The 5 layers of safely connecting agents to your databases:
Most AI agents need access to structured data (CRMs, databases, warehouses), but giving them database access is a security nightmare. Here's a layered architecture that addresses this:
Layer 1: Data Sources
Your raw data repositories (Salesforce, PostgreSQL, Snowflake, etc.). Traditional ETL/ELT approaches to clean and transform it needs to be done here.
Layer 2: Agent Views (The Critical Boundary)
Materialized SQL views that are sandboxed from the source acting as controlled windows for LLMs to access your data. You know what data the agent needs to perform it's task. You can define exactly the columns agents can access (for example, removing PII columns, financial data or conflicting fields that may confuse the LLM)
These views:
• Join data across multiple sources
• Filter columns and rows
• Apply rules/logic
Agents can ONLY access data through these views. They can be tightly scoped at first and you can always optimize it's scope to help the agent get what's necessary to do it's job.
Layer 3: MCP Tool Interface
Model Context Protocol (MCP) tools built on top of agent data views. Each tool includes:
• Function name and description (helps LLM select correctly)
• Parameter validation i.e required inputs (e.g customer_id is required)
• Policy checks (e.g user A should never be able to query user B's data)
Layer 4: AI Agent Layer
Your LLM-powered agent (LangGraph, Cursor, n8n, etc.) that:
• Interprets user queries
• Selects appropriate MCP tools
• Synthesizes natural language responses
Layer 5: User Interface
End users asking questions and receiving answers (e.g via AI chatbots)
The Flow:
User query → Agent selects MCP tool → Policy validation → Query executes against sandboxed view → Data flows back → Agent responds
Agents must never touch raw databases - the agent view layer is the single point of control, with every query logged for complete observability into what data was accessed, by whom, and when.
This architecture enables AI agents to work with your data while maintaining:
• Complete security and access control
• Reduces LLMs from hallucinating
• Agent views acts as the single control and command plane for agent-data interaction
• Compliance-ready audit trails
If you're building agents that touch sensitive customer information stored across your data stack, Pylar can help!
Hoshang07•1mo ago
Most AI agents need access to structured data (CRMs, databases, warehouses), but giving them database access is a security nightmare. Here's a layered architecture that addresses this:
Layer 1: Data Sources Your raw data repositories (Salesforce, PostgreSQL, Snowflake, etc.). Traditional ETL/ELT approaches to clean and transform it needs to be done here.
Layer 2: Agent Views (The Critical Boundary) Materialized SQL views that are sandboxed from the source acting as controlled windows for LLMs to access your data. You know what data the agent needs to perform it's task. You can define exactly the columns agents can access (for example, removing PII columns, financial data or conflicting fields that may confuse the LLM)
These views: • Join data across multiple sources • Filter columns and rows • Apply rules/logic
Agents can ONLY access data through these views. They can be tightly scoped at first and you can always optimize it's scope to help the agent get what's necessary to do it's job.
Layer 3: MCP Tool Interface Model Context Protocol (MCP) tools built on top of agent data views. Each tool includes: • Function name and description (helps LLM select correctly) • Parameter validation i.e required inputs (e.g customer_id is required) • Policy checks (e.g user A should never be able to query user B's data)
Layer 4: AI Agent Layer Your LLM-powered agent (LangGraph, Cursor, n8n, etc.) that: • Interprets user queries • Selects appropriate MCP tools • Synthesizes natural language responses
Layer 5: User Interface End users asking questions and receiving answers (e.g via AI chatbots)
The Flow: User query → Agent selects MCP tool → Policy validation → Query executes against sandboxed view → Data flows back → Agent responds
Agents must never touch raw databases - the agent view layer is the single point of control, with every query logged for complete observability into what data was accessed, by whom, and when.
This architecture enables AI agents to work with your data while maintaining: • Complete security and access control • Reduces LLMs from hallucinating • Agent views acts as the single control and command plane for agent-data interaction • Compliance-ready audit trails
If you're building agents that touch sensitive customer information stored across your data stack, Pylar can help!