Daniele Zonca is a Senior Principal Software Engineer and Architect for model serving of Red Hat OpenShift AI, Red Hat's flagship AI product combining multiple stacks. He has the responsibility for the architecture of serving components: model server management, model registry, monitoring and trustworthy AI. On the community side he is one of the founders of the TrustyAI project focused on responsible and trustworthy AI and involved with KServe, ModelMesh and Kubeflow model registry projects
Although there is no universally agreed definition of what an AI agent is, in practice several patterns are emerging that demonstrate how to coordinate and combine the capabilities of multiple AI services, in order to create AI-based systems that can accomplish more complex tasks.
These Agentic Systems architectures can be grouped in 2 main categories: workflows, where LLMs and tools are orchestrated through predefined code paths, and agents, where LLMs dynamically direct their own processes and tool usage, maintaining control over how they execute tasks.
Moreover the security and safety of Agentic Systems architectures is one of the biggest challenges for the adoption in mission critical scenarios. The possibility to serve the model on premise and the introduction of guardrailing techniques are key enablers.
The goal of this talk is giving a theoretical overview of Agentic AI in general and these patterns in particular, discussing their range of applicability and showing with practical examples how they can be easily implemented with Quarkus and its LangChain4j extension on premise using vLLM and Kubernetes.
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