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# You need a contextual semantic layer for your Agentic AI systems.

**[Pavan Belagatti](https://daily.dev/sources/dfa0u7-fs)** · [@johhnypav](https://daily.dev/johhnypav) · 2 min read · 1 upvotes · 0 comments

## Summary

Traditional semantic layers designed for dashboards and static reporting fall short for agentic AI systems. A contextual semantic layer acts as an intelligent middle layer between raw data sources (cloud warehouses, transactional DBs, operational systems, unstructured data) and downstream consumers. It passes data through four governing layers — Metrics & Data Model, Ontology Layer, Context + Memory, and Knowledge Graph — adding structure, business meaning, and reasoning capability. This enriched data then powers AI agents, BI dashboards, AI analytics tools, and embedded data apps.

## Content

Traditional semantic layers built for dashboards and static reporting are no longer sufficient in the age of agentic AI. All you need is a contextual semantic layer in the age of Agentic AI. 

See, modern enterprises sit on mountains of data: structured, unstructured, transactional, and operational, yet still struggle to extract timely, trustworthy decisions from it. The Contextual Semantic Layer changes that.

At its core, it acts as the intelligent middle layer between raw data sources and the tools that consume them. 

As you can see, on the left, data flows in from Cloud Warehouses (Snowflake, BigQuery, Redshift), Transactional Databases (Postgres, MySQL), Operational Systems (Salesforce, SAP, Oracle), and Unstructured Data (PDFs, Email, Gong). 

Rather than passing this chaos directly to downstream tools, it passes through four governing layers, Metrics & Data Model, Ontology Layer, Context + Memory, and Knowledge Graph, each adding structure, business meaning, and reasoning capability.

Port sits at the center of this architecture, serving as the connective fabric that organizes, governs, and routes semantic context across the stack.

On the right, this enriched, governed data powers AI Agents & Automation (OpenAI, Anthropic, LangChain), BI Dashboards (Power BI, Tableau, Qlik), AI Analytics (Tellius, ThoughtSpot), and Embedded Data Apps (Jupyter, Metabase, Sigma).

Know more about semantic layer in this article: [https://www.tellius.com/resources/blog/from-metrics-to-meaning-the-evolution-of-the-semantic-layer-in-the-age-of-agentic-ai](https://www.tellius.com/resources/blog/from-metrics-to-meaning-the-evolution-of-the-semantic-layer-in-the-age-of-agentic-ai)

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Tags: [#langchain](https://daily.dev/tags/langchain), [#agentic-ai](https://daily.dev/tags/agentic-ai), [#data-architecture](https://daily.dev/tags/data-architecture)

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