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.

Everything you need to know about securing AI agent data access in 2025. From understanding the risks to implementing proper governance, this guide covers prompt injection prevention, SOC2 compliance, and real-world security scenarios.

Most teams connect agents directly to databases and hope for the best. Here are the architecture patterns we've seen work in production—from sandboxed views to read replicas to MCP tools—that make secure agent access practical.

An agent data access layer is the governance system that sits between AI agents and your databases. This guide explains what it is, why you need one, and how to build it that actually works in production.

You don't need to code to build MCP tools. This tactical guide shows three ways to create them—from manual coding to Pylar's natural language approach—and why the simplest method takes under 2 minutes.

Should you build MCP tools or custom APIs for your agents? This guide compares both approaches head-to-head, showing when to use each and why MCP tools are usually the better choice for agents.

Data sandboxing creates isolated, controlled environments where AI agents can only access authorized data. This guide explains what it is, why it's essential, and how to implement it with modern architecture patterns.

A deep dive into ForcedLeak—the critical vulnerability in Salesforce Agentforce that allowed data exfiltration through indirect prompt injection. Learn what happened, how it worked, and how to prevent similar attacks.

Agent costs can spiral out of control fast. This practical guide for data engineers shows where costs come from, how to measure them, and strategies to optimize costs by 50-70% without breaking functionality.

Traditional RBAC fails for AI agents. This guide shows how to design Role-Based Access Control specifically for agents—with context-aware permissions, dynamic scoping, and instruction source validation.

Learn how to build a safe agent layer on top of Postgres. Three-layer architecture: read replica isolation, sandboxed views, and tool abstraction. Step-by-step implementation guide.

Learn how to build a secure MCP server on Supabase for AI agents. Implement RLS-protected views, connection pooling, and MCP tools to safely expose data.

A comprehensive guide to building a production-ready Postgres MCP server for AI agents. Covers connection pooling, sandboxed views, security layers, and deployment.

Learn how to build a secure MCP server on Stripe for finance and revenue ops agents. Implement sandboxed data views, API key isolation, and MCP tools to safely expose revenue data.

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.

Learn how to build a secure, custom MCP server for BigQuery in under 2 minutes using Pylar. No coding required.

Learn how to build a secure, custom MCP server for Snowflake in under 2 minutes using Pylar. No coding required.

Learn how to build a secure, custom MCP server for PostgreSQL in under 2 minutes using Pylar. No coding required.

Learn how to build a secure, custom MCP server for MySQL in under 2 minutes using Pylar. No coding required.

Learn how to build a secure, custom MCP server for Amazon Redshift in under 2 minutes using Pylar. No coding required.

Learn how to build a secure, custom MCP server for Supabase in under 2 minutes using Pylar. No coding required.

Learn how to build a secure, custom MCP server for HubSpot in under 2 minutes using Pylar. No coding required.

Learn how to build a secure, custom MCP server for Salesforce in under 2 minutes using Pylar. No coding required.

Learn how to build a secure, custom MCP server for Zendesk in under 2 minutes using Pylar. No coding required.

Learn how to build a secure, custom MCP server for Stripe in under 2 minutes using Pylar. No coding required.

Learn how to build a secure, custom MCP server for Intercom in under 2 minutes using Pylar. No coding required.

Learn how to build a secure, custom MCP server for PostHog in under 2 minutes using Pylar. No coding required.

Learn how to build a secure, custom MCP server for Google Analytics 4 in under 2 minutes using Pylar. No coding required.

Learn how to build a secure, custom MCP server for Google Ads in under 2 minutes using Pylar. No coding required.

A practical guide to building secure, governed AI agents that can access your data without compromising security or compliance.

The Model Context Protocol was supposed to be AI's breakthrough moment. Instead, it's become a masterclass in what happens when convenience eclipses security. 43% of MCP servers suffer from command injection vulnerabilities, 33% allow unrestricted network access, and 22% expose file systems beyond their intended scope.

A deep dive into how OpenAI uses reinforcement learning and automated red teaming to discover and patch prompt injection vulnerabilities in ChatGPT Atlas before attackers can exploit them.

How to build an automated ticket triage pipeline that enriches tickets with customer context, scores priority, and routes to the right team—turning a chaotic inbox into a structured workflow.

How to build a unified customer context view that joins data from CRM, billing, product analytics, and support systems—giving agents complete customer context in one query.

How to build a knowledge base agent that indexes support documentation and resolved tickets, provides instant answers, and learns from resolutions—turning documentation into an intelligent assistant.

How to build an intelligent routing system that analyzes ticket content, customer context, and agent expertise—automatically matching tickets to the right agent or team.

How to build an automated analytics pipeline that aggregates ticket data, identifies patterns, surfaces insights, and generates executive summaries—turning ticket data into actionable intelligence.

How to build an automated inventory forecasting pipeline that analyzes sales history, product trends, seasonal patterns, and external factors—generating accurate stock predictions and automated reorder recommendations.

How to build a recommendation agent that analyzes customer behavior, purchase history, browsing patterns, and product attributes—generating personalized recommendations in real-time.

How to build an automated fulfillment pipeline that analyzes order data, inventory locations, shipping costs, and delivery networks—optimizing fulfillment decisions and reducing costs.

How to build a dynamic pricing system that analyzes competitor prices, demand signals, inventory levels, and profit margins—automatically adjusting prices to maximize revenue and margin.

How to build an automated fraud detection pipeline that analyzes transaction patterns, customer behavior, device fingerprints, and historical fraud data—scoring risk in real-time and flagging suspicious transactions.

How to build an automated abandoned cart recovery pipeline that analyzes cart data, customer behavior, abandonment patterns, and recovery success rates—triggering personalized recovery campaigns at optimal times.

Why the next trillion-dollar opportunity in AI isn't better data access—it's capturing decision traces. Here's how Pylar sits in the execution path to build context graphs that make agents truly autonomous.