One of the biggest blockers to building agents is getting the data 'agent-ready'. Teams spend months building pipelines, wiring up sources, cleaning data, and centralizing it - before an agent can even ask its first question.
Pylar now does this out of the box.
We re source-agnostic. Whether your data lives across multiple databases and warehouses (Supabase, Snowflake, MySQL, etc.), you can connect one or many instantly, no re-architecture required.
If you don't have a warehouse yet, we ve got you covered. Pylar ships with 100+ built-in integrations across marketing tools, CRMs, support platforms, product databases, and billing systems. Data comes in cleaned, transformed, and centralized, ready for agents to work with.
Next up is agent views - once you've connected to your sources, you can write SQL across or within to create precise, sanitized, sandboxed views purpose built for specific agents.
Agents don t roam your databases arbitrarily. You deterministically scope exactly what fields they can access, so they do their job well, without hallucinating or giving you different answers for the same/similar questions.
Give it a try and let me know what you think!
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
Swytchcode
It's an amazing idea. So can the agent run analytical queries in the DB as well?
Pylar
@chilarai agents can run analytical queries, but only within the sandboxed view you expose to them.
So if you include things like aggregates, joins, or computed fields in that view, the agent can use them freely. What it can’t do is hit your raw warehouse or run heavy, unscoped analytics outside the boundaries you’ve set.
Think of it like giving the agent a curated data view purpose built for the agent to do its task well.
Here's more on this- https://docs.pylar.ai/learn/creating-data-views/overview
Swytchcode
@hoshang_m hmm, I get it now
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.
Pylar
@cynthia_ombati Thanks!
Sandboxed views instead of raw DB access feels like the right primitive.
Does Pylar throttle or rate-limit agent queries in any way? Congrats on the launch.
Pylar
@himani_sah1 Great question and thanks for the support. Rate limiting queries is going to be live on the product next week, but for now you can set additional guardrails like row limits and scoped filters with policies between your data and the mcp tools so an agent can’t over-query or wander outside the slice of data you’ve exposed. And every attempt gets logged so you can see if an agent is starting to push its boundaries.
Trace-AI
Airbook AI
@sriramg appreciate your support Sriram. We support a wide range of databases, data warehouses and Business Applications. The list of supported data sources can be found here - https://docs.pylar.ai/learn/making-connections/supported-data-sources.
Pylar
@sriramg yes! You can find the full list here - https://docs.pylar.ai/learn/making-connections/supported-data-sources - We're constantly adding more connectors. Are you looking for anything specific?
Great launch. congrats. Do you have custom guardrails option too?
Airbook AI
@ashish_dubey11 Thanks for your support Ashish. We do have Query-level guardrails on the tool itself.
We let you set limits on row counts, frequency, and regulate access via policies. If an agent tries to exceed it, Pylar shuts it down and logs the attempt. Are there any more guardrails that you would like to add between your data sources and the AI agent?