This podcast episode focuses on "Agent Ops," the operational lifecycle of AI agents, guiding them from simple prototypes to reliable enterprise solutions. The discussion unpacks the challenges of deploying AI agents, emphasizing that 80% of the effort lies in infrastructure, security, and validation rather than core AI development. It highlights the differences between traditional ML Ops and Agent Ops, focusing on the dynamic nature of AI agents. The speakers delve into key roles like prompt engineers and AI engineers, the importance of automated evaluation, CI-CD pipelines, comprehensive observability, and security measures such as policy definition, guardrails, and continuous assurance. They also cover rollout strategies, versioning, operational controls, security response playbooks, agent interoperability using Model Context Protocol (MCP) and Agent2Agent (A2A) protocols, and the concept of agent registries for scalability and governance, emphasizing the need for testing and deployment automation.
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