Agentic RAG - Securing GenAI with F5 Distributed Cloud Services
Agentic RAG (Retrieval-Augmented Generation) enhances the capabilities of a GenAI chatbot by integrating dynamic knowledge retrieval into its conversational abilities, making it more context-aware and accurate. In this demo, I will focus on security aspect of the solution. This demonstration will highlight the various security measures implemented and enforced in our AI reference architecture for this Agentic RAG. F5 is a trusted leader in security, with a track record of delivering robust solutions for securing applications and networks. Recognized by many independent evaluations as a Leader in Web Application and API Security from IDC, SC Award, TrustRadius, EMA, and many more, F5 exemplifies excellence and innovation. These endorsements affirm F5’s expertise, reassuring organizations that their digital assets are protected by a capable, reputable partner that keeps pace with evolving security needs.Enhance your GenAI chatbot with the power of Agentic RAG and F5 platform
Agentic RAG (Retrieval-Augmented Generation) enhances the capabilities of a GenAI chatbot by integrating dynamic knowledge retrieval into its conversational abilities, making it more context-aware and accurate. In this demo, I will demonstrate an autonomous decision-making GenAI chatbot utilizing Agentic RAG. I will explore what Agentic RAG is and why it's crucial in today's AI landscape. I will also discuss how organizations can leverage GPUaaS (GPU as a Service) or AI Factory providers to accelerate their AI strategy. F5 platform provides robust security features that protect sensitive data while ensuring high availability and performance. They optimize the chatbot by streamlining traffic management and reducing latency, ensuring smooth interactions even during high demand. This integration ensures the GenAI chatbot is not only smart but also reliable and secure for enterprise use.How To Build An Agent With n8n And Docker - AI Step-By-Step Lab
OK, community... It’s time for another addition to our AI Step-By-Step Labs, from yours truly! I did a mini lab for installing n8n on a mac. It has been popular, but we thought it would be better as a Docker installation to fit into our lab. So I set out to do this on Docker and it was super easy. I'm not going to spoil it too much, as I'd like for you to visit the GitHub repository for our labs, but this is the flow you create: This lab walks you through installation in Docker and walks you through creation of an AI agent that proxies requests to the LLM instance you created with our first lab on installing Ollama. Please check out the repository on GitHub to get started using this powerful agentic framework in your lab, production or personal use today. Feel free to watch the video on our YouTube channel here:
Scaling and Traffic-Managed Model Context Protocol (MCP) with BIG-IP Next for K8s
Introduction As AI models get more advanced, running them at scale—especially in cloud-native environments like Kubernetes—can be tricky. That’s where the Model Context Protocol (MCP) comes in. MCP makes it easier to connect and interact with AI models, but managing all the traffic and scaling these services as demand grows is a whole different challenge. In this article and demo video, I will show how F5's BIG-IP Next for K8s (BNK), a powerful cloud native traffic management platform from F5 can solve that and keep things running smoothly and scale your MCP services as needed. Model Context Protocol (MCP) in a nutshell. There were many articles explaining what is MCP on the internet. Please refer to those in details. In a nutshell, it is a standard framework or specification to securely connect AI apps to your critical data, tools, and workflow. The specification allow Tracking of context across multiple conversation Tool integration — model call external tools Share memory/state — remember information. MCP’s "glue" model to tools through a universal interface "USB-C for AI" What EXACTLY does MCP solve? MCP addresses many challenges in the AI ecosystem. I believe two key challenges it solve Complexities of integrating AI Model (LLM) with external sources and tools By standardization with a universal connector ("USB-C for AI") Everyone build "USB-C for AI" port so that it can be easily plug in each other Interoperability. Security with external integration Framework to establish secure connection Managing permission and authorization. What is BIG-IP’s Next for K8s (BNK)? BNK is F5 modernized version of the well-known Big-IP platform, redesigned to work seamlessly in cloud-native environments like Kubernetes. It is a scalable networking and security solution for ingress and egress traffic control. It builds on decades of F5's leadership in application delivery and security. It powers Kubernetes networking for today's complex workloads. BNK can be deployed on X86 architecture or ARM architecture - Nvidia Data Processing Unit (DPU) Lets see how F5's BNK scale and traffic managed an AIOps ecosystem. DEMO Architecture Setup Video Key Takeaways BIGIP Next for K8s, the backbone of the MCP architecture Technology built on decades of market-leading application delivery controller technology Secure, Deliver, and Optimize your AI infrastructure Provides deep insight through observability and visibility of your MCP traffic.
