I’ve been building in the business intelligence (BI) space for over two decades. I’ve seen the rise of self-service BI, the transition to cloud-native analytics, and the promise of augmented insights. Each wave brought new tools, new opportunities, and plenty of hype. Some innovations delivered lasting value. Others faded quickly. Now, we’re witnessing the next major evolution: agentic AI.
Agentic AI, powered by large language models (LLMs), promises a new level of autonomy in analytics—where intelligent agents don’t just respond to natural language queries, but reason, adapt, and take proactive steps to support decision-making. As compelling as this vision is, the infrastructure behind it is often overlooked. Eric Siegel, in his recent Forbes article, warned against normalizing the agentic AI hype before the technology is truly enterprise-ready. I couldn’t agree more. Without the right architecture, these AI agents will underperform and erode trust.
The critical enabler for agentic BI isn’t the language model or the interface. It’s the semantic layer.
Why First-Gen Natural Language Query Systems Failed And Why Agentic AI Will Too Without Semantics
Natural language query (NLQ) systems were marketed as the answer to self-service analytics. The idea was simple: ask a question in plain English and get an answer. But the execution fell short. These systems focused on translating language into SQL, without addressing the complexity of business logic, data hierarchies, and metric definitions. Without a solid foundation in a semantic layer, these systems required extensive training and metadata wrangling that couldn’t keep up with changes in business requirements.
In practice, real business questions depend on context—time frames, fiscal calendars, exceptions, segmentation rules, and governance. When NLQ systems lacked a semantic understanding of this context, they produced inconsistent and often misleading results. The problem wasn’t with the language parsing. It was due to the absence of a shared, governed understanding of business logic—the role a semantic layer is designed to fulfill.
GenAI Changed the User Experience, Not the Architecture
The introduction of generative AI and large language models, such as GPT-4, has fundamentally changed the interface for business intelligence. Now users can ask questions conversationally and receive responses that sound intelligent. But when those responses are grounded in raw schemas and inconsistent metric definitions, they risk becoming articulate hallucinations.
At AtScale, we ran benchmark tests to quantify this risk. When LLMs queried raw data models without semantic context, accuracy was below 20%. When those same models were paired with AtScale’s governed semantic layer, accuracy exceeded 95%. The differentiator wasn’t the model. It was the presence of structured, reusable, explainable business logic.
The Semantic Layer Is the Foundation for Agentic BI
Think of GenAI as the text-to-query translator. The semantic layer is the semantic engine. It gives AI agents the ability to understand—not just retrieve—information. It defines business metrics, encodes time logic, enforces access control, and supports explainability.
With a universal semantic layer, the same definition of “net revenue” can be used across Tableau, Excel, Power BI, Slack, and any LLM-powered interface. The result is consistent, governed analytics that scale across tools, teams, and technologies.
This isn’t just about better answers. It’s about enabling agents to reason. If Q2 revenue is below plan, the agent should know what “plan” means, how revenue is calculated, which product lines underperformed, and how to investigate further—all without human prompting. That’s only possible when the business logic is defined in a semantic layer.
Don’t Normalize the Hype—Invest in the Right Architecture
Eric Siegel’s cautionary take on agentic AI is reinforced by Gartner’s 2025 Hype Cycle for Analytics and Business Intelligence. Agentic analytics, decision intelligence, and natural language query are positioned as high-impact innovations—but they are also approaching the peak of inflated expectations.
Organizations risk deploying LLMs too quickly, without the semantic infrastructure needed to ensure consistency, trust, and explainability. Gartner specifically calls out the rise of the composite semantic layer as a transformational enabler for distributed analytics. We agree—and have been building toward this for years.
AtScale’s semantic layer isn’t platform-bound. It’s built to be open, reusable, and interoperable across any cloud data platform, BI tool, or AI agent interface.
AtScale’s Agentic BI Stack
Modeling Agent (One-Click Semantic Modeling): Our platform uses machine learning to infer relationships from raw data and generate governed semantic models automatically. These models include version control, lineage, and business logic out of the box—no manual SQL required.
Performance Agent (Autonomous Engineering): Our system continuously monitors query performance, recommends optimizations, and adapts metric definitions as needed. Analysts no longer need to tune models manually: our agent does it intelligently and transparently.
MCP Server (Model Context Protocol): Our Model Context Protocol (MCP) interface enables LLMs to access semantic business logic, including definitions, synonyms, hierarchies, and query patterns, without exposing raw data. This is the bridge between trusted semantic models and safe, explainable AI agent behavior.
These capabilities enable LLM agents to transition from syntactic pattern matching to semantic reasoning, paving the way for agentic BI that actually works in enterprise environments.
Agentic BI in Action: Use Cases With Impact
Autonomous Data Quality Monitoring: One Fortune 500 retailer used AtScale’s semantic-powered agents to reduce data quality incident resolution time from days to minutes. By using the semantic layer to define normal behavior, agents could flag and explain anomalies in near real time.
Cross-Platform Semantic Consistency: AtScale enables consistent business logic across Excel, Tableau, Power BI, and natural language interfaces. This allows agents to deliver the same KPI—say, “gross margin”—regardless of the delivery tool or audience.
Strategic Planning and Simulation: With access to governed semantic context, agents can simulate different outcomes and suggest proactive strategies. This shift in BI from descriptive to prescriptive is grounded in logic that business teams can trust.
Why Open, Governed Semantic Layers Matter
The market is converging around semantic infrastructure as a strategic priority. Gartner’s recognition of the composite semantic layer highlights the need for consistent, federated business logic that spans cloud platforms, BI tools, and AI systems.
Yet many organizations continue to adopt semantic solutions that are deeply tied to a single data or BI platform. Take, for example, vendor-specific implementations like Snowflake’s Semantic Views, Databricks’ Metric Views, or Google’s LookML. While these technologies can offer convenience within their respective ecosystems, they bind business logic to a single platform, creating new silos in the process. Metric definitions, governance rules, and hierarchies become trapped within proprietary frameworks, making it difficult to reuse or extend them across other tools or AI applications.
The impact is clear: teams using Excel or Tableau can’t rely on the same logic encoded in a Looker model. AI agents built on top of Databricks can’t access business context defined inside a Snowflake-native semantic layer. Over time, this fragmentation leads to inconsistent KPIs, conflicting definitions, and increased technical debt as teams rebuild the same logic multiple times in different formats.
AtScale’s approach is different. We support:
- Modeling via the open-source Semantic Modeling Language (SML) specification
- Standards-based AI alignment via Model Context Protocol (MCP)
- Platform-neutral deployment across Databricks, Snowflake, Google BigQuery, and more
This ensures that your business logic is reusable, explainable, and future-proof—whether consumed by a dashboard, a chatbot, or an autonomous agent. Open semantics eliminate the risk of vendor lock-in and give organizations the flexibility to evolve their stack without rewriting the foundation of their analytics strategy.
Conclusion: Build for the Agents That Come Next
The shift from static queries to agentic intelligence is already underway. But it won’t succeed on UI alone. It will depend on robust, governed, LLM-ready infrastructure—specifically, a semantic layer that brings structure, trust, and transparency to every AI interaction.
Organizations that invest in an open semantic architecture today will be ready to scale GenAI across BI, decision intelligence, and autonomous workflows tomorrow. Those who don’t will find themselves trapped in the next wave of hype, again.
If you want to enable truly intelligent agents, start by giving them something to understand.
Start with the semantic layer.
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