AI is only as good as what we feed it — and humans are still the ones feeding it. Despite the hype around autonomy and foundation models, most AI systems don’t fail at the model layer. They fail in operations: weak feedback loops, ontology drift, tooling gaps, inconsistent edge-case decisions, and quality decay at scale. This session explores the hidden DataOps infrastructure behind production AI and autonomy systems and why operational discipline is often the difference between a model that demos well and one that survives reality.

• Where autonomy systems actually fail in production
• Why operational feedback loops drive reliability
• How teams reduce drift, ambiguity, and quality decay at scale
• The role humans still play in “autonomous” systems
• Why output quality is only as good as the operational input behind it