A critical cloud application, serving thousands of users, had just been flagged for a security violation. No “hack” had occurred; nothing obviously was broken. What appeared to be a minor misconfiguration had quietly exposed sensitive data. The system was still running. The business was still operating. But compliance? Already compromised.

Introduction

Before diving into the programmatic details, first some concepts need to be explained. Do realize that in this blog, only the surface is scratched from what is possible with RabbitMQ. A detailed overview can be found in the official RabbitMQ documentation.

I have heard a version of this for fifteen years. The app ships. The customers are happy. The code is unfashionable. And somebody wants to tear it down and rebuild it on a stack that looks better on a resume.

Together, feature flags and observability unlock powerful insights by correlating code changes with real-time system behavior. This combination reduces time-to-diagnosis and builds greater confidence when rolling out new features.

Background Work With Constraints

The new com.codename1.background package schedules work that the OS runs when its conditions are met, mapping to Android JobScheduler and iOS BGTaskScheduler underneath. You describe what the work needs, not when to poll:

These features come from PR #5133 (WebSockets) and PR #5141 plus PR #5099 (the typed clients).

This is the inter-agent protocol problem: We have multi-agent frameworks, but no agreed-upon protocol for agents to communicate across frameworks. This post breaks down the four main approaches, compares them, and examines the need for standardization.

In our CRM system, we allow publishing long-form articles to a blog. However, users rarely want to read everything up front.

Human-in-the-loop (HITL) sounds like a single feature. In practice, it covers a wide design space: 

But before we dig into the new features, there's one number I'm particularly proud of…

Code Patterns in Spring Boot With Kafka

Spring for Apache Kafka gives me two primitives that feel pleasantly old Spring KafkaTemplate for sending and @KafkaListener for receiving. That template/listener model is intentionally similar to other Spring integration tech, which keeps application code focused on domain logic instead of raw client plumbing. 

Quick Intro

• Sign up at https://x.ai/api.
• Generate an API key from the console.
• Install pip install xai-sdk.
• Set env var: export XAI_API_KEY="your_key_here".
• Models list: https://docs.x.ai/developers/models.

I'll share some samples in Python.

This article will discuss the structure and usage of Kotlin Coroutines in detail. We will examine how to maximize Kafka message processing performance using Structured Concurrency principles and Resource Throttling techniques.

If this logic is within the code, between when-or-if-else blocks, every change means a new unit test process, code analysis, CI/CD pipeline work, and ultimately a "deployment."

They all have flaws common to these sorts of instruments: they are self-reported, self-scored, and self-interpreted. All pollsters know that self-reported data is inherently suspect. Pollsters and researchers have a name for what happens when people assess themselves without any external calibration: they call it bias, and it’s been studied exhaustively.

Because of that, the JVM loads the needed classes already linked and starts compiling the hot code paths immediately. The MovieManager application starts in less than half the time with these optimizations without any code changes.

A week later, the customer calls in frustrated. "Why did you close my case? My issue wasn't resolved."

Most papers about "agentic AI in production" stop where the problem starts: price. Interacting with Claude or GPT requires a natural pace setter: you, reading the generated text. Take the same agent out of chat mode and drop it into CI/CD, nightly batch, webhook handling; the pacing goes away, and you're running the thing purely on computer time, at computer prices.

The numbers look even scarier when you dig deeper. ReAct-style looped execution prepends all outputs to the following prompts, meaning you consume tokens at roughly O(n²). A PR review agent in a loop of three steps, which costs $0.04 in local development, can rack up a charge of $0.40+ once it gets stuck in its looping process. With hundreds of PRs a week, that could easily amount to five-figure surprises in your bill.

"2025 was meant to be the year agents transformed the enterprise, but the hype turned out to be mostly premature. It wasn't a failure of effort. It was a failure of approach."

— Kate Jensen, Head of Americas, Anthropic, TechCrunch, February 2026

Jensen's diagnosis is precise, and it matters that she made it in February 2026 — twelve months after the agent deployment wave crested. The teams that struggled in 2025 weren't short on ambition or resources. They were short on a coherent architecture for deciding what to build, what to buy, and how to govern the seam between the two.

Spring’s TestContext framework can cache and reuse ApplicationContext between test classes, but only if the effective test configuration is the same. If the configuration differs, Spring has to create another context. In large enterprise applications, this can become expensive very quickly.