Agentic testing tools help teams plan, generate, adapt, and run tests with far less manual effort. They’re quickly becoming part of how modern QA scales without slowing delivery.

One thing to get right from the start is scope. Not all agentic testing tools operate at the same level of scope or strategic impact. They vary significantly in what they do and where they fit. Some are point solutions that help you author or run tests faster. Others sit inside broader AI-driven quality platforms that prioritize risk, optimize test portfolios, and enforce quality gates across the pipeline.

Leveraging a lightweight SKILL.md file for declarative workflows (with progressive context loading to minimize token usage), parallel sub-agents for specialized checks (security, performance, style, and documentation), and a companion local MCP server exposing deterministic tools (linting, GitHub PR fetching, and vulnerability scanning), the system achieves consistent, auditable, and scalable reviews with minimal manual intervention.

This guide maps the specific metrics, alarm thresholds, and configuration steps for each service, and then addresses the observability delta that CloudWatch leaves unresolved: cross-service correlation, root-cause traceability, and the capacity-planning intelligence that prevents cascades in the first place.

Every industry has its version of the same data engineering problem: massive, complex payloads generated at the edge — far from the cloud, often on unreliable networks — that need to become queryable, structured datasets as fast as possible. In genomics, it is multi-gigabyte sequencing files produced by instruments in labs. 

In autonomous vehicles, it is LiDAR and camera telemetry streaming off test fleets. The underlying architectural challenge is the same in every case: ingest heavy data at burst scale, store it cost-effectively for years, and transform it into something an analyst or ML model can actually use without touching the raw files.

You bought the GPUs. Maybe you've got a couple of NVIDIA A100s in a rack, some RTX 4090s under desks, or a Kubernetes cluster with mixed hardware. You've got the compute. Congratulations!

Now what?

When migrating or running SAP S/4HANA on AWS, many organizations fixate on EC2 instance prices and assume that choosing the cheapest instance types will yield the biggest savings. In reality, cloud TCO is heavily impacted by landscape design choices, how many environments you run, how they’re sized, how data is managed and what auxiliary services you use. Cutting cloud costs isn’t just about shrinking VM sizes it’s about architecting an efficient SAP landscape. As one SAP FinOps guide notes, focusing only on instance sizing addresses symptoms, not causes. True cost optimization asks Is the SAP landscape design efficient? Are you running unnecessary SAP instances, and can workloads consolidate onto fewer systems?. In other words, a thoughtful landscape architecture often yields larger savings than a simple per-server cost reduction.

Understanding an SAP S/4HANA Landscape on AWS

A typical S/4HANA landscape consists of multiple tiers and environments. You might have separate DEV, QA, Staging and Production systems each a full SAP stack with its own HANA database and application servers. On AWS, that could translate to dozens of EC2 instances, along with associated storage and network infrastructure. Each additional environment or system copy multiplies costs for compute, Amazon EBS storage, Amazon EFS shared file systems, backup retention, and so on. Landscape design decisions such as how many parallel systems to run or whether every environment needs high availability can quickly outweigh the cost of an individual EC2 instance.

Can you add a way to mock Kafka in your app? I need something simple, just a way for me to produce messages so my app can consume them. I just don't want to spin up a real Kafka for it.

]]> Tue, 19 May 2026 19:00:00 GMT https://dzone.com/articles/3646855 Roman Dubinin https://feeds.dzone.com/link/23566/17343677/application-deployment-strategies Modern application development has moved toward distributed, cloud-based, and even microservices-based applications, requiring scalability, reliability, and performance under different conditions. Therefore, deployment has become a part of application development, not merely a final activity.

Intelligent deployment patterns and practices are all about building applications that are not just easy to deploy, but also reliable, scalable, and efficient in production. This means moving away from traditional, manual deployment patterns and toward automated, container-based deployment practices.

]]> Mon, 18 May 2026 18:00:00 GMT https://dzone.com/articles/3644790 Manju George https://feeds.dzone.com/link/23566/17343544/genkit-middleware-ai-pipelines If you have been building anything non-trivial with Genkit, you have probably bumped into the same set of cross-cutting concerns over and over again: retrying transient model errors, falling back to a cheaper model when quota explodes, gating tool execution behind human approval, injecting filesystem access for coding agents, logging every request and response for observability...

Until now, you ended up either wrapping ai.generate() calls by hand or writing ad-hoc helpers that ended up duplicated across flows. The new Genkit Middleware changes that. It introduces a first-class, composable middleware layer for the generate() pipeline, with hooks for the model, the tool execution, and the high-level generation loop, plus a small but very useful set of official middlewares published in the brand new @genkit-ai/middleware package.

]]> Mon, 18 May 2026 14:30:00 GMT https://dzone.com/articles/3654577 Xavier Portilla Edo https://feeds.dzone.com/link/23566/17343484/spring-crud-generator-v110-field-validation-redis I’ve just released Spring CRUD Generator v1.1.0 — an open-source generator that helps you bootstrap a Spring Boot CRUD backend from a single YAML specification.

If you’ve built more than a couple of CRUD-heavy services, you’ve probably experienced the same pain points: repeating the same layers (entity, repository, service, controller), keeping consistent naming and structure across modules, and constantly adjusting boilerplate when requirements change. Spring CRUD Generator aims to reduce that overhead by letting you define your data model and project options once (in YAML) and generate a consistent project structure around it.

]]> Mon, 18 May 2026 14:00:01 GMT https://dzone.com/articles/3638294 Marko Zivkovic https://feeds.dzone.com/link/23566/17343445/multi-cloud-lessons-aws-gcp-azure It started, as most bad architectural decisions do, with a PowerPoint slide from a VP who had just returned from a conference. “We need to avoid vendor lock-in,” he declared, and suddenly our platform engineer team had a mandate to distribute workloads across three public clouds. Eighteen months later, we had something that technically ran on three major public clouds (AWS, GCP, and Azure). We also had a Terraform code that made people cry and an on-call rotation nobody wanted.

This is what I learned about multi-cloud strategy, not the vendor pitch but the messy reality of keeping production alive across multi-cloud boundaries.

]]> Mon, 18 May 2026 13:00:00 GMT https://dzone.com/articles/3646955 Pruthvi Raj Seknametla https://feeds.dzone.com/link/23566/17342018/mcp-vs-a2a-vs-agui If you're building AI agents in 2026, you've probably bumped into at least one of these acronyms: MCP, A2A, AG-UI. Maybe all three. And if you're anything like me, your first reaction was: "Are these competing standards? Do I need all of them? Which one do I actually use?"

Here's the short answer: They're not competing — they're complementary. Each one solves a different problem at a different layer of the agent architecture. Think of them like TCP, HTTP, and HTML — different protocols at different layers that work together to make the web function.

]]> Fri, 15 May 2026 16:00:00 GMT https://dzone.com/articles/3651194 Jubin Abhishek Soni https://feeds.dzone.com/link/23566/17341899/designing-effective-meetings-in-tech-strategic If you’ve been in software engineering long enough — especially as a senior, staff engineer, architect, or tech executive — you’ve felt it: meetings that drain energy, fragment your focus, and somehow still fail to move anything forward.

The irony is almost historical. The word meeting comes from the Old English mētan — “to encounter” or “to come together with purpose.” Yet in modern organizations, especially in IT, meetings often represent the opposite: diffusion instead of direction.

]]> Fri, 15 May 2026 13:00:02 GMT https://dzone.com/articles/3651193 Otavio Santana https://feeds.dzone.com/link/23566/17341234/working-with-cowork-dont-be-confused TL;DR: Understand the Claude Desktop Architecture and Save Time

You configured Claude in Claude Desktop, wrote instructions, uploaded reference files, and set your preferences. Then you clicked the Cowork tab.

Unfortunately, Claude had no memory of what you just did. Your instructions were gone, as were your files and preferences. You assumed this was a bug, but it is a feature: You switched applications.

]]> Thu, 14 May 2026 13:30:01 GMT https://dzone.com/articles/3654491 Stefan Wolpers https://feeds.dzone.com/link/23566/17339925/kiro-feature-to-requirements-design-tasks The agentic IDE space has gotten crowded fast. Cursor, Claude Code, Copilot, Windsurf — they all share the same core model: you type a prompt, the AI writes some code, you iterate. It works well for prototyping. It breaks down when you're building production systems on a large codebase with a team of more than one.

AWS Kiro takes a different bet. Instead of chat-first, it's spec-first. The unit of work isn't a prompt — it's a structured specification that the agent uses to plan, implement, verify, and document your feature end to end. That's a meaningful philosophical difference, and in practice it changes what the tool is useful for.

]]> Wed, 13 May 2026 14:30:00 GMT https://dzone.com/articles/3655491 Jubin Abhishek Soni https://feeds.dzone.com/link/23566/17339385/java-rss-growth-docker-m1 The Problem

You're running a Java application in a Docker container on your M1 Mac. Everything works fine, but you notice something strange: The resident set size (RSS) keeps growing, even though your heap usage is stable. After hours of investigation, you find mysterious rwxp memory regions, each exactly 128 MB, accumulating in your process memory map.

What's causing this? Is it a memory leak? A JVM bug? Something else entirely?

]]> Tue, 12 May 2026 19:00:00 GMT https://dzone.com/articles/3638995 Sumeet Sharma https://feeds.dzone.com/link/23566/17338672/monitoring-spring-boot-applications-with-prometheus Monitoring Spring Boot Applications with Prometheus and Grafana

Spring Boot’s Actuator and Micrometer provide rich metrics that can be scraped by Prometheus and visualized in Grafana. This guide covers configuring a Spring Boot application to expose Prometheus-formatted metrics, writing custom metrics, and setting up Prometheus and Grafana for monitoring.

We cover installing Prometheus, writing a configuration to scrape your application, importing Grafana dashboards, and crafting PromQL queries and alerting rules. We also discuss Prometheus best practices, including metric naming conventions, label cardinality, and retention settings. Security considerations, troubleshooting tips, and the performance impact of metrics collection are also included.

]]> Mon, 11 May 2026 18:00:00 GMT https://dzone.com/articles/3639645 Ramya vani Rayala https://feeds.dzone.com/link/23566/17338595/serverless-illusion-when-you-pay-what-you-use The pitch is seductive in its simplicity. You write a function. You deploy it. You pay only for the milliseconds it runs. No servers idling through the night, no reserved capacity gathering dust, no 3 a.m. pager alerts because a VM decided to kernel panic during a deployment window. The cloud provider handles the undifferentiated heavy lifting — their phrase, not mine — and you, liberated from operational tedium, focus on building the thing that actually matters.

I believed this. Genuinely. For a long time.

]]> Mon, 11 May 2026 17:00:00 GMT https://dzone.com/articles/3645755 David Iyanu Jonathan https://feeds.dzone.com/link/23566/17336969/model-context-protocol-mcp-for-nl2sql-a-rigorous-e When you let an LLM turn natural language into SQL, you need to know: is it correct, will it run on your database, and is it efficient? SQLclMCP is an open-source framework that answers those questions by comparing LLM-generated SQL to human-written baselines on Oracle Database — using the Model Context Protocol (MCP) and a 500-question TPC-H benchmark. MCP keeps “how SQL is generated” behind a single HTTP API: the evaluator sends a question and gets back SQL, so you can swap models, prompts, or even the server implementation and still run the same evaluation. This article walks through the pipeline, how to run it, what gets measured, a few example graphs and tables, and Oracle gotchas we fixed in the prompt.

Why This Matters

Natural language to SQL (NL2SQL) works well for ad-hoc questions and app backends — until the model returns the wrong rows or a query that fails or runs too slowly in production. To ship with confidence you need three guarantees: the result set is correct (same logical result as the intended query), the SQL executes on your database without syntax or runtime errors, and it’s efficient enough (reasonable latency and plan quality, e.g. Oracle EXPLAIN PLAN). The only reliable way to get those guarantees is to compare LLM output to a gold standard on a real database, in a repeatable pipeline — so you can improve prompts, compare models, and catch dialect gotchas (Oracle vs MySQL, EXTRACT vs LIMIT, and the like). This framework gives you that pipeline.

]]> Fri, 08 May 2026 15:00:00 GMT https://dzone.com/articles/3642362 Sanjay Mishra https://feeds.dzone.com/link/23566/17336921/how-ai-is-rewriting-full-stack-java-systems-practi Building real-time applications means balancing user responsiveness with heavy backend processing. A proven solution is to decouple heavy workloads using events and asynchronous processing. In this approach, a Spring Boot application quickly publishes events to Kafka instead of processing requests inline. Then Kafka consumers (with AI/ML logic) handle the data in the background, and the results are pushed to clients in real time via WebSockets. This article highlights three key patterns enabling this architecture:

1. Event Production with Spring Boot and Kafka
2. AI-Driven Processing in Kafka Consumers
3. Real-Time WebSocket Delivery to the Frontend

Event Production with Spring Boot and Kafka

The first step is capturing an event and publishing it to Kafka. By offloading work to Kafka the application can respond immediately to the user without waiting for processing. Spring Boot’s integration with Apache Kafka provides a KafkaTemplate to send messages to topics.

]]> Fri, 08 May 2026 14:00:00 GMT https://dzone.com/articles/3640373 Ramya vani Rayala