Last week, Docker was thrilled to be part of the inaugural AI Engineer Paris, a spectacular European debut that brought together an extraordinary lineup of speakers and companies. The conference, organized by the Koyeb team, made one thing clear: the days of simply sprinkling ‘AI dust’ on applications are over. Meaningful results demand rigorous engineering, complex data pipelines, focus on distributed systems, understanding compliance and supply chain security of AI.
But the industry’s appetite for automation and effectively working with natural language and unstructured data isn’t going anywhere. It’s clear that AI Agents represent the next, inevitable wave of application development.
At Docker, we’re dedicated to ensuring that building, sharing, and securing these new AI-powered applications is as simple and portable as containerizing microservices. That was the core message we shared at the event, showcasing how our tools simplify the entire agent lifecycle from local development to secure deployment at scale.
Keynote on democratizing AI Agents
Tushar Jain, Docker’s EVP Engineering & Product, joined a powerful line-up of Europe’s top AI engineering thought leaders including speakers from Mistral, Google DeepMind, Hugging Face, and Neo4j.
Tushar’s session, “Democratizing AI Agents: Building, Sharing, and Securing Made Simple,” focused on a critical challenge: AI agent development can’t stay locked away with a few specialists. To drive real innovation and productivity across an organization, building agents must be democratized. We believe agents need standardized packaging and developers need a simple, secure way to discover and run MCP servers.
Tushar spoke about how over the last decade, Docker made containers and microservices accessible to every developer. Now we see agents following the same trajectory. Just as containers standardized microservices, we need new tooling and trusted ecosystems to standardize agents. By developing standardized agent packaging and building the MCP Toolkit & Catalog for secure, discoverable tools, Docker is laying the groundwork for the next era of agent-based development.
Hands-On: Building Collaborative Multi-Agent Teams
To guide attendees to understanding this in practice, we followed this vision with a hands-on workshop, Building Intelligent Multi-Agent Systems with Docker cagent: From Solo AI to Collaborative Teams. And it was a massive hit! Attendees had a perfect way to connect with the cagent team and to learn how to package and distribute agents as easily as building and pushing Docker images.
The workshop focused on recently open-sourced cagent and how to use it for common tasks in agent development:
- Orchestrate specialized AI agent teams that collaborate and delegate tasks intelligently.
- using
cagentto easily package, share, and run existing multi-agent systems created by community - and of course how to integrate external tools through the Model Context Protocol (MCP), ensuring agents have access to the data and can affect changes in the real world.
If you want to try it yourself, the self-paced version of the workshop is available online: https://cagent-workshop.rumpl.dev/README.html
At the end of the day during a breakout session, we followed that up with another reality-inspired message in my talk Building AI workflows: from local experiments to serving users. Whatever technologies you pick for your AI agent implementation: AI applications are distributed systems. They are a combination of the model, external tools, and your prompts. This means that if you ever aim to move from prototypes to production, you shouldn’t develop agents as simple prompts in AI assistants UI. Instead, treat them as you would any other complex architecture: containerize the individual components, factor in security and compliance, and architect for deployment complexity from the start.
Next Steps: Build and Secure Your Agents Today!
All in all, we had plenty of fantastic conversations with the AI Engineer community, which reinforced that developers are looking for tools that offer simplicity, portability, and security for this new wave of applications.
Docker is committed to simplifying agent development and securing MCP deployments at scale.
Learn More
- Watch the AI Engineer Paris Keynote
- Explore the MCP Catalog: Discover containerized, security-hardened MCP servers
- Download Docker Desktop to get started with the MCP Toolkit: Run MCP servers easily and securely
- Check out our MCP Horror Stories series to see common MCP security pitfalls and how you can avoid them
- Visit the cagent repo and build your agents in a few easy steps