Agents are inherently unreliable. While simple to prototype, agentic systems with their many (distributed) moving parts are complex, have degrading trust, and are expensive due to inefficient model usage and ineffective scaling. The gap between simple agents and an agentic system (AI that never fails) is wide. How should agentic developers and enterprises overcome these challenges? 

Agentic AI Platforms are an emerging market – a technology stack that simplifies building systems, leverages methodologies for continuously building trust in production, and then scaling cost effectively.

In this brief session, we are going to demo developing, testing, deploying, and managing an agentic AI system:

• Create a multi-agent, dynamically orchestrated AI system with a continuous feedback loop.
• Build, test, pack, and run this system in a multi-node cluster with elasticity and resilience.
• Manage the system with immutable tracing, agentic interaction audits, and workflow tracking. 
• Setup HA/DR by deploying into multiple clouds with active-active memory replication.