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Agentic AI in Action: Deploying Secure, Task-Driven Agents in Rackspace Cloud

The concept of AI agents has emerged as a transformative tool, empowering organizations to create AI systems capable of secure, real-world interactions. By leveraging a language model’s natural language abilities alongside function-calling capabilities, an agentic AI system can interact with external systems, retrieve data, and perform complex tasks autonomously. Enterprises can harness the full potential of AI by designing agent workflows that interact securely with business data, ensuring control and privacy. In this post, we explore building an AI agentic workflow with Meta’s LLaMA 3.1, specifically crafted for interacting with private data from a database. We’ll dive into the technical foundation of agentic systems, examine how function calls operate, and show how to securely deploy all of this within a private cloud, keeping data secure.

Running Llama on Rackspace Cloud

In one of my favourite movie series The Avengers, Tony Stark (Iron Man) creates this Artificial Intelligence (AI) named Jarvis, which helps him make much of his other works possible. This portrayal sparks curiosity: Are such sophisticated AIs possible in real life? Until a few years ago, AI capabilities like JARVIS were confined to the realm of science fiction. However, advancements in AI have bridged the gap between fantasy and reality, making powerful, customizable AI models accessible to enthusiasts and professionals alike.