Agentic Knowledge Layer

Unlike traditional data catalogs or analytics tools, the Agentic Knowledge Layer is purpose-built to power autonomous and semi-autonomous AI agents by combining data governance, metadata from the data catalog, and data products into a declarative, automated system.

In today’s enterprise, most critical decision-making data is structured. Large Language Models (LLMs) excel at working with unstructured text, but they lack awareness of schemas, joins, metrics, and relationships. This gap creates significant risks: hallucinations, inaccurate SQL generation, and unreliable analytics. An Agentic Knowledge Layer fills this gap by grounding AI in contextual metadata—ensuring that AI agents can deliver accurate, compliant, production-ready insights instead of misleading approximations.

Simply put: without an Agentic Knowledge Layer, enterprise AI sounds right but often isn’t. With it, AI becomes reliable, explainable, and aligned to business context.

Core components of the Agentic Knowledge Layer

An Agentic Knowledge Layer integrates several essential elements:

1. Metadata

Metadata provides the semantic context agents need to interpret data correctly. It describes schema, relationships, lineage, joins, and meaning. With metadata, agents stop guessing and start executing queries with accuracy. Experiments show a 25–30% lift in Text2SQL accuracy when enterprise AI is grounded in metadata.

2. AI agents and tools

Agents are the execution layer—autonomous or semi-autonomous systems that query data, generate insights, and automate tasks. In an enterprise setting, agents must be governed, precise, and customizable. They need context to understand business-specific rules, policies, and metrics.

2. Data products

AI agents are only as powerful as the data that fuels them. Data products are designed to address specific business questions. These curated, high-quality datasets, packaged for reuse, power both chat interfaces and specialized agents. By turning data into products, organizations can provide reliable building blocks for analytics, forecasting, and operational automation.

3. Governance

For AI to be trusted, data governance must be declarative and automated. Policies should not be applied manually but instead embedded into the knowledge layer so that agents inherit rules by default. This ensures compliance, reduces risk, and accelerates time-to-insight.

Benefits of the Agentic Knowledge Layer

Because the Agentic Knowledge Layer supports Alation’s Chat with Your Data and Agent Builder, these capabilities are:

• Accurate: Grounded in governed metadata. Built-in testing, evaluation sets, and custom judges ensure 90%+ accuracy for production, with explainable outputs you can trust.

• Integrated: Works seamlessly with 100+ data sources like Snowflake, Databricks, Tableau, Power BI, and more. Fully vendor-neutral and ready to evolve with your stack. Customizable: Use popular models like Claude, GPT, Gemini—or bring your own. Start with prebuilt agents (Query, Catalog Search, Deep Research, Dashboard Agents) and extend with tools for querying, charting, and curation.

• Enterprise-ready: Deploy anywhere via MCP or API. Embed into workflows or white-label for customers. Security, governance, and lineage come built in and extend through deployment.

Additional benefits include:

• Precision: Agents generate accurate outputs when guided by metadata.

• Adaptability: Organizations can adapt the knowledge layer to their unique business logic and evolve it with their tech stack as new use cases and tools arise without the risk of vendor lock in.

• Data sovereignty: Knowledge stays within the enterprise, ensuring no vendor lock-in.

How different teams benefit from the Agentic Knowledge Layer 

The Agentic Knowledge Layer sits at the intersection of data management, analytics, and AI engineering teams.

For data management leaders

• Democratize governed, trusted data through data catalogs, governance, and quality controls.

• Standardize and enrich metadata to eliminate silos and extend interoperability.

• Ensure compliance and lineage across systems.

For business analysts

• Deliver insights and business-facing data products through a Data Products  Marketplace.

• Use Chat with Your Data to interact naturally with governed datasets, extracting trusted insights that drive innovation and data-driven decision making.

• Build repeatable, production-ready dashboards and metrics.

For AI builders and engineers

• Use the AI Agent SDK to create precision data agents that operate on enterprise data.

• Leverage LLMs, open-source models like LLaMA, and proprietary AI systems.

• Deploy AI agents that are reliable, customizable, and compliant.

Together, these capabilities empower every team to work smarter, faster, and with greater trust in AI-driven insights.

Technical foundation

The Agentic Knowledge Layer is powered by a metadata lakehouse that connects with 100+ systems across cloud warehouses, BI platforms, and ML environments. This unified foundation enables AI agents and tools to access contextualized enterprise knowledge without friction.

The Agentic Knowledge Layer powers trusted AI

Alation uniquely enables the Agentic Knowledge Layer by combining:

• Data catalog: Centralized metadata and governance.

• Data governance: Automated, declarative policies.

• Data quality: Integrity checks and automated lineage tracking.

• AI-ready integrations: 100+ connectors, metadata lakehouse, and agent SDKs.

With Alation, enterprises can make AI accurate, explainable, and trusted—transforming metadata into a competitive advantage.