, what you can do then is have a dashboard that runs multiple models simultaneously. What that agentic workflow with these APIs allows is for companies to run those on different systems at different times in different locations without changing any of their code.” This modularity also extends to inference use cases, such as chat interfaces, defect detection and IT automation. Each task might leverage a different AI model or compute resource, but with agentic workflows, they can all operate within a unified dashboard. Standards such as the Llama and OpenAI APIs are central to enabling this level of fluidity and collaboration between agents, according to Mungara. At the foundation of this vision is the Open Platform for Enterprise AI, which provides infrastructure-agnostic building blocks for generative AI. Supported by contributions from Advanced Micro Devices Inc., Neo4j Inc., Infosys Ltd. and others, OPEA allows enterprises to rapidly test, validate and deploy scalable solutions across cloud and on-prem infrastructure, Branch explained.