Securely connect your entire data stack to any agent
Pylar connects agents to your data stack, safely. Connect to any datasource, define exactly what an agent can see, turn those views into custom MCP tools, and publish them to any agent builder - with full observability across every AI deployment.
Super excited to finally share what we’ve been building.
Agents today are great at reading docs, invoices, websites, transcripts - but the moment you want them touching structured systems where sensitive customer data is stored e.g Snowflake, Postgres, CRMs… things get tricky.
We kept hearing the same two blockers over and over:
Agents may over-query and silently spike warehouse bills
Agents are at a risk of leaking sensitive data (PII, financials, customer history) because access isn’t properly scoped
And right now, teams have two options:
- Off-the-shelf MCP servers : 18,000 exist, ~10% are malicious, and most are exploitable or too generic for production. - Custom API wrappers : months of engineering bandwidth used up in building endpoints, policies, and governance… all brittle, fragmented, and hard to audit.
This forces companies into a painful choice: lock agents down so much they become useless, or open things up and risk a security incident.
Traditional database ACLs weren’t designed for autonomous systems. Custom APIs are hard to build, govern and control for agent level interactions.
Pylar exists to fix this. It’s a governed access layer between your agents and your entire data stack.
You connect your datasources → define sandboxed SQL views → turn them into MCP tools → ship them to any agent builder… all from one control plane, with full observability.
What you get out of the box:
Agent-specific sandboxed views (never raw DB access)
Enforced permissions & guardrails
Automatic breach containment + audit logs
Publish to any agent builder (n8n, Cursor, Claude, LangGraph, etc.) via a single secure link
We’re already working with some fantastic data, platform, and security teams - everything from internal analytics copilots to customer-facing AI features wired directly into production data.
If you’re exploring structured-data access for agents, I’d love to hear your thoughts, help you build your use case or just share best practices on what we've been seeing with our customers. You can book a call with me here if you'd like.
👋 Hey everyone, I'm Hoshang, Co-founder of Pylar.
Super excited to finally share what we’ve been building.
Agents today are great at reading docs, invoices, websites, transcripts -
but the moment you want them touching structured systems where sensitive customer data is stored e.g Snowflake, Postgres, CRMs… things get tricky.
We kept hearing the same two blockers over and over:
Agents may over-query and silently spike warehouse bills
Agents are at a risk of leaking sensitive data (PII, financials, customer history) because access isn’t properly scoped
And right now, teams have two options:
- Off-the-shelf MCP servers : 18,000 exist, ~10% are malicious, and most are exploitable or too generic for production.
- Custom API wrappers : months of engineering bandwidth used up in building endpoints, policies, and governance… all brittle, fragmented, and hard to audit.
This forces companies into a painful choice: lock agents down so much they become useless, or open things up and risk a security incident.
Traditional database ACLs weren’t designed for autonomous systems. Custom APIs are hard to build, govern and control for agent level interactions.
Pylar exists to fix this. It’s a governed access layer between your agents and your entire data stack.
You connect your datasources → define sandboxed SQL views → turn them into MCP tools → ship them to any agent builder… all from one control plane, with full observability.
What you get out of the box:
Agent-specific sandboxed views (never raw DB access)
Enforced permissions & guardrails
Automatic breach containment + audit logs
Publish to any agent builder (n8n, Cursor, Claude, LangGraph, etc.) via a single secure link
We’re already working with some fantastic data, platform, and security teams - everything from internal analytics copilots to customer-facing AI features wired directly into production data.
If you’re exploring structured-data access for agents, I’d love to hear your thoughts, help you build your use case or just share best practices on what we've been seeing with our customers. You can book a call with me here if you'd like.
Thanks for checking us out — means a lot. 🚀
- Hoshang
Co-founder, Pylar