Building AI agents shouldn’t be hard. According to theCUBE Research, Docker makes it easy

For most developers, getting started with AI is still too complicated. Different models, tools, and platforms don’t always play nicely together. But with Docker, that’s changing fast.

Docker is emerging as essential infrastructure for standardized, portable, and scalable AI environments. By bringing composability, simplicity, and GPU accessibility to the agentic era, Docker is helping developers and the enterprises they support move faster, safer, and with far less friction. 

Real results: Faster AI delivery with Docker

The platform is accelerating innovation: According to the latest report from theCUBE Research, 88% of respondents reported that Docker reduced the time-to-market for new features or products, with nearly 40% achieving efficiency gains of more than 25%. Docker is playing an increasingly vital role in AI development as well. 52% of respondents cut AI project setup time by over 50%, while 97% report increased speed for new AI product development.

Reduced AI project failures and delays

Reliability remains a key performance indicator for AI initiatives, and Docker is proving instrumental in minimizing risk. 90% of respondents indicated that Docker helped prevent at least 10% of project failures or delays, while 16% reported prevention rates exceeding 50%. Additionally, 78% significantly improved testing and validation of AI models. These results highlight how Docker’s consistency, isolation, and repeatability not only speed development but also reduce costly rework and downtime, strengthening confidence in AI project delivery.

Build, share, and run agents with Docker, easily and securely

Docker’s mission for AI is simple: make building and running AI and agentic applications as easy, secure, and shareable as any other kind of software.

Instead of wrestling with fragmented tools, developers can now rely on Docker’s trusted, container-based foundation with curated catalogs of verified models and tools, and a clean, modular way to wire them together. Whether you’re connecting an LLM to a database or linking services into a full agentic workflow, Docker makes it plug-and-play.

With Docker Model Runner, you can pull and run large language models locally with GPU acceleration. The Docker MCP Catalog and Toolkit connect agents to over 270 MCP servers from partners like Stripe, Elastic, and GitHub. And with Docker Compose, you can define the whole AI stack of models, tools, and services in a single YAML file that runs the same way locally or in the cloud. Cagent, our open-source agent builder, lets you easily build, run, and share AI agents, with behavior, tools, and persona all defined in a single YAML file. And with Docker Sandboxes, you can run coding agents like Claude Code in a secure, local environment, keeping your workflows isolated and your data protected.

Even hardware limits aren’t a blocker anymore when building agents. Docker Offload lets developers run heavy compute tasks on cloud GPUs with one click. 

Conclusion 

Docker’s vision is clear: to make AI development as simple and powerful as the workflows developers already know and love. And it’s working: theCUBE reports 52% of users cut AI project setup time by more than half, while 87% say they’ve accelerated time-to-market by at least 26%.

Learn more

• Read more about ROI of working with Docker in our latest blog
• Download theCUBE Research Report and eBook – economic validation of Docker
• Explore the MCP Catalog: Discover containerized, security-hardened MCP servers
• Open Docker Desktop and get started with the MCP Toolkit (Requires version 4.48 or newer to launch the MCP Toolkit automatically)
• Head over to the cagent GitHub repository, give the repository a star, try it out, and let us know what amazing agents you build!

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