Systems | Development | Analytics | API | Testing

PostgreSQL MCP Server: Setup, Security & Best Practices for AI Agents

Last updated: May 2026 A PostgreSQL MCP server is a service that exposes PostgreSQL databases as tools an AI agent can call through the Model Context Protocol (MCP). Rather than giving an LLM direct database credentials, you put an MCP server between the agent and the database. The agent discovers what queries it can run, calls them as named tools, and the MCP server translates those calls into safe, governed SQL against PostgreSQL.

Scale AI test automation without losing visibility | QMetry + Reflect integration

AI is changing how testing gets done. As automation grows, so does the complexity of tracking what’s been tested, what passed, and what’s ready to release. See how SmartBear Reflect and QMetry work together to scale AI-powered test automation without losing visibility or control. Reflect makes it easy to create and run automated tests using plain language, while QMetry brings structure to that speed, connecting tests, results, and reporting into a single system of record.

How to set up Billing for AI Agents with LangChain and Kong in 15 Minutes | Monetize AI Agents

Want to bill customers for the AI tokens they actually use? This video shows you how to set up a LangChain app that meters LLM token usage and streams it to Kong Konnect Metering & Billing as CloudEvents — turning every prompt and response into invoiced usage, automatically.

Stop Subsidizing Innovation, Start Monetizing It

The ‘AI Credit’ Economy: GitHub’s Pricing Shift Is the Beginning, Not the Exception *GitHub just sent waves of budget panic across its developer base. Seat-based Copilot pricing is out. Consumption-based credits are in. And if you're building an AI-driven product today on flat-rate pricing? You're building a problem into your roadmap.* Seats aren't going away, but they now fund a shared pool of AI credits (one credit = one cent) instead of unlocking uncapped use.

Self-Healing Test Automation: How It Works And How To Implement It

Your team ships a UI update on Monday. By Tuesday morning, 47 automated tests are failing and half of them are not real bugs. They broke because a button ID changed from confirmButton to confirm-purchase-btn. Your engineers spend hours figuring out what is an actual regression and what is just a broken locator. Self healing test automation solves this by allowing tests to automatically recover from UI changes, locator failures, timing issues, and API schema updates without constant manual fixes.

AI Agent Integration: Gartner Research Confirms Need for AI Control Layer

Three-quarters of enterprises are now piloting or deploying AI agents. But here’s the problem: actually integrating those agents with enterprise applications is proving to be one of the hardest parts of the whole endeavor. The research doesn’t mince words about the challenge. And it maps directly to the infrastructure gap Kong was built to address..

AI code created a new testing problem | From the Bear Cave Ep. 3

SmartBear’s study Closing the AI software quality gap found that 60% of teams have already experienced quality issues tied to AI-generated code, evidence of how increased abstraction is changing how software gets built. When development shifts from well-defined requirements to prompts and generated outputs, it becomes much harder to understand what the system is actually supposed to do, and what you should be testing against.

The 8 Best API Documentation Examples | DreamFactory

Last Updated: May 2026 Your API documentation is just as important as your API itself. It defines how easy it is for users to learn, understand, and use your open-source or paid product. In this post, DreamFactory highlights eight of the best API documentation examples from well-known tools. These examples can serve as inspiration for creating effective, developer-friendly API documentation.