If it feels like everyone is suddenly talking about agentic AI, you’re not wrong. In just the past few months, some of the biggest names in data have signaled a shift in how we think about analytics, automation, and intelligence:
- Snowflake introduced a new capability called Semantic Views, which allows users to define business logic natively in the platform, validating the semantic layer as core infrastructure for AI-powered BI.
- Databricks announced Metric Views, its version of a semantic layer. They shared their agentic vision in the announcement of Agent Bricks, a new framework for building AI agents that reason and act autonomously, leveraging semantic context from LakehouseIQ and Unity Catalog.
- Google reiterated the foundational role of knowledge graphs and structured semantics in making LLMs more explainable and trustworthy.
- Recent research from Gartner and Forrester highlights the increasing importance of semantic layers in enterprise AI governance, particularly in enabling trust, context, and interoperability at scale.
This momentum marks a turning point. But for us at AtScale, it’s not a reinvention, it’s a recognition.
We’ve been building toward this moment for over 12 years.
Semantics as the Foundation for Trustworthy AI Agents
What the industry calls “agentic AI” today is ultimately an evolution of something we’ve always believed: analytics systems should be intelligent, explainable, and grounded in business logic. Not just reactive, but proactive. Not just automated, but trustworthy.
The key to that? Semantics.
We’ve spent the last 12 years building the semantic layer that makes this intelligence possible. In 2013, we weren’t using the words “AI agents” or “agentic architectures,” but we were solving for the exact same thing: how do we model knowledge in a way that scales across people, platforms, and now, AI?
The recent industry validation signals to the broader market what our customers have known for years: semantics are the connective tissue between data and decisions.
AtScale’s Approach to AI Agents: Enterprise-Grade by Design
So, how is AtScale building toward this future?
Our AI Business Intelligence Agents aren’t a new product; they’re the natural extension of what we’ve always done. But today, we’re supercharging them with three core innovations that enable true autonomous analytics:
AtScale Modeling Agent (aka One-Click Modeling)
Our one-click modeling capability isn’t just about convenience; it’s a foundational step toward enabling agentic semantic model building at scale. In today’s fast-moving AI landscape, organizations need a way to rapidly build trusted data models that power AI agents with context and consistency.
AtScale’s AI-powered modeling engine does just that. With a single click, it:
- Analyzes raw cloud data sources
- Infers relationships and key business logic
- Auto-generates governed semantic models ready to be consumed by BI tools and AI agents alike
What sets this apart is that the resulting model is not only fast but also enterprise-grade for production. It adheres to enterprise governance standards, supports explainability for AI, and ensures consistency across every interface, whether a dashboard or a conversational agent.
As the demand for semantic-aware, autonomous systems grows, AtScale’s one-click modeling is becoming essential for operationalizing agentic AI.
Performance Agent (aka, AtScale Autonomous Engineering)
Gone are the days of data extracts and proprietary data silos to support speed of thought querying.Our AI agents can optimize queries, recommend new metrics, and even self-correct when inconsistencies arise. This isn’t about replacing people, it’s about augmenting their capabilities with automation that’s built on trust.
AtScale MCP Server
The AtScale MCP Server implements the Model Context Protocol (MCP), a standard pioneered by Anthropic to provide AI models with structured, real-time access to contextual information. By exposing a governed semantic layer through this protocol, the AtScale MCP Server allows large language models (LLMs) and AI agents to interact with enterprise data in a controlled, consistent, and interpretable way.
This enables a wide range of integrations—from natural language querying of business metrics to agent-driven analytics workflows—while preserving data integrity, security, and metric consistency across tools and users.
The Real Challenge: Enterprise-Readiness
It’s tempting to chase the hype, but building AI agents that work for the enterprise is hard. It requires accuracy, governance, lineage, and security. It also requires a way to bridge the gap between natural language and business logic without compromising on standards.
AtScale offers exactly that. We’ve seen up to 100% accuracy when business users query data through AI interfaces connected to our semantic layer. That starkly contrasts with the 80+% failure rates we see from direct LLM-based querying without context.
Looking Ahead: What Comes After the Hype
I’m glad the industry is catching on. The fact that we’re seeing terms like “semantic layer,” “knowledge modeling,” and “AI agents” in more headlines is a good thing. It means the conversation is maturing. But with that maturity comes responsibility.
AtScale’s job isn’t to chase trends, it’s to build durable, trustworthy foundations that help enterprises lead with intelligence. And as the world leans into agentic architectures, we’ll keep doing what we’ve always done: enabling context-aware, governable, interoperable analytics for everyone, from dashboards to AI agents.
Because at the end of the day, semantics isn’t just a feature. It’s the architecture for the AI era.
Interested in seeing how AI agents can work in your enterprise? Explore how AtScale’s semantic layer empowers autonomous analytics without sacrificing trust.
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