AI Agents

AI Agents - coming soon Planned to Q3 2025 - we will release a full suite of feature that enable agents work smoothly with Lynk. Navigate through this section to learn more on what's coming

Data teams can use pre-built AI agents or build custom agents on top of Lynk's MCP and RAG framework.

Use cases

There are many possible use cases for AI agents - and how they can help data teams and data consumers succeed with data.

Below you can find some examples:

• Self-service analytics ("Ask me anything")

• Explain queries

• Build data (entities, features and measures)

• Perform root-cause analysis

• Perform predictions and apply advanced ML practices

• Detect and fix data quality issues

• Build data visualizations

• Manage data pipeline materialization logic

• Detect and alarm on accounts at risk for churning

• Fraud detection

and more.

High level architecture

AI agents need access to effective tools and the right context;

High level architecture - enabling AI agents with context and tools

Tools

Lynk’s Model Context Protocol (MCP) provides a rich set of tools powered by both the Semantic Layer and Context (RAG) system. These tools enable AI agents to reason over data, generate queries, and explain results in a way that aligns with your business logic and tribal knowledge.

Context

Lynk’s context system uses retrieval-augmented generation (RAG) to inject domain-specific knowledge into agent reasoning. This makes the system more accurate, explainable, and aligned with how your team actually works.

The role of the Semantic Layer

The Semantic Layer plays a central and critical role in enabling Agents with the right tools and context. LLMs, and Agents, are non deterministic by nature. They need a translation layer that translate natural language business terms to structured, governed and trusted SQL queries, and this is exactly the role of the Semantic Layer.