Systems | Development | Analytics | API | Testing

When a user asks your AI assistant a question, who actually runs the database query? In most enterprise AI deployments, the answer is troubling: a shared service account with broad access to everything. The user's identity evaporates the moment their request enters the AI system. This architectural pattern creates security gaps, compliance failures, and data leakage risks that undermine enterprise AI adoption.

70% of teams are already integrating generative AI tools into their daily workflows, according to our 2025 State of Game Technology Report. Now more than ever, teams are looking to connect their AI tools to the services and applications they rely on to get work done. To address this issue, the industry has begun to standardize using the Model Context Protocol (MCP) to connect their existing tools and LLMs like Claude, GPT, and Gemini.

Modern AI applications are no longer just about sending prompts to an LLM and returning text. As soon as AI systems need to interact with real business data, internal APIs, or operational workflows, the problem becomes one of orchestration, security, and control. The challenge is to build secure AI agents without embedding fragile logic or exposing sensitive systems directly to a model. This is where a layered architecture using Volcano SDK, DataKit, and Kong MCP Proxy becomes compelling.

If you have been writing tests for a while, you have probably encountered code coverage and test coverage. These concepts can be difficult to differentiate because they are somewhat intertwined. In this article, you will learn what code coverage vs test coverage means, and the basis of these concepts. You will also learn the key differences between code coverage and test coverage in Python. You would discover tools, techniques, and best practices to improve your testing strategy.

DreamFactory is a secure, self-hosted enterprise data access platform that provides governed API access to any data source, connecting enterprise applications and on-prem LLMs with role-based access and identity passthrough. Organizations working toward SOC 2 compliance face a familiar set of challenges: inconsistent access controls, fragmented data access security, noisy or incomplete logs, risky custom integrations, and difficulty proving governance during an audit.

Executive Summary: Organizations spend weeks or months manually reverse-engineering legacy databases into OpenAPI specifications before they can leverage SmartBear's powerful API toolchain. DreamFactory eliminates this bottleneck entirely.

Database performance is often the primary bottleneck in API-driven applications. Whether you're serving a mobile app, powering a microservices architecture, or exposing enterprise data through REST APIs, slow queries translate directly to poor user experience, increased infrastructure costs, and system scalability challenges. This guide explores proven query optimization strategies that development teams can implement to dramatically improve API performance.

Have you ever asked an AI assistant to generate code for a framework it doesn't quite understand? Maybe it produces something that looks right, but the syntax is slightly off, or it uses deprecated patterns. The AI is working hard, but it lacks the specific context it needs to truly help you. The Model Context Protocol (MCP) was designed to bridge this knowledge gap by giving AI assistants access to domain-specific knowledge and capabilities they don't have built in.

A customer recently shared their debugging workflow with me. When an error shows up in Honeybadger, they import it to Linear, manually add context about where to look in the codebase, then assign GitHub Copilot to investigate. It works, but they asked a good question: could Copilot just access Honeybadger directly? The answer is yes—and it's easier than I expected.