The Challenge: Modern AI on Legacy Infrastructure

Why SSAS Cubes Still Matter

Despite the rise of modern analytics platforms, SSAS multidimensional cubes remain critical infrastructure for many enterprises. They excel at serving complex, highly aggregated analytics over massive historical datasets with many concurrent users. These systems provide:

• Battle-tested semantic layers with rich hierarchies, KPIs, and calculated measures

• Sophisticated security models enforced at the data layer

• MDX calculations encoding years of business logic

• Native integration with Excel and legacy reporting tools

The Migration Dilemma

Organizations cannot simply abandon these systems. Re-implementing intricate calculations and security rules in modern stacks is non-trivial and risky. Regulatory reporting requirements, tightly coupled downstream systems, and the sheer scale of business user adoption make "lift and shift" migrations expensive and disruptive.

For these companies, SSRS reports on top of SSAS cubes remain stable, paid-for platforms, even as they explore modern analytics capabilities in parallel.

Technical Obstacles

Connecting an AI agent to SSAS cubes presented three fundamental challenges:

Multi-Axis Data Structure

SSAS cubes return results across multiple axes, rows, columns, and slices, creating a fundamental shape mismatch with AI agents that expect flat, tabular data. The agent's reasoning model assumes traditional SQL-style result sets, not OLAP's dimensional structure.

Limited Connectivity Options

Native drivers for SSAS are either Windows-only (ADOMD.NET, OLE DB) or severely limited. The OPENQUERY approach suffers from an 8KB query size limit, produces only flattened results, and offers no parameterization—making it unsuitable for dynamic AI-generated queries.

Authentication Complexity

SSAS typically relies on Windows/Kerberos authentication, which creates significant friction from non-.NET environments, especially when navigating VPCs, network hops, and cloud-based agent infrastructure.

The Solution: XMLA Protocol and Intelligent Translation 

Rather than forcing SSAS into a SQL paradigm, we embraced its native protocol and built a translation layer that bridges OLAP and AI reasoning. 

Direct XMLA/HTTP Integration 

SSAS exposes an XML for Analysis (XMLA) endpoint via msmdpump.dll that accepts MDX queries over SOAP and returns structured XML responses. By consuming this protocol directly, we bypassed the limitations of Windows-only drivers and SQL-based workarounds. 

Bidirectional Translation Layer 

We built an MDX generation engine that: 

• Translates the AI agent's tabular mental model (filters, groupings, aggregations) into valid MDX with appropriate axis mappings 

• Parses multi-dimensional XML responses from SSAS 

• Reshapes results into the flat, 2D tabular format the agent expects 

This bidirectional translation lets Milo maintain its simple "ask questions, get answers" interface while leveraging the full power of multidimensional analytics underneath. 

Simplified Authentication 

We configured the SSAS XMLA endpoint with Basic authentication over HTTPS, eliminating the Kerberos double-hop problem. A simple service account allows our Go-based client to authenticate without Windows dependencies or complex delegation configurations. 

Business Impact 

This integration delivers immediate value to organizations with SSAS infrastructure: 

Key Takeaways 

• Legacy doesn't mean obsolete: Well-designed legacy systems often contain irreplaceable business logic and proven scalability 

• Integration beats migration: Building bridges to existing infrastructure is often faster and safer than wholesale replacement 

• AI can adapt to data, not vice versa: Intelligent translation layers allow AI to work with data in its native format 

• Authentication matters: Simple, standards-based authentication removes significant integration friction 

The Result: Milo's can now query complex SSAS cubes using natural language, delivering the benefits of Generative BI while preserving existing investments in cube design, security models, and business logic. 

Conclusion 

By meeting SSAS cubes on their own terms rather than forcing them into a SQL paradigm, Milo demonstrates that organizations don't have to choose between AI innovation and infrastructure stability. The right integration approach can deliver both. 

For enterprises with significant SSAS investments, this means Generative BI isn't a distant future goal requiring years of migration, it's an immediate capability that respects and extends existing systems.