Most AI agent failures in production get blamed on the model. Retrain it. Fine-tune it. Swap it for a bigger one. In practice, the model is rarely the problem. The data pipeline feeding it is.

This talk walks through five specific, documented failure modes that systematically undermine AI agents connected to batch data pipelines: stale data, where agents diagnose from outdated snapshots that no longer reflect reality; memory gaps, where batch windows strip away the history needed to detect patterns; delete blindness, where ghost records cause agents to act on data that no longer exists; schema fragility, where silent field loss degrades decisions without any failure signal; and coordination failure, where multiple agents consuming inconsistent snapshots execute conflicting remediations that cascade.

Each failure mode is presented with its mechanism, real-world consequence, and the streaming-first architectural remedy that eliminates it. Attendees will leave with a diagnostic framework they can apply immediately to their own agent deployments and a clear understanding of the architectural prerequisites for reliable agentic systems.