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.
This tool caters to teams building or deploying AI agents: it lets agents leverage internal data (to deliver context-rich outputs) while maintaining strict control over data access—addressing both efficiency and risk management needs.
Do you plan to expand compatibility to niche or industry-specific datasources (e.g., healthcare EHR databases, financial ledger systems) for specialized use cases? Also, will Pylar include pre-built access control templates aligned with common compliance frameworks (e.g., HIPAA, GDPR) to accelerate secure setup for regulated industries?
Love what Pylar is solving. As someone who works with founders handling fast growth + ops, I know how often “data safety + speed” becomes the invisible pressure behind the scenes.
Awesome! Is it possible to have an agent that sync my Notion page with notes of different customers to my CRM system Attio?
This is pretty cool, I have been working with some fintech companies and the sheer volume of data they have is ginormous.
I love how it abstracts the base layer queries and have complex queries converted to tools. amazing job guys! Will try it for sure
@Pylar Congrats on the launch! Securely connecting entire data stacks to AI agents is exactly what teams need as AI adoption scales.
Data security and access control are critical when agents interact with sensitive information. How does Pylar handle permission management across different data sources?
Are developers able to set granular access rules for specific agents or use cases? Curious about how the authentication flow works when connecting multiple platforms.
It's an amazing idea. So can the agent run analytical queries in the DB as well?
@hoshang_m Congratulations. And happy product launch.
@vishalbajaj Great product! All the best for your launch 🎉
Congrats Hoshang! Pylar seems like a huge step forward for safely connecting agents to structured data.
Hey Hoshang, congrats on the launch! That stat about 10% of MCP servers being malicious is wild. I’m curious was there a specific moment that made this feel urgent for you? Like did you witness (or hear about) an agent accidentally exposing customer data, or maybe a team get hit with a surprise warehouse bill they didn’t see coming?
Does Pylar throttle or rate-limit agent queries in any way? Congrats on the launch.
How do you monitor agent behavior across different builders (Cursor, LangGraph, n8n, etc.) from one place?
👋 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