DEV Community: michelle sebek

The Real Cost of Technical Debt: Why Your Spring Apps May be Holding You Back (and What to Do About it)

michelle sebek — Mon, 15 Sep 2025 18:11:14 +0000

We need to talk about technical debt. Not the scrubbed, executive-friendly version that gets discussed in board meetings, but the brutal reality that's probably making your workday miserable right now.
You know the feeling: you open up that legacy Spring application that "just needs a small feature added," and three hours later, you're still trying to figure out why changing one line breaks three seemingly unrelated tests. The codebase has layers of quick fixes, workarounds, and "temporary" solutions that became permanent years ago. Sound familiar?

The Technical Debt Reality Check
IDC just released research showing that technical debt has become one of the most critical business drivers for 2026. But here's what they won't tell you in the executive summary: this isn't just a business problem—it's a developer quality-of-life problem.

We're dealing with:

Legacy Spring applications running on ancient versions with security vulnerabilities
Patchwork architectures where every change feels like defusing a bomb
Data quality issues that make AI initiatives laughably impossible
Integration nightmareswhere adding a simple API endpoint becomes a week-long project
Deployment anxiety because nobody really knows what will break in production

The kicker? IDC's research shows that organizations stuck in technical debt become "cloud laggards" and eventually "AI laggards." While everyone else is building cool AI features, you're still fighting with legacy XML configurations.

Why Spring Apps Can Become Technical Debt Magnets

Spring Framework has been around for over 20 years, which means there are applications out there that were built when:

XML configuration was the only option
Annotations were new and scary
Spring Boot didn't exist
Microservices were just a theoretical pattern
Cloud deployment meant "buying more servers"

These applications can accumulate debt in predictable ways:
Configuration Drama
Multiple configuration approaches are mixed; XML, annotations, JavaConfig, and properties files all trying to coexist. Good luck figuring out where that bean is actually defined.
Dependency Nightmares
That critical library you depend on? Last updated in 2018. Has three known security vulnerabilities. Upgrading it breaks five other things because everything is tightly coupled.
Testing Gaps
Unit tests that require a full application context to run. Integration tests that hit real databases. End-to-end tests that fail randomly and nobody knows why. Test coverage that looks good on paper but doesn't actually test the important stuff.
Architecture Drift
What started as a clean layered architecture has evolved into a big ball of mud with circular dependencies, god classes, and business logic scattered everywhere.

The Hidden Costs You're Already Paying

Let's be honest about what technical debt is costing you personally:
Your Development Velocity: Remember when you could add features quickly? Now every change requires archaeological work to understand the existing code, careful planning to avoid breaking things, and extensive testing because you don't trust the existing tests.
Your Mental Health: That Sunday evening anxiety when you know you have to touch the legacy code on Monday. The constant fear that your "simple" change will cause a production incident.
Your Learning Time: Instead of exploring new technologies and patterns, you're spending time maintaining ancient codebases and working around their limitations.
Your Career Growth: While other developers are building modern, cloud-native applications with the latest frameworks, you're becoming an expert in legacy Spring configurations that are increasingly irrelevant.

A Practical Path Forward

Here's the thing: you don't have to rewrite everything from scratch. That's usually not realistic anyway. Instead, you can take a systematic approach to reducing technical debt while delivering business value.
Start with Assessment, Not Assumptions
Before you start refactoring, understand what you're dealing with. Tools like Spring Application Advisor can automatically analyze your codebase and identify:

Which dependencies have security vulnerabilities
Where your architecture violates common patterns
What parts of the code are most tightly coupled
Which areas would benefit most from refactoring

This gives you data to prioritize your efforts instead of just fixing whatever is annoying you most today.

Use the Strangler Fig Pattern
Instead of the "big rewrite" that never ships, gradually replace pieces of your legacy application:

Identify a bounded context that you can extract
Build the new implementation alongside the old one
Route traffic gradually from old to new
Remove the old code once you're confident in the replacement

This approach lets you deliver value continuously while reducing risk.

Modernize Dependencies Systematically
Don't just upgrade everything at once and hope for the best:

Audit your dependencies to understand what you're actually using
Prioritize security updates and actively maintained libraries
Test thoroughly with each upgrade (this is where good test coverage pays off)
Update your build process to make future updates less painful

Improve Your Development Process
Technical debt isn't just about code—it's about the processes that created the debt:

Code reviews that actually check for architectural concerns, not just syntax
Automated testing that gives you confidence to make changes
Documentation that explains the "why" behind architectural decisions
Refactoring as a regular part of feature development, not a separate initiative

Real Talk: Making the Business Case

Your manager probably doesn't care about technical debt until it starts affecting business outcomes. Here's how to frame the conversation:
Don't say:"We need to refactor this legacy code because it's messy."
Do say: "This technical debt is increasing our time-to-market by xx% and creating security risks that could impact compliance."

Don't say: "The architecture is bad and needs to be rewritten."
Do say: "We can reduce our bug rate by up to 60% and enable faster feature delivery by addressing these architectural issues systematically."

Don't say: "We need to upgrade our dependencies."
Do say:"These outdated dependencies have known security vulnerabilities and are preventing us from adopting modern development practices that would improve our productivity."

The Spring Modernization Playbook

If you're dealing with legacy Spring applications specifically, here's a practical modernization path:

Phase 1: Stabilize

Upgrade to the latest patch version of your current Spring version
Add comprehensive integration tests
Implement proper logging and monitoring
Fix critical security vulnerabilities
Check to make sure the version has OSS support, and when that support ends

Phase 2: Modernize Configuration

Convert XML configuration to annotation-based configuration
Consolidate properties files
Implement proper externalized configuration
Add configuration validation

Phase 3: Improve Architecture

Extract services to reduce coupling
Implement proper error handling
Add caching where appropriate
Improve database interaction patterns

Phase 4: Platform Modernization

Migrate to Spring Boot if you're not already there
Implement proper health checks and metrics
Add support for modern deployment patterns
Enable cloud-native features like graceful shutdown

The Bottom Line

Technical debt isn't going away by itself. Every day you delay addressing it, the problem gets worse and more expensive to fix. But you don't have to solve it all at once.
Start small. Pick one application, one service, or even one class that's causing you pain. Apply systematic modernization techniques. Measure the impact—both on the codebase and on your own productivity and job satisfaction.
The goal isn't perfect code (that doesn't exist anyway). The goal is code that you can work with confidently, modify safely, and extend easily. Code that doesn't make you dread Monday morning.
Your future self will thank you. Your team will thank you. And maybe, just maybe, you'll get to work on those cool AI projects instead of fighting with legacy XML configurations.

What's your biggest technical debt pain point right now? Drop a comment and let's commiserate—or better yet, let's figure out a practical solution together.

Spring Application Advisor 1.4: Windows Support, Java 21 Recommendations and Smarter Migration Paths

michelle sebek — Mon, 11 Aug 2025 14:52:09 +0000

new! video that demos Spring Application Advisor 1.4 in real time.

🚀

As developers, we've all been there—staring at a legacy Spring application that desperately needs modernization, but the upgrade path feels like navigating a minefield. Should you tackle Spring Boot 3.x first? What about that ancient Spring Security OAuth setup? And don't get me started on Java version compatibility across your entire codebase.

Spring Application Advisor 1.4 just dropped (August 8, 2025), and it's packed with features that solve the real problems we face during application modernization. Let's dive into what's new and why it matters for your daily development workflow.

🪟 Finally, First-Class Windows Support

# Now works seamlessly on Windows!
spring-app-advisor analyze --path ./my-spring-app

If you've been stuck in a Windows development environment while watching your macOS and Linux colleagues enjoy smooth Spring modernization tools, your wait is over. The advisor now runs natively on Windows with full feature parity.

Why this matters: No more spinning up Docker containers or WSL just to run modernization analysis. Your CI/CD pipelines can now include modernization checks regardless of your build environment. Plus, enterprise teams with Windows-heavy infrastructure can finally standardize on the same tooling.

☕ Smart Java Version Recommendations

The new advice command now includes intelligent Java upgrade suggestions:

spring-app-advisor advice --path ./my-app

# Output example:
✅ Recommendation: Upgrade to Java 21
   - Current: Java 17
   - Benefits: Virtual threads, pattern matching improvements, performance gains
   - Compatibility: ✅ All dependencies support Java 21
   - Estimated effort: Low

Developer experience win: Instead of manually checking compatibility matrices and changelogs, you get actionable recommendations with effort estimates. The tool analyzes your dependency graph and identifies potential blockers before you start the upgrade.

🔄 Migration Framework for Legacy Dependencies

This is where things get really interesting. Version 1.4 introduces a migration system that handles complex dependency transitions:

# Example: Automatic migration mapping
spring-security-oauth2 → spring-security-oauth2-resource-server
spring-security-jwt → spring-security-oauth2-jose

The advisor now understands that some libraries aren't just "upgraded"—they need to be migrated to entirely different artifacts. The Spring Security OAuth to Spring Security 6.x transition is fully automated now.

Real-world impact: Remember spending days figuring out how to migrate from the deprecated @EnableOAuth2Sso to the new security DSL? The advisor handles these complex transitions automatically, generating the necessary configuration changes and dependency updates.

🔧 Auto-Generated Build Configuration

Here's a quality-of-life improvement that's going to save you hours:

spring-app-advisor upgrade --generate-build-config
# Automatically creates missing build files
# Applies upgrades
# Cleans up temporary files

The problem it solves: You're working with a project that has incomplete or missing build configuration, but you still want to run upgrade analysis. Previously, you'd have to manually create Maven/Gradle files. Now the advisor generates them on-demand and cleans up afterward.

📦 Squash Multiple Upgrade Steps

New --squash flag lets you consolidate upgrade operations:

# Instead of multiple commits for each step
spring-app-advisor upgrade --squash --target spring-boot:3.2.0

# Results in a single, comprehensive changeset

Git workflow friendly: Perfect for teams that prefer atomic commits or need to align upgrades with sprint boundaries. You can still see individual steps in the planning phase but apply them as a single operation.

🔐 Enhanced Spring Security 6.x Recipes

The recipe system now includes comprehensive Spring Security modernization:

// Old (automatically detected and flagged)
@EnableWebSecurity
@EnableGlobalMethodSecurity(prePostEnabled = true)
public class SecurityConfig extends WebSecurityConfigurerAdapter {
    // deprecated patterns
}

// New (automatically suggested)
@Configuration
@EnableWebSecurity
@EnableMethodSecurity
public class SecurityConfig {
    @Bean
    public SecurityFilterChain filterChain(HttpSecurity http) {
        // modern configuration
    }
}

🎯 Better Error Handling and Visibility

Version 1.4 includes improved diagnostics:

spring-app-advisor plan --show-excluded-artifacts

# Output shows exactly what's blocking your upgrade
⚠️  Excluded artifacts preventing upgrade:
   - com.example:legacy-lib:1.0 (no compatible version found)
   - org.springframework:spring-web:4.3.21 (conflicts with target Spring Boot version)

Debugging made easier: When upgrades fail or get blocked, you now get clear visibility into what's causing the issue instead of cryptic error messages.

🏗️ Enterprise Recipe Repository

For teams using Tanzu Platform, there's now a dedicated Maven repository with advanced recipes:

<repository>
    <id>tanzu-recipes</id>
    <url>https://packages.broadcom.com/tanzu/recipes</url>
</repository>

These include platform-specific patterns like JAXRS modernization and advanced enterprise integration patterns.

Getting Started

If you're already using Spring Application Advisor:

# Update to latest version
curl -sSL https://get.spring-app-advisor.com | bash

# Verify version
spring-app-advisor --version
# Should show 1.4.0

New to the tool? Check out the getting started guide.

Real Talk: Why This Matters

Look, modernization tooling often feels like it's built by people who haven't actually done the grunt work of upgrading a 5-year-old Spring Boot app with 47 dependencies and questionable architectural decisions.

Spring Application Advisor 1.4 feels different. The Windows support isn't just a checkbox feature—it's recognition that developers work in diverse environments. The migration framework tackles the hard problems (like OAuth transitions) that usually require deep Spring Security expertise. The build config generation eliminates those annoying setup friction points.

These aren't flashy features, but they're the kind of improvements that make the difference between a tool you try once and abandon versus one that becomes part of your regular workflow.

What's Next?

The team is clearly listening to developer feedback and solving real problems. With enterprise adoption growing and more migration patterns being codified, expect to see more language-specific recipes and advanced transformation capabilities in future releases.

Have you tried Spring Application Advisor 1.4 yet? What modernization challenges are you hoping future versions will tackle? Drop a comment below—I'd love to hear about your experiences with Spring application modernization!

Want to see more content like this? Follow me for more Spring ecosystem updates and practical development tips. And if you're working on large-scale Spring modernization projects, definitely check out what Tanzu Platform can do for your team.

Navigating America's AI Future: What the AI Action Plan Means for Enterprise Software

michelle sebek — Mon, 28 Jul 2025 14:36:05 +0000

Written from an enterprise software perspective, this analysis examines the July 2025 AI Action Plan through the lens of business impact and technology adoption. The piece explores how policy decisions translate into practical considerations for organizations implementing AI strategies.

The release of America's AI Action Plan in July 2025 marks a pivotal moment for the technology industry. As someone involved in the enterprise software ecosystem through Spring Framework and Spring AI, I've been analyzing how this strategy will reshape our landscape and create new opportunities for innovation.

The Policy Landscape: A Foundation for Growth
The Action Plan's three pillars—accelerating AI innovation, building infrastructure, and leading international diplomacy—create a uniquely favorable environment for enterprise software companies. The emphasis on removing regulatory red tape while promoting open-source and open-weight AI models aligns perfectly with the collaborative development approach that has made Spring Framework a cornerstone of enterprise Java development for over two decades.
What strikes me most is the plan's recognition that "the bottleneck to harnessing AI's full potential is not necessarily the availability of models, tools, or applications. Rather, it is the limited and slow adoption of AI, particularly within large, established organizations." This validates what we've been seeing across our customer base: organizations have access to powerful AI capabilities but struggle with practical implementation at scale.

The Open Source Advantage
The Action Plan's strong support for open-source AI development represents a significant validation of our approach with Spring AI. The policy explicitly recognizes that "open-source and open-weight AI models are made freely available by developers for anyone in the world to download and modify" and have "unique value for innovation because startups can use them flexibly without being dependent on a closed model provider."
This philosophy mirrors the Spring ecosystem's approach to enterprise software development. Just as Spring Framework democratized enterprise Java development by providing powerful, flexible abstractions without vendor lock-in, Spring AI aims to democratize enterprise AI development by providing consistent programming models across different AI providers and deployment scenarios.
The government's commitment to ensuring "America has leading open models founded on American values" creates a stable foundation for open-source AI frameworks. This policy backing reduces the risk that organizations face when adopting open-source AI solutions, as they are aware that the federal government recognizes their strategic importance.

Infrastructure and Skills: Building for the Future
The Action Plan's focus on AI infrastructure development and workforce training creates immediate opportunities and challenges for enterprise software providers. The emphasis on "automated cloud-enabled labs" and "secure compute environments" aligns with the cloud-native architectures that modern application platforms enable.
The policy's recognition that AI will "transform how work gets done across all industries and occupations" underscores the critical importance of making AI adoption as seamless as possible for development teams. This is where battle-tested frameworks become invaluable—they provide the stability and abstraction layers that allow organizations to adopt new technologies without completely rebuilding their technical foundations.
The plan's call for "regulatory sandboxes" and "AI Centers of Excellence" suggests that organizations will need flexible, compliant platforms that can adapt to evolving requirements while maintaining security and governance standards. Enterprise platforms that can provide consistent deployment models across different environments—from on-premises to public cloud to edge computing—will be essential for organizations navigating this transition.

The Security and Compliance Imperative
One of the most significant aspects of the Action Plan is its emphasis on security and robust AI systems. The document repeatedly emphasizes the need for "secure-by-design, robust, and resilient AI systems" and underscores the importance of safeguarding AI innovations against security risks.
For enterprise software companies, this creates both opportunity and responsibility. Organizations will need platforms that can provide comprehensive security controls, audit trails, and compliance frameworks for their AI workloads. The ability to deploy AI applications consistently across different security zones—from development environments to production systems handling sensitive data—becomes a critical capability.
The plan's focus on "AI interpretability, control, and robustness" also validates the importance of providing developers with clear abstractions and debugging capabilities. When AI systems are poorly understood black boxes, organizations struggle to deploy them in mission-critical scenarios. Frameworks that provide consistent programming models and observability across different AI providers help address this challenge.

Global Competition and Innovation
The Action Plan's international focus, particularly around exporting American AI technology and countering foreign influence, creates interesting dynamics for enterprise software companies. The emphasis on ensuring that "domestic AI computing stack is built on American products" and infrastructure free from "foreign adversary information and communications technology" suggests that organizations will increasingly prioritize platforms with clear supply chain transparency.
This geopolitical dimension adds another layer to technology selection decisions. Organizations will need to balance innovation capabilities with security considerations and regulatory compliance. Platforms that can provide both cutting-edge AI capabilities and clear governance frameworks will have significant advantages in this environment.

Practical Implications for Enterprise Development
The Action Plan's emphasis on rapid AI adoption across government agencies provides a preview of what private sector organizations will need to accomplish. The policy calls for ensuring that "all employees whose work could benefit from access to frontier language models have access to, and appropriate training for, such tools."
This democratization of AI access requires platforms that can provide enterprise-grade capabilities with developer-friendly abstractions. Organizations need solutions that allow their existing development teams to incorporate AI capabilities without requiring deep expertise in machine learning or data science. The key is providing the right level of abstraction—powerful enough to enable sophisticated use cases, but simple enough for mainstream enterprise development teams to adopt.

Looking Forward: The Platform Play
Successful enterprise-scale AI adoption necessitates more than just access to models or compute resources. It requires comprehensive platforms capable of managing the entire lifecycle of AI-enabled applications, from development and testing to deployment and operations.
The Action Plan's vision of AI transforming government operations and private sector productivity requires platforms that offer consistent, secure, and scalable foundations for AI workloads. This becomes crucial as organizations transition from experimental AI projects to production systems that directly influence business operations and customer experiences..
The policy's emphasis on workforce development and skills training also highlights the importance of platforms that reduce the learning curve for AI adoption. When development teams can leverage familiar programming models and deployment patterns, they can focus on solving business problems rather than wrestling with infrastructure complexity.

Embracing the AI-Driven Future
America's AI Action Plan represents more than just policy guidance—it's a roadmap for technological transformation that will reshape how we build and deploy software systems. For enterprise software companies, this creates unprecedented opportunities to help organizations navigate this transition successfully.
The key insight from the Action Plan is that successful AI adoption requires more than just powerful models or abundant compute resources. It requires comprehensive platforms that can provide security, governance, scalability, and developer productivity. Organizations that can deliver these capabilities while maintaining the flexibility and openness that drive innovation will be well-positioned for this AI-driven future.

As we continue to develop Spring AI and support the broader ecosystem, we're committed to providing the abstractions and integration capabilities that make enterprise AI adoption both practical and powerful. The future outlined in America's AI Action Plan is one where AI capabilities are seamlessly integrated into the applications and systems that drive business value—and that future starts with the platforms and frameworks we build today.

The Spring AI project continues to evolve rapidly, with regular releases adding new provider integrations, enhanced security features, and improved developer experiences. To learn more about how Spring AI can support your organization's AI journey, visit (spring.io/projects/spring-ai).
Tanzu Platform is an AI-ready private PaaS that bridges the gap between ideas and production, unifying developer and operations workflows into a single, streamlined platform experience. To learn more, visit https://www.vmware.com/products/app-platform/tanzu.

SpringOne 2025 Vegas

michelle sebek — Mon, 16 Jun 2025 18:10:53 +0000

Are you heading to SpringOne 2025 — And Why You Should Too
Hey Spring fans, Java architects, and curious engineers —

Let me cut to the chase: SpringOne 2025 is shaping up to be an exciting developer event this year.

🗓️ Date: August 25-28, 2025
📍 Location: The Venetian, Las Vegas
🎟️ Vibe: Part tech deep-dive, part hallway-conference magic, all-in on Spring.

If you’ve ever built with Spring Boot, wrangled Kafka topics, fought with NullPointerExceptions, or wondered if your REST APIs could be better, this is your playground. And it's not just talks — it's tutorials, roundtables, quick talks, and one-on-one time with the actual engineers behind the code.

Let me give you a glimpse of a few sessions I’ve marked “can’t miss” on my schedule:

🔁 AMQP 1.0 with Spring (and RabbitMQ 4.0)
Speaker: Artem Bilan, Lead Software Engineer, Broadcom
RabbitMQ 4.0 brings AMQP 1.0, and Spring is ready. Artem’s session introduces a new Spring AMQP module designed to integrate directly with AMQP 1.0. If you deal with messaging protocols, queues, or want to prep for future-proof async architectures, this is your deep-dive.

🚫 Null Safety in Spring with JSpecify and NullAway
Speakers: Cora Iberkleid & DaShaun Carter, Developer Advocates, Broadcom
Nulls have haunted Java devs for decades. JSpecify and NullAway are about to change that. Learn how static analysis and smarter typing can help you squash NullPointerExceptions before they hit production. Bonus: a sneak peek at Spring Framework 7 and Spring Boot 4.

🚀 Beyond REST: Crafting a Modern GraphQL API Live
Speaker: Dan Vega, Spring Developer Advocate
Dan’s doing this one live. No slides — just code. You’ll learn how to build a GraphQL API in real time with Spring Boot, including schema-first design, query filtering, and batching. It’s everything REST can’t do, and it’s way more fun to build.

☕ Meet the Spring Engineering Teams (Roundtables)
Sometimes, all you want is answers. Or maybe you want to pitch an idea, share pain points, or just say thank you. At SpringOne, you can sit down (literally) with the engineers behind:

Spring Messaging (Kafka, AMQP, Cloud Stream) – with Artem Bilan & Soby Chacko

Spring Cloud – with Ryan Baxter & Spencer Gibb

Spring Security – with Rob Winch & Josh Cummings

Where else do you get direct face time with the maintainers and the roadmap shapers?

🔥 What Else?
Hands-on tutorials that go beyond "Hello, World"

Quick talks for bite-sized insights and practical patterns

After-hours networking and community magic

Real talk from devs in banking, aviation, retail, and more using Spring at scale

🎰 Why Vegas?
The Venetian is gorgeous, the energy is contagious, and you’ll leave recharged — and a better dev.

✅ So, Should You Come?
If you care about building secure, scalable, modern Java apps, SpringOne is the place to be this year.

I’ll be there, notebook in one hand, coffee in the other. Hope to see you in the hallway track 👋

👉 Get your ticket.

Boost Your Apps with Spring Application Advisor

michelle sebek — Mon, 02 Jun 2025 17:32:39 +0000

Modernizing Spring applications isn’t just about upgrading code—it’s about gaining insight, ensuring security, and optimizing performance. That’s where Spring Application Advisor (SAA) steps in.

What Is Spring Application Advisor?
Spring Application Advisor is a lightweight diagnostic tool from VMware Tanzu that helps teams assess the health, complexity, and upgrade readiness of their Spring-based Java apps. It’s designed to support modernization efforts by delivering actionable insights.

Why Use It?

✅ Assess Technical Debt: Understand what’s outdated or misaligned with Spring best practices.

🔐 Improve Security Posture: Identify vulnerable libraries and framework versions.

🚀 Modernize with Confidence: Get clear upgrade guidance and remediation tips.

How It Works:

Drop the advisor into your app using a Java agent—no code changes needed.
Run your app to collect runtime insights.
Review the results in a clear, web-based UI.

Great for Teams That Want To:

Accelerate Spring Boot upgrades
Reduce maintenance risk
Streamline app modernization

Final Thought:
Spring Application Advisor gives your dev team a fast, non-invasive way to shine a spotlight on the health of your Spring apps. It’s a must-have for teams embracing continuous modernization.

👉 Learn more and get started

🎉 Spring AI 1.0 Is Here — Making AI Production-Ready for the Enterprise

michelle sebek — Wed, 21 May 2025 16:08:25 +0000

"Bringing the power of modern AI into the hands of developers — the Spring way."

Spring AI 1.0 has officially reached General Availability (GA) as of May 20, 2025 — and it's a milestone worth celebrating for Java developers, enterprise teams, and anyone looking to integrate AI into production systems without reinventing the wheel.

📢 Official Announcement
🚀 Get Started Tutorial
🧠 Deep Dive by The New Stack

🌱 What is Spring AI?

Spring AI brings a consistent, idiomatic, and pluggable framework for integrating AI into Spring Boot applications. It abstracts the complexities of LLMs and vector stores — offering developers a clean, declarative API to interact with AI capabilities like:

Prompt templates
Embedding generation
Vector store access
AI function invocation
RAG (Retrieval-Augmented Generation) patterns
And integrations with major LLM providers: OpenAI, Azure OpenAI, Hugging Face, Ollama, and more.

In true Spring fashion, it emphasizes developer productivity, modularity, and testability — making it easier than ever to build intelligent features on solid engineering foundations.

🛠️ Getting Started in Minutes

Want to call an LLM with a simple prompt? It's as easy as this:

AiClient client = ...
Prompt prompt = new Prompt("What's the weather in Paris?");
AiResponse response = client.generate(prompt);
System.out.println(response.getResult().getOutput());

Need to integrate a retrieval layer using embeddings and vector stores? Spring AI provides first-class support for PostgreSQL pgvector, Redis, Milvus, Pinecone, and others — and it's composable, so you can swap providers without major refactors.

The official getting started guide walks you through building your first AI-powered app using Spring Boot and Spring AI in under 10 minutes.

🤖 Built for Production from Day One

As The New Stack notes, Spring AI 1.0 is “production-worthy”. That’s not just marketing hype. With Spring Boot integration, observability support (Micrometer, logging), and Tanzu-friendly deployment patterns, Spring AI is designed to run reliably at scale.

No duct tape, no Jupyter notebooks in prod — just clean, testable Java code and Spring idioms you already know and trust.

💡 Spring AI + Tanzu: A Perfect Pair

For teams building AI-powered applications on VMware Tanzu Platform, Spring AI offers a seamless path from dev to prod. With Spring AI, Tanzu Platform becomes not just cloud-native, but AI-native.

🚀 Ready to Build Smarter?

Whether you're building smart chatbots, personalized recommendations, intelligent search, or internal copilots, Spring AI helps you ship faster with fewer headaches.

Spring AI 1.0 is GA — and it's your turn to build something extraordinary.

👉 Start your first project here

🛡️ What Is a Sovereign Cloud—and How Tanzu Spring + Spring AI Enable Innovation Within Borders

michelle sebek — Wed, 14 May 2025 15:05:47 +0000

In an age where data is currency and compliance is king, the cloud has become a double-edged sword. The promise of global scalability and agility often clashes with the growing demand for data sovereignty. Enter sovereign cloud services—and alongside them, technologies like Tanzu Spring and the new Spring AI project that help developers innovate without compromising compliance.

In this post, we’ll unpack what sovereign clouds are, why they matter more than ever, and how tools like Tanzu Spring and Spring AI support modern development inside highly regulated, nationalized environments.

🌐 What Is a Sovereign Cloud?

A sovereign cloud ensures that data is stored, processed, and managed within a nation’s borders, under the jurisdiction of local laws, with no exposure to foreign regulations like the U.S. CLOUD Act.

Think of it as a geopolitically aware cloud service: one that protects data ownership, privacy, and control—especially critical in sectors like healthcare, banking, energy, and public services.

Key characteristics:

Data residency: Data never leaves the national territory.
Data sovereignty: Governed solely by local laws and standards.
Isolation and control: Often operated by national or regional providers with trusted infrastructure.
Compliance assurance: Designed to meet GDPR, Schrems II, CCPA, BAFIN, etc.

📈 According to IDC, by 2026, 75% of enterprises will prioritize data sovereignty requirements in their cloud strategy.

🧭 The Tension: Innovation vs. Regulation

Global developers want fast iteration, cloud-native patterns, and integrated AI—but data regulators want strong boundaries and zero foreign exposure.

The question becomes: How can you enable fast, secure, modern development inside sovereign environments?

That’s where Tanzu Spring and Spring AI come in.

🌱 Tanzu Spring: Modern Development with Guardrails

Tanzu Spring is VMware’s enterprise-grade Spring offering, built to support cloud-native Java development—even in highly controlled, sovereign cloud environments.

What does Tanzu Spring offer?

🔧 Production-ready Spring Boot apps: Pre-configured, secure templates and accelerators.
🚀 Developer experience tooling: IDE plugins, CI/CD integration, and golden paths to production.
🛡️ Secure software supply chain: Signed containers, CVE scanning, and policy enforcement.
📦 Platform-agnostic: Runs across sovereign cloud providers like OVHcloud, T-Systems, Orange, and others offering VMware Cloud Verified services.

Whether you're deploying to a public sector sovereign cloud or an isolated Kubernetes cluster within a financial institution, Tanzu Spring makes it possible to:

Modernize legacy workloads.
Accelerate developer productivity.
Stay fully compliant with jurisdictional policies.

🤖 Spring AI: Ethical AI in Controlled Environments

While sovereign cloud ensures data jurisdiction, AI governance adds another layer of complexity. How can organizations safely integrate generative AI in sovereign environments?

This is where Spring AI, a new initiative from the Spring ecosystem, shines.

What is Spring AI?

Spring AI is a developer-friendly abstraction layer for integrating with LLMs and vector databases, much like how Spring simplifies working with databases or web services.

Features include:

🧠 LLM support: OpenAI, Azure OpenAI, Hugging Face, Mistral (with more on the way)
📚 Retrieval-Augmented Generation (RAG): Connect LLMs to sovereign or internal datasets
🔍 Embedding and search tools: Use vector stores like PostgreSQL pgvector, Redis, or Elasticsearch
🔒 Deployment flexibility: Ideal for sovereign environments that require private, air-gapped AI usage

Spring AI helps teams bring contextual, domain-specific AI experiences to applications—without leaking data to third-party models.

🧩 Example: A public health agency could use Spring AI to power an internal chatbot that helps clinicians search anonymized medical data—fully inside the national cloud boundary.

🧪 Use Case: A Financial Regulator’s App Modernization Journey

A European financial regulator needed to modernize a suite of legacy Java applications and expose APIs to banks and fintechs. Their requirements:

Data must stay within EU borders
Applications must adhere to BAFIN and GDPR
Internal developers needed modern tooling

Using Tanzu Spring, the agency:

Migrated legacy Java EE apps to Spring Boot microservices
Deployed on a sovereign VMware-based cloud
Implemented developer accelerators for faster onboarding
Integrated an internal document assistant powered by Spring AI and a private Hugging Face model

The result: faster time-to-market, full compliance, and a dramatically better developer experience.

🔄 The Future Is Sovereign—and Cloud-Native

Sovereign cloud isn't a trend—it's a tectonic shift in how we think about cloud adoption, AI ethics, and digital transformation.

Thanks to tools like Tanzu Spring and Spring AI, organizations no longer have to choose between innovation and control. They can ship faster, stay compliant, and build confidently—all while respecting the sovereignty of the data they steward.

Let’s Talk 🚀

Are you navigating the sovereign cloud landscape? Building with Spring Boot or exploring AI in regulated spaces? I’d love to connect and hear your journey.

Follow for more on cloud-native dev, data sovereignty, and Spring in the enterprise.

Would you like me to turn this into a slide deck, executive summary, or social post series as well?

Spring Application Advisor: Built for Spring, Designed for Java

michelle sebek — Fri, 02 May 2025 18:54:12 +0000

Modern Java developers are under pressure to build faster, cleaner, and more maintainable applications. Spring has long been the go-to framework for building enterprise-level Java apps—but what if there was a tool that could advise you as you build?

Meet the Spring Application Advisor—a tool built to optimize your Spring applications with pre-configured recipes and deep Spring ecosystem integration. But here's where it gets exciting: while it's tailored for Spring today, the core functionality is framework-agnostic, laying the groundwork for broader Java support in the future.

🌱 What Is Spring Application Advisor?
At its heart, Spring Application Advisor is your proactive development companion:

🔍 Scans your Spring codebase

🛠️ Suggests best practice improvements

🚦 Identifies potential issues before they hit production

🧠 Helps you adopt modern Spring idioms automatically

Think of it like a linter + code coach + architectural guide—specifically trained on the Spring ecosystem.

⚙️ Why Spring?
Spring’s popularity and deep abstraction layers often lead to:

Hidden performance bottlenecks

Misuse of powerful features (like caching, AOP, and transactions)

Slow adoption of newer features (like records, native images, observability)

Spring Application Advisor is optimized with pre-built recipes that understand these patterns. Instead of starting from scratch, you benefit from curated guidance based on common real-world use cases.

🧩 What About Non-Spring Java Apps?
Here's the kicker: under the hood, the advisor’s engine is framework-agnostic. Its architecture is designed to support:

Any Java-based application

Future framework integrations (Quarkus, Micronaut, etc.)

Custom advisor plugins

While Spring gets the first-class treatment today, this tool is built with expansion in mind.

💡 Example Use Cases
💬 "You're still using RestTemplate—consider upgrading to WebClient."

⚠️ "Your beans aren’t being closed properly—this might cause memory leaks."

🌐 "Enable @Observability here for better insight in production."

🧭 The Road Ahead
Spring Application Advisor is just getting started. We're investing in:

More recipes
Community-contributed patterns
Expanded framework support

Whether you're building with Spring Boot 3.x or managing a legacy monolith, this tool helps you code with confidence and grow with the Java ecosystem.

👨‍💻 Get Involved
Stay tuned for open beta access and GitHub contributions. Have a use case or pattern in mind? Let us know—we’re actively building this with the community in mind.

🔗 Built for Spring. Designed for Java. Ready for the future.
https://enterprise.spring.io/spring-application-advisor

The Security Crisis: How Tanzu Spring Is Changing the Game

michelle sebek — Tue, 22 Apr 2025 18:14:27 +0000

In today's world, application security isn't just an IT concern—it's a business survival imperative. As organizations race to deliver innovative software solutions, security vulnerabilities continue to expose them to unprecedented financial and reputational risks.

The Security Breach Epidemic

The numbers are staggering:

92% of organizations experienced security breaches with applications developed in-house in the past 12 months (Security Magazine, 2024 | Checkmarx Study 2024).

Even more concerning, 76% of vulnerabilities in enterprise Java applications remain unpatched for 90+ days (Veracode State of Software Security, 2024).

With Java remaining the backbone of enterprise applications—72% of Java applications use Spring Framework—organizations face both significant risk and tremendous opportunity for enterprise-wide security transformation.

The Rising Cost of Compromise
The financial impact of security failures continues to climb at an alarming rate:

2023: $4.45 million average cost per data breach

2024: $4.88 million average cost per data breach (10% increase)

2025: $5.17 million current average cost per data breach (IBM Cost of Data Breach Report)

Application vulnerabilities remain the primary attack vector for these costly breaches. This surge is driven by several factors, including escalating class action lawsuits, expanding cyber insurance exclusions, increased cloud and AI usage, and a persistent cybersecurity skills shortage.

The Role-Specific Challenge

For CTOs

Balancing the demand for new features against security requirements
Managing technical debt in legacy Java systems that increases security risk
Teams spending excessive time on patching rather than innovation

For CISOs

Dealing with expanding attack surfaces as applications become more distributed
Managing compliance across multiple regulatory frameworks
Limited visibility into the actual security posture of Java applications

For IT Leadership

Struggling to balance innovation with risk management
Facing a genuine security talent shortage
Managing security across increasingly complex modern infrastructure

Enter Tanzu Spring: Security By Design

VMware Tanzu Spring offers comprehensive solutions to address these challenges head-on:
Spring Application Advisor
This powerful tool ensures continuous updates and governance by automatically detecting and addressing vulnerabilities in Spring applications. It maintains security and compliance across all Git repositories, enabling teams to catch issues early in the development process—before they reach production environments.
Enterprise Spring Boot Governance Extension
For organizations in regulated sectors, this extension streamlines compliance by providing audit-ready information and validating application dependencies against critical standards like FIPS-140-3 and NIST 800-53.
Secure Development and DevSecOps Tooling
The broader Tanzu Platform embeds security throughout the application development lifecycle, integrating policy enforcement, automated vulnerability scanning, and secure framework governance. With built-in support for Spring-based applications, it enables consistent security and compliance across environments while providing the visibility and auditability needed to meet regulatory requirements.

The Business Impact Proof Points

Organizations implementing Tanzu Spring solutions have experienced remarkable results:

For CTOs:

43% faster release cycles while reducing vulnerabilities by 90% (IDC 2024)
Dramatically reduced technical debt in legacy systems
Significantly less resource drain from constant security patching

For CISOs:

Unified security dashboards providing visibility across the entire Java estate
Continuous compliance monitoring with 94% fewer audit findings (Forrester, 2023)
Comprehensive visibility into application security posture

For IT Leadership:

Built-in security expertise and automation reduce security staffing needs by 67%
Existing teams are enabled to secure 3x more applications (GigaOm, 2023)
A more effective balance between innovation and risk management

Transforming Security from Burden to Enabler

As we navigate this software security crisis, organizations need solutions that transform security from a burden into a business enabler. By leveraging Tanzu Spring within the broader Tanzu Platform, organizations can accelerate innovation while simultaneously reducing breach risk, lowering costs, and simplifying compliance.
In a time where application vulnerabilities cost organizations millions and jeopardize business continuity, Tanzu Spring offers a compelling path forward—one where security and development work in harmony rather than opposition.
The question isn't whether you can afford to integrate security into your development practices, but whether you can afford not to. With data breach costs projected to continue rising, the time to act is now.

Are you facing similar security challenges with your Java applications? Share your experiences in the comments below, or reach out to learn more about how Tanzu Spring can help transform your organization's approach to application security.

Spring's new Support Policy Updates

michelle sebek — Sun, 16 Feb 2025 23:53:21 +0000

Understanding the Latest Spring Support Policy Updates

As developers, one of the key aspects of maintaining long-term, stable applications is understanding the support policies of the frameworks and tools we use. For Spring developers, this is especially crucial, given the framework’s widespread adoption in enterprise environments. To help ensure smooth project maintenance, the Spring team has recently announced updates to its Support Policy, providing a clearer roadmap for developers regarding the lifecycle and support of Spring releases.

In this article, we’ll break down what these updates mean, why they’re important, and how they’ll impact your Spring projects.

What’s New in the Spring Support Policy?

The Spring Support Policy Updates aim to provide more transparency and clarity around which versions of Spring are actively supported, when support ends, and what you can expect in terms of security fixes, bug fixes, and feature updates. Here are some of the key changes:

1. Updated Long-Term Support (LTS) Guidelines
The new support policy places a greater emphasis on Long-Term Support (LTS) releases, ensuring that these versions receive extended maintenance and security updates for a longer period. LTS releases are particularly important for enterprise applications that require a stable foundation for many years, often with minimal changes.

With this update, the Spring team has clarified the expected LTS lifespan for each release and detailed how security patches and critical bug fixes will be handled throughout the support period.

2. Streamlined Version Lifecycle
To avoid confusion around support timelines, the Spring team has made the version lifecycle more predictable. From now on, major versions of Spring will have clearly defined end-of-life dates, and developers will know exactly when a version will transition from active support to end-of-life (EOL).

This change ensures that teams can plan upgrades and migrations well in advance, preventing any unexpected disruptions due to versions falling out of support.

3. Improved Security & Maintenance Updates
As security threats evolve and new vulnerabilities are discovered, keeping frameworks up-to-date is crucial. The new support policy outlines the process for delivering timely security updates and maintenance fixes. It also clarifies the level of support available for both major and minor releases.

For example, minor releases will continue to receive security patches for an extended period, while major versions will only receive support during their active maintenance window. This change allows Spring developers to plan more effectively for long-term security without scrambling for fixes on deprecated versions.

4. Clearer Communication of Version Support
One of the standout updates is the improved communication around which Spring versions are currently supported, which are transitioning out of support, and which have reached end-of-life (EOL). With regular updates to the support policy, developers will be able to stay on top of important lifecycle milestones and avoid common pitfalls that come from using unsupported versions.

Why Are These Updates Important?

For enterprise developers and teams managing mission-critical applications, understanding the support policy is crucial to ensuring application stability and security over time. Here are a few reasons why these updates matter:

1. Better Planning for Upgrades
With clearer support timelines, development teams can now plan their upgrade paths with greater confidence. No longer will you have to guess when a Spring version will no longer be supported. You’ll have a clear roadmap of when to start considering an upgrade to newer versions and which releases are safe to stay on for the long term.

2. Security Assurance
Security is always top of mind for developers, especially when working on applications that handle sensitive data. The updated policy ensures that developers have the necessary resources to stay on top of security vulnerabilities and patching requirements. It provides a predictable schedule for when security updates will be delivered, which helps prevent potential security breaches due to unsupported software.

3. Easier Project Maintenance
For teams with complex, long-lived applications, maintaining a consistent environment over time can be challenging. With the new policy in place, you’ll have better clarity about which versions are actively supported, allowing you to make informed decisions about managing project dependencies and planning for long-term maintenance.

What Does This Mean for You?

If you’re already using Spring in your project, these support policy updates will help you make more informed decisions about which versions to use and when to plan your upgrades. If you’re still on an older version, now is a great time to review your support options and ensure your Spring-based project is running on a supported version.

Here are a few actions you can take right now:

Review your current Spring version and check its support status using the updated policy guidelines.
Plan your upgrade strategy based on the new support timelines.
Consider moving to an LTS version if you’re on a non-LTS release and need long-term support.
Stay informed about security and maintenance updates through official Spring channels.

The updated Spring Support Policy is designed to make the Spring framework even more reliable and predictable for developers building enterprise-grade applications. With clearer timelines, enhanced security updates, and more transparent communication, developers can focus more on building innovative features and less on worrying about version lifecycles.

For more details on the policy changes and how they’ll affect your projects, you can read the full official announcement on the Spring blog.

By staying informed and aligning your projects with the latest support policies, you’ll enable that your applications are always running on a secure and stable foundation.

Building Powerful AI Systems that Solve Real-World Problems with The Power of Model Context Protocol (MCP)

michelle sebek — Sun, 16 Feb 2025 23:31:32 +0000

Artificial Intelligence (AI) continues to evolve, one area that's seeing tremendous growth is the integration of Large Language Models (LLMs) with a variety of data sources, tools, and services. However, achieving smooth and consistent integration across various platforms and environments has always been a challenge for developers. This is where the Model Context Protocol (MCP) comes in.

Launched and steadily advancing through the spring-ai-mcp experimental project, MCP has become a game changer for developers who are looking to build intelligent systems, agents, and workflows powered by LLMs. By providing a unified way to connect AI models to multiple data sources and tools, MCP simplifies what could otherwise be a complicated and fragmented integration process. Let's dive into why this is so important and how the Model Context Protocol is shaping the future of AI development.

What Is the Model Context Protocol (MCP)?

The Model Context Protocol (MCP) is a powerful and flexible protocol that serves as the foundation for connecting Large Language Models (LLMs) to different external systems, APIs, and tools. MCP provides a standardized framework that ensures smooth communication between LLMs and external resources, enabling developers to build intelligent agents and complex workflows without worrying about the underlying complexities of data integration.

MCP offers a set of protocols and interfaces that abstract away the difficulties involved in linking your AI models to various services. Instead of worrying about how to interface with different data sources and tools, developers can rely on MCP to ensure that everything works seamlessly.

Key Benefits of MCP for LLMs

1. Pre-built integrations for Easy Connectivity
MCP simplifies the integration process by offering a growing list of pre-built integrations. These integrations allow your LLM to easily connect to external tools, data sources, and services. Whether it’s a database, API, or a specialized service, MCP ensures that the LLM can access and interact with it without friction.

2. Flexibility to Switch Between Providers
The ability to switch between different LLM providers and vendors is critical. MCP empowers developers to change providers with minimal hassle, ensuring that your application remains flexible and adaptable to future advancements in AI technology.

3. Standardized Interfaces for Tool Discovery and Execution
MCP provides standardized interfaces for tool discovery and execution, streamlining the process of finding and interacting with external systems. This allows developers to quickly build and modify workflows incorporating various tools and services, without worrying about proprietary integration methods.

4. Seamless Model-to-Data Communication
Since LLMs often need to work with external data sources to enhance their capabilities, MCP acts as the bridge that connects them seamlessly. Whether you're pulling in data for training or providing real-time input during model execution, MCP ensures smooth communication between the model and external systems.

The Evolution of MCP in Spring AI

The spring-ai-mcp project began as an experimental initiative last November and has evolved into a core part of the MCP Java SDK. This SDK is the result of collaboration between the Spring AI team and David Soria Parra and colleagues at Anthropic,aiming to make #MCP an official standard within the Java ecosystem.

The MCP Java SDK comes with a variety of features that make it incredibly powerful and adaptable for developers:

Core Capabilities of the MCP Java SDK:

Synchronous and Asynchronous MCP Client/Server Implementations: This gives developers flexibility in how they handle communication between their AI models and external systems, ensuring that both time-sensitive and long-running tasks are properly managed.
Protocol Version Compatibility Negotiation: Ensures backward and forward compatibility, so your application can evolve over time without breaking existing integrations.
Tool Discovery and Execution with Change Notifications: Keeps you informed about changes in your toolset, ensuring that your workflows stay up to date.
Resource Management with URI Templates: Simplifies resource management by allowing dynamic handling of resources and their associated URIs.
Roots List Management and Notifications: Manages resources and provides updates to keep developers informed of changes to the environment.
Prompt Handling and Management: Allows for sophisticated handling of model inputs and outputs, ensuring that your interactions with the model are as efficient as possible.
Sampling Support: Facilitates AI model interactions with support for various sampling strategies, enabling more control over model behavior and output.

Multiple Transport Options for Flexibility

The MCP Java SDK supports several transport mechanisms, allowing developers to choose the method that best fits their application architecture:

Studio-Based Transport: Ideal for process-based communication, this transport method is simple and efficient.
Java HttpClient-Based SSE Client Transport: Great for handling server-sent events (SSE) in web applications.
Servlet-Based SSE Server Transport: Supports servlet-based applications with SSE for real-time communication.
Spring-Specific Transports: For Spring developers, there are two options:
- WebFlux SSE Transport:
Designed for reactive HTTP streaming in applications built with Spring
- WebFlux.WebMVC SSE Transport:
Best suited for traditional servlet-based applications using Spring MVC.

Why MCP Matters for the Future of AI

MCP is more than just a tool for LLM integration—it’s a framework that enables developers to build smarter, more powerful AI-driven applications with ease. The protocol provides scalability, flexibility, and extensibility, all while promoting consistency and standardization in the way AI models interact with external resources.

As AI technology continues to grow and become more integrated into our daily lives, tools like MCP will be essential for ensuring that these powerful models can work effectively with real-world data and tools. Whether you're working on AI-powered chatbots, recommendation systems, or autonomous agents, MCP is the glue that can tie it all together, enabling seamless, consistent, and efficient AI solutions. Check out the video by Josh Long

Conclusion

With Spring AI and the Model Context Protocol (MCP), developers have the tools they need to create intelligent, interconnected applications powered by Large Language Models. By simplifying integration and offering flexibility, MCP allows developers to focus on what really matters—building powerful AI systems that solve real-world problems.

The future of AI development is here, and it's more connected than ever.

Feel free to leave a comment or reach out if you have any questions about how you leverage MCP in your projects! Let’s connect and discuss the future of AI integration. 🚀

Check out our newest enhancement to Spring Application Advisor that is part of Tanzu Platform and Tanzu Spring entitlement.

michelle sebek — Tue, 11 Feb 2025 19:50:22 +0000

A New Era for Spring Application Management: The Impact of Spring Application Advisors’ Latest Features

michelle sebek ・ Feb 10

#developers #spring #microservices #springboot