Building a data foundation ready for agentic AI is becoming a priority for organizations deploying AWS. This foundation relies on a unified lakehouse and interoperability protocols like Model Context Protocol (MCP). You can move from prototype to production. Yet many teams struggle to know where to start.
According to Gartner, more than 40 percent of agentic AI projects will be canceled by 2027 for failing to deliver the expected value. While Gartner points to several contributing factors, we believe the root cause comes down to two things: a lack of governance and inconsistent data.
If you’re building a data platform for AI agents, you probably recognize four barriers. Your data is fragmented across silos. Your unstructured data is available but hard to use. Your agents and your systems lack a common way to communicate. Gaps in quality and governance weaken trust and create hallucinations.
Each barrier holds back agentic AI. Closing them requires…
