Near-100% Accurate Data for your Agent with Comprehensive Context Engineering

The real challenge with agentic AI isn't the models—it's the data feeding them. I've seen teams ship agents that hallucinate or make poor decisions because their context layer was brittle, pulling from stale warehouses or incomplete views. Google's push toward "comprehensive context engineering" signals what we're already experiencing: agents performing well at scale demands we treat data freshness, accuracy, and completeness as first-class architectural concerns, not afterthoughts. This means rethinking how we structure dbt transformations, governance frameworks, and real-time pipelines. For teams still operating siloed data products, this is a wake-up call. You'll need unified semantic layers, automated data quality gates, and tighter feedback loops between ML systems and warehouse operations. The operational burden is real—but the alternative is shipping agents that fail unpredictably in production. My recommendation: audit your current context pipelines now. Where does your agent depend on slow-moving batch data? Where are your freshness SLOs undefined? Those gaps are your failure points.