Blog

The Hidden Cost of Giving AI Raw Access to Your Database

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.

Why Agent Projects Fail (and How Data Structure Fixes It)

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.

The Rise of Internal AI Agents for Ops, RevOps, and Support

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.

Structured Endpoints: The Missing Layer Between Data and AI Agents

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 New Analytics Stack: Data → Views → Tools → Agents

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.

The Complete Guide to Agent Data Security (2025 Edition)

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.

Secure Agent Database Access: Architecture Patterns That Actually Work

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.

What Is an Agent Data Access Layer? A Practical Guide

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.

How to Build MCP Tools Without Coding

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.

MCP Tools vs Custom APIs: What's Better for Agents?

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 for AI Agents: Modern Architecture Guide

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.

ForcedLeak: How a $5 Domain Purchase Exposed Critical AI Agent Security Flaws

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 Cost Optimization: A Data Engineer's Guide

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.

Designing RBAC for AI Agents: The Complete Framework

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.

How to Build a Safe Agent Layer on Top of Postgres

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.

Building a Supabase MCP Server for AI Agents

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.

How to Build a Postgres MCP Server for AI Agents

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

Creating a Personalized Product Recommendation Agent: From Customer Behavior to Recommendations

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