Blog

We've seen teams rush to connect AI agents directly to databases, only to discover the real costs: security risks, governance nightmares, and agents making expensive mistakes. Here's what we learned and why a structured layer matters.

Most AI agent projects fail not because of the models, but because agents can't reliably access the right data at the right time. We break down the common failure patterns and how structured data views solve them.

Internal AI agents are becoming the new operating system for modern teams. We explore how ops, RevOps, and support teams are using agents to automate workflows and get answers faster.

APIs are too rigid, databases are too risky. We believe structured endpoints—governed views that agents can query safely—are the missing piece that makes AI agents actually work in production.

The modern analytics stack isn't just about dashboards anymore. It's about turning data into views, views into tools, and tools into agents that can act on insights autonomously.

See how to build an agent that monitors customer health by combining usage data, support tickets, revenue metrics, and risk signals into a single, actionable view.

Build an agent that helps your sales team prepare for meetings, identify at-risk deals, and spot pipeline shifts before they become problems.

Create an agent that helps your support team triage tickets faster by pulling customer context, summarizing issues, and flagging high-priority cases automatically.

Build an agent that tracks marketing performance across channels by combining CRM data with product usage, giving you true attribution insights without the manual work.

Monitor product activation in real-time with an agent that tracks onboarding flows, identifies drop-off points, and surfaces adoption signals before customers churn.

Start every day with an agent that pulls together pipeline health, revenue trends, customer metrics, and risk signals into a single morning briefing.

Get started with Pylar in minutes. We'll walk you through creating your first SQL view, connecting a data source, and setting up basic access controls.

Turn your data view into an MCP tool that agents can actually use. This step-by-step guide shows you how to publish a view as a tool in under 10 minutes.

Deploy your Pylar MCP tool to OpenAI's Agent Builder so you can use it in custom GPTs and agent workflows. We'll show you exactly how to connect and test it.

Pylar works with all the major data sources. Learn how to connect BigQuery, Snowflake, and Postgres, and what to consider when building views across different systems.

Set up evals to monitor how agents are using your data. We'll show you how to track query patterns, identify anomalies, and improve agent performance over time.

From data source to working agent in 60 minutes. This end-to-end tutorial walks you through building a complete internal agent workflow using Pylar and your favorite agent builder.