The 4-Layer Production Stack Every AI Agent Needs

Most enterprise teams are building agents that shine in demos but fall apart in production. The problem is rarely the model. It is the infrastructure underneath. In this session, we walk through the four-layer production stack that closes the gap between agent prototypes and enterprise-grade deployments, and introduce Agentspan, an open source server and SDK that compiles your agent definitions into durable workflows so execution state lives outside your process, tool calls retry automatically, and human approvals wait and resume cleanly.

Whether you are deploying your first production agent or designing infrastructure for enterprise-scale AI systems, this session gives you a practical, grounded blueprint.

Keep agents running through failures. Built on Conductor OSS, originally created at Netflix, to give agents fault tolerance, state persistence, and the ability to resume after a process is killed or scaled down.

Orchestrate multi-agent workflows including sequential, parallel, and handoff patterns so agents can collaborate across tasks without losing context or state.

Add guardrails, human-in-the-loop checkpoints, structured input/output validation, and credential management so agents operate within governed, auditable boundaries.

Query and replay every step of agent execution. Full decision traceability, cost monitoring, and audit trails across every action your agents take.

You will see a live demo that shows a durable agent surviving a killed process and resuming with full state, plus a multi-agent handoff example using real integrations. By the end of the session you will understand exactly why agents need this infrastructure and how to apply it in your own stack.