Based on Docker’s State of Agentic AI report, a global survey of more than 800 developers, platform engineers, and technology decision-makers, this blog summarizes key findings of what’s really happening as agentic AI scales within organizations. Drawing on insights from decision-makers and purchase influencers worldwide, we’ll give you a preview on not only where teams are seeing early wins but also what’s still missing to move from experimentation to enterprise-grade adoption.
Rapid adoption, early maturity
60% of organizations already have AI agents in production, and 94% view building agents as a strategic priority, but most deployments remain internal and focused on productivity and operational efficiency.
Security and complexity are the top barriers
40% of respondents cite security as the #1 challenge in scaling agentic AI, with 45% struggling to ensure tools are secure and enterprise-ready. Technical complexity compounds the challenge. One in three organizations (33%) report orchestration difficulties as multi-model and multi-cloud environments proliferate (79% of organizations run agents across two or more environments).
MCP shows promise but isn’t enterprise-ready
85% of teams are familiar with the Model Context Protocol (MCP), yet most report significant security, configuration, and manageability issues that prevent production-scale deployment.
Want the full picture? Download the latest State of Agentic AI report to explore deeper insights and practical recommendations for scaling agentic AI in your organization.
Fear of vendor lock-in is real
Enterprises worry about dependencies in core agent and agentic infrastructure layers such as model hosting, LLM providers, and even cloud platforms. Seventy-six percent of global respondents report active concerns about vendor lock-in, rising to 88% in France, 83%in Japan, and 82% in the UK.
Containerization remains foundational
94% use containers for agent development or production, and 98% follow the same cloud-native workflows as traditional software, establishing containers as the proven substrate for agentic AI infrastructure.
Long-term outlook
Rather than a “year of the agents,” the data points to a decade-long transformation. Organizations are laying the governance and trust foundations now for scalable, enterprise-grade agent ecosystems.
The path forward
The path forward doesn’t require reinvention so much as consolidation around a trust layer: access to trusted content and components that can be safely discovered and reused; secure-by-default runtimes; standardized orchestration and policy; and portable, auditable packaging.
Agentic AI’s near-term value is already real in internal workflows; unlocking the next wave depends on standardizing how we secure, orchestrate, and ship agents. Teams that invest now in this trust layer, on top of the container foundations they already know, will be first to scale agents from local productivity to durable, enterprise-wide outcomes.
Download the full Agentic AI report for more insights and recommendations on how to scale agents for enterprise.
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