As organizations move beyond basic LLM integrations toward autonomous agentic workflows, the infrastructure required to support these systems grows increasingly complex. Running multi-agent architectures in production introduces unique challenges around tool discovery, secure access, and traffic routing.

This session explores how to leverage Kubernetes and cloud-native abstractions to develop, test, and deploy AI agents at scale. Using a reference architecture leveraging the Kagenti project, we will demonstrate how to construct a secure, scalable agentic environment. The talk covers unifying tool access via an MCP Gateway, enforcing zero-trust workload identity with SPIFFE/SPIRE, and standardizing inter-agent communication.

To illustrate these concepts, we will walk through a real-world financial use case: an autonomous agent that securely accesses market data and executes simulated transactions using financial tools over MCP, demonstrating end-to-end cloud-native deployment.