The Foundations of AI-Native Analytics: From Context to Agentic Workflows

How to move beyond RAG and build analytics enterprises can trust.

This whitepaper serves as a practical guide for data and analytics leaders who want to streamline manual workflows and establish the right foundations for AI-native analytics. It explains what needs to be governed, why it matters, and how to design architectures that scale.

What the Whitepaper Governs

1. Context Management
   • Why context is the missing piece in most AI analytics projects.
   • How semantic layers standardize KPIs, relationships, and logic across queries.
   • The balance between too much and too little context.
2. Multi-Agent Workflows
   • The transition from single-agent to multi-agent systems.
   • How specialized agents mirror human analysts: intent clarification, query generation, validation, and insight delivery.
   • Practical examples of orchestration in areas like supply chain analysis.
3. Governance Frameworks
   • Guardrails for controlled access to data and metrics.
   • Embedding business rules and KPI definitions into a transparent, enforceable semantic layer.
   • Feedback loops and auditability to ensure durable trust.
4. Agentic Workflows in Action
   • How networks of specialized agents can turn vague requests into sharp, actionable insights.
   • The link between analysis and action, with governance ensuring consistency and accountability.
5. Building Blocks for Success
   • Semantic layers
   • Business context encoding
   • Continuous feedback loops
   • Governance-first design principles
   • Multi-agent architectures

Why It Matters

The whitepaper lays out the governance-first approach enterprises must adopt to:

• Prevent shallow or contradictory analyses
• Ensure KPI and metric consistency
• Protect sensitive data
• Build executive trust in AI-driven insights
• Transform analytics from reactive reporting into proactive action

Closing Thought

AI-native analytics isn’t about a single model or tool. It’s about governance, context, and orchestrated agents working together. By putting these guardrails in place, enterprises can finally move past proof-of-concepts and scale AI analytics with confidence.

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