Engineering AI, Metadata & Context for Self-Service Agentic Analytics
Read this BARC Spotlight on what an agentic analytics fabric is, why most analytics agents fail without one, and how context engineering and real-time data access combine to finally make self-service analytics achievable.
Why Most Analytics Agents Fail Before They Start
Analytics agents promise the ultimate vision of self-service — business teams exploring data and making smarter decisions without burdening technical specialists. But despite impressive gains in model intelligence, most agentic analytics tools still can’t deliver production-level insights. According to BARC research, while 50% of organizations have agents in production, only 27% use them for BI or analytics. The problem? Agents can’t access distributed data in real time, and they’re starved of the metadata context needed for accurate, personalized conversations. The result: business users are stuck exploring data in a slow, impersonal way — unsure which outputs to trust.
What you’ll discover:
- Why a new architectural approach — the agentic analytics fabric — is emerging to solve the accuracy and access challenges holding back self-service analytics, and how it brings together agent engineering, context engineering, and real-time distributed data access
- How context engineering goes beyond traditional semantic layers to organize technical, business, operational, and governance metadata — giving agents the intelligence they need to deliver role-specific, explainable insights
- Four real-world use cases — from sales forecasting to supply chain optimization — that show how the fabric delivers trusted, actionable analytics at enterprise scale