Security continues to surface as a top blocker to agent adoption. But it’s not the only one. Technical complexity is rising fast as well. Vendor lock-in is a big concern for the vast majority of the respondents surveyed. 

So how do teams cut through the complexity and prepare for a world of multi-model, multi-tool, and multi-framework agents, while avoiding vendor lock-in in their agent workflows? In this blog, we break down the key findings from our research: what teams are actually using to power their agentic workloads, and what it takes to build a more scalable, future-ready agent architecture.

Multi-model and multi-cloud are the new normal. And complexity is rising

Our recent Agent AI study found that enterprises are embracing multi-model and multi-cloud architectures to gain greater control over performance, customization, privacy, and compliance. Multi-model is now the norm. Nearly two-thirds of organizations (61%) combine cloud-hosted and local models. And complexity doesn’t stop there: 46% report using between four and six models within their agents, while just 2% rely on a single model.

Deployment environments are just as diverse. 79% of respondents operate agents across two or more environments; 51% in public clouds, 40% on-premises, and 32% on serverless platforms.

This architectural flexibility delivers control, but it also multiplies orchestration and governance efforts. Coordinating models, tools, frameworks, and environments is consistently cited as one of the hardest parts of building agents. Nearly half of respondents (48%) identify operational complexity in managing multiple components as their biggest challenge, while 43% point to increased security exposure driven by orchestration sprawl.

The strategic shift away from vendor lock-in

As organizations double down on agent investments, concerns about supply chain fragility are rising. Seventy-six percent of global respondents report active worries about vendor lock-in.

Rather than consolidating, teams are responding by diversifying. They’re distributing workloads across multiple models, tools, and cloud environments to reduce dependency and maintain leverage. Among the 61% of organizations using both cloud-hosted and locally hosted models, the primary drivers are control (64%), data privacy (60%), and compliance (54%). Cost ranks significantly lower at 41%, underscoring that flexibility and governance, not cost savings are shaping architectural decisions.

Containers power the next wave of agent adoption

Containerization is already foundational to agent development. Nearly all organizations surveyed (94%) use containers in their agent development or production workflows and the remainder plan to adopt them.

As agent initiatives scale, teams are extending the same cloud-native practices that power their application pipelines such as microservices architectures, CI/CD, and container orchestration to support agent workloads. Containers are not an add-on; they are the operational backbone. In fact, 94% of teams building agents rely on them.

At the same time, early signs of orchestration standardization are emerging. Among teams building agents with Docker, 40% are using Docker Compose as their orchestration layer, a signal that familiar, container-based tooling is becoming a practical coordination layer for increasingly complex agent systems.

The agentic future won’t be monolithic

The agentic future won’t be monolithic. It’s already multi-cloud, multi-model, and multi-environment. That reality makes open standards and portable infrastructure foundational for sustaining enterprise trust and long-term flexibility.

What’s needed next isn’t reinvention, but standardization around an open, interoperable and portable infrastructure: the flexibility to work across any model, tool, and agent framework, secure-by-default runtimes, consistent orchestration and integrated policy controls. Teams that invest now in this container-based trust layer will move beyond isolated productivity gains to sustainable enterprise-wide outcomes while reducing vendor lock-in risk.

Download the full Agentic AI report for more insights and recommendations on how to scale agents for enterprise.  

Join us on March 25, 2026, for a webinar where we’ll walk through the key findings and the strategies that can help you prioritize what comes next.

Learn more:

• Get your copy of the latest State of Agentic AI report! 
• Learn more about Docker’s AI solutions
• Read more about why AI agents challenge existing governance approaches and explore a new framework designed for agentic AI.

Agent adoption today is driven by a pragmatic focus on productivity, efficiency, and operational transformation, not revenue growth or cost reduction. Early adoption is concentrated in internal, productivity-focused use cases, especially across software, infrastructure, and operations. The feedback loops are fast, and the risks are easier to control. 

So what’s holding back agent scaling? Friction shows up and nearly all roads lead to the same place: AI agent security. 

AI agent security isn’t one issue it’s the constraint

When teams talk about what’s holding them back, AI agent security rises to the top. In the same survey, 40% of respondents cite security as their top blocker when building agents. The reason it hits so hard is that it’s not confined to a single layer of the stack. It shows up everywhere, and it compounds as deployments grow.

For starters, when it comes to infrastructure, as organizations expand agent deployments, teams emphasize the need for secure sandboxing and runtime isolation, even for internal agents.

At the operations layer, complexity becomes a security problem. Once you have more tools, more integrations, and more orchestration logic, it gets harder to see what’s happening end-to-end and harder to control it. Our latest research data reflects that sprawl: over a third of respondents report challenges coordinating multiple tools, and a comparable share say integrations introduce security or compliance risk. That’s a classic pattern: operational complexity creates blind spots, and blind spots become exposure.

And at the governance layer, enterprises want something simple: consistency. They want guardrails, policy enforcement, and auditability that work across teams and workflows. But current tooling isn’t meeting that bar yet. In fact, 45% of organizations say the biggest challenge is ensuring tools are secure, trusted, and enterprise-ready. That’s not a minor complaint: it’s the difference between “we can try this” and “we can scale this.”

MCP is popular but not ready for enterprise

Many teams are adopting Model Context Protocol (MCP) because it gives agents a standardized way to connect to tools, data, and external systems, making agents more useful and customized.  Among respondents further along in their agent journey,  85% say they’re familiar with MCP and two-thirds say they actively use it across personal and professional projects. 

But the security story hasn’t caught up yet. Teams adopt MCP because it works, but they do so without the security guarantees and operational controls they would expect from mature enterprise infrastructure. For teams earlier in their agentic journey: 46% of them identify  security and compliance as the top challenge with MCP.

Organizations are increasingly watching for threats like prompt injection and tool poisoning, along with the more foundational issues of access control, credentials, and authentication. The immaturity and security challenges of current MCP tooling make for a fragile foundation at this stage of agentic adoption.

Conclusion and recommendations

Ai agent security is what sets the speed limit for agentic AI in the enterprise. Organizations aren’t lacking interest, they’re lacking confidence that today’s tooling is enterprise-ready, that access controls can be enforced reliably, and that agents can be kept safely isolated from sensitive systems.  

The path forward is clear. Unlocking agents’ full potential will require new platforms built for enterprise scale, with secure-by-default foundations, strong governance, and policy enforcement that’s integrated, not bolted on.

Download the full Agentic AI report for more insights and recommendations on how to scale agents for enterprise. 

Join us on March 25, 2026, for a webinar where we’ll walk through the key findings and the strategies that can help you prioritize what comes next.

Learn more:

• Get your copy of the latest State of Agentic AI report! 
• Learn more about Docker’s AI solutions
• Read more about why AI agents challenge existing governance approaches and explore a new framework designed for agentic AI.

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.

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.  

Learn more:

• Get your copy of the latest State of Agentic AI report! 
• Learn more about Docker’s AI solutions
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