Pylar
The secure data layer for AI agents (MCP Guardrails)
Building AI agents is easy; giving them safe access to production data is terrifying. Pylar solves the 'Integrity Gap' in the Agentic AI stack. While tools like **LangChain** focus on orchestration, Pylar acts as a firewall, ensuring your agent doesn't accidentally download your entire user table or execute a $5,000 query on **Snowflake**. By turning governed SQL views into **MCP tools**, it creates a secure bridge that tools like **Cursor** and **Claude** can use immediately. It's a must-have infrastructure piece for any team moving agents from prototype to production.
Why we love it
- Prevents 'Runaway Agent' scenarios that spike Snowflake/DB bills
- Native MCP support makes it plug-and-play for Cursor and Claude users
- Granular permissions allow row-level security without building custom APIs
Things to know
- Another infrastructure layer to maintain in the LLM stack
- Success depends on the adoption of the MCP standard
- Currently focused on structured data, less on unstructured docs
About
Pylar provides a governed access layer between your AI agents and your data stack. It allows developers to create sandboxed SQL views and expose them as secure MCP (Model Context Protocol) tools, preventing agents from over-querying databases or accessing sensitive PII.
Key Features
- ✓Sandboxed SQL Views for Agents
- ✓Native MCP Tool Generation
- ✓Query-Level Guardrails (Row/Rate Limits)
- ✓Audit Logs & Cost Tracking
- ✓Integration with Cursor/Claude/LangGraph