IDC: Why the right networking approach is foundational to agentic AI

Editor's note: Today we hear from IDC on the results of its 2026 AI in Networking Special Report Survey exploring the enterprises' concerns about networking infrastructure to support the rise of agentic AI in their organizations. Enterprises are moving quickly on AI pilots, but the move from pilot to production remains uneven.

While AI models remain important, IDC research indicates that the pilot-to-production bottleneck is primarily infrastructure-centric, with core networking concerns emerging as one of the leading drivers of AI project delays and abandonment. In IDC's 2026 AI in Networking Special Report Survey : 32. 6% of respondents cite security concerns: As AI workflows become more distributed and autonomous, enforcing consistent security and governance becomes more difficult.

8% of respondents cite challenges in automation: Manual operations and fragmented controls can slow deployment and make AI environments harder to scale. 7% of respondents cite staff time and talent restrictions: Limited skills and operational bandwidth can constrain an organization's ability to move AI initiatives into production. Agentic AI specifically heightens these concerns by introducing more distributed and dynamic interactions across applications, services, APIs, tools, and data sources.

In production environments, these interactions often span different agent frameworks, model providers, clouds, open-source tools, SaaS APIs, and internal applications, expanding both the operational scope and the security and governance surface area. Networking for operational control, security, and governance at scale Networking is the primary enabler of agentic interactions and plays a foundational role for intracloud and intercloud network- and services-layer connectivity, end-to-end security, and consistent governance. In agentic systems, networking increasingly extends into tighter service-centric controls that govern how distributed services identify one another, communicate, and exchange data securely.

While AI workloads in general are increasing east-west traffic demands, agentic AI adds an additional layer of complexity by creating dynamic interactions that require tighter policy, visibility, and control closer to the application workflow. From an infrastructure perspective, networking is much more than just a connectivity function. It is part of the infrastructure platform control plane that applies policy-based controls, supports observability, and helps maintain consistent security and governance across an AI agent's activity.

This is significant because framework-level controls alone become insufficient in environments where agents and services span different runtimes, clouds, deployment models, and operating domains. That is why an infrastructure-level approach becomes key. It does not replace application frameworks or orchestration environments, but it provides broader and more consistent policy implementation across a complex architectural landscape.

As agentic AI becomes more autonomous and distributed, organizations need these controls built in as part of the infrastructure to reduce fragmented observability, inconsistent policy application, and unmanaged shadow agent activities. From a cloud infrastructure standpoint, this is where cloud network services become strategically important. best-of-breed approach Agentic AI systems are inherently fragmented because of underlying distributed workflows.

Enterprises are already navigating a rapidly evolving landscape of business requirements, open-source components, emerging protocol standards, and new architecture patterns. In this context, choices between best-of-breed point solutions and platform-based approaches should be strategic rather than ideological. Best-of-breed capabilities may be necessary to address specific technical requirements.