Why data governance is the secret to AI agent success

I've watched teams deploy AI agents into production only to face cascading failures rooted in poor data quality and lineage visibility. The governance angle here isn't bureaucratic overhead—it's infrastructure. When you run autonomous agents making decisions on stale or inconsistent data, you're amplifying risk exponentially. What's changing is that DevOps rigor now extends upstream into data pipelines. Teams building production-grade agentic systems need declarative data contracts, automated lineage tracking via tools like OpenLineage, and observability that catches data drift before agents act on it. This connects to the larger shift toward data mesh thinking—agents can't thrive in monolithic data warehouses with unclear ownership. The concrete move: start mapping data dependencies now, implement data quality gates as CI/CD checkpoints, and treat data governance as a platform capability, not a compliance checkbox. Without this foundation, your agents become expensive hallucination engines.