Systems | Development | Analytics | API | Testing

AI coding agents have quickly become part of the daily workflow for Node.js developers. Whether you're using Claude Code, Codex CLI, OpenCode, Antigravity CLI, or Pi Agent, these tools are great at generating code, explaining implementations, and automating development tasks. But debugging production systems is a different challenge.

This post is the technical follow-up, written for solution architects and developers wanting to take the next step on leveraging Kong Konnect Metering & Billing's new prepaid credits in addition to post-consumption invoice billing.

A React Native build is really two builds stacked on top of each other. On Android, Gradle compiles your Kotlin and Java, then drops down into cmake and ndk-build to compile the C++ that ships inside Hermes, Folly, ReactCommon, and your own turbo modules. On iOS, Xcode does the equivalent: Swift and Objective-C on top, the same C++ underneath via Clang. Two build systems, two languages on top, one shared set of C++ underneath.

Your frontend can look flawless — fast search, clean filters, a map view that renders in under a second — and none of it will matter the day you connect your fifth MLS. Somewhere between the second and third regional feed, most PropTech teams hit the same wall: the code that worked beautifully for one market starts breaking in ways nobody predicted. A field that was always a number is suddenly a string. A boolean flag for “has pool” shows up as free text in the new feed.

Document-centric workflows have been difficult to automate and required human intervention. Attempts to automate document handling often failed or did not scale, because legacy intelligent document processing (IDP) systems were fragile. They often required manually retraining models on dozens of documents just to identify specific fields—only to repeat the process whenever a format changes. The result was a costly cycle of maintenance and manual data entry.

Since its inception, agentic AI has felt like a distant aspiration. Today, agents are here, and enterprise adoption is accelerating. Gartner predicts that by 2028, the average global Fortune 500 enterprise will have more than 150,000 AI agents in use, up from fewer than 15 in 2025. Agents arrive with incredible, broad intelligence, but lack the knowledge of your operating model: your customers, policies, approvals, exceptions, business rules, systems, and operational history.

Shadow AI detection is the practice of finding and governing unsanctioned AI tools, models, and API integrations that employees deploy without security approval. It has become urgent because these tools route live enterprise data to external models in real time, and traditional security stacks cannot see them. The 2026 Cordyceps disclosure, which exposed identical AI-generated vulnerabilities across 300+ GitHub repositories, showed how fast ungoverned AI can turn into a supply-chain crisis.

‍Build Cache can meaningfully shorten CI feedback loops, but only if it’s connected to workflows where it’ll actually make a difference. So how do you figure out which workflows will benefit? That’s the part that’s been easy to get wrong — until now. Quick Connect is a new feature that takes the guesswork out of estimating time savings: it looks at your last 30 days of build data and surfaces the workflows that will benefit themost from caching.