What Breaks When AI Workflows Go to Production

About this session

AI teams move fast, but production systems often fail in the spaces between models, tools, workflows, and deployment gates. This session shares lessons from building cloud-scale engineering platforms that orchestrate complex software delivery and AI-ready infrastructure workflows across validation, governance, automation, observability, and operational readiness. I will cover what breaks when workflows become long-running, distributed, and dependent on many downstream systems: unclear failure ownership, unreliable status, retry/cancellation edge cases, weak auditability, and manual recovery. Attendees will learn practical patterns for designing resilient workflow layers: stage abstraction, durable execution state, idempotent integrations, reconciliation loops, meaningful observability, and governance controls that help AI and platform teams ship faster without losing reliability.

Speaker

Key takeaways

  • How to design reliable workflow orchestration layers for AI and cloud engineering teams.
  • How to handle production failure modes such as retries, cancellations, stuck executions, inconsistent status, and unclear ownership.
  • How observability, auditability, reconciliation, and governance make AI-ready platforms safer to scale.

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