Posts Tagged ‘DevoxxGR2026’
[DevoxxGR2026] An Online Real-World Unified Data Architecture: Bridging Digital Analytics and Transactions for Comprehensive Customer Understanding
Lecturers
Sotiris Lyras and Andreas Athanasopoulos are experienced data professionals working within the online analytics and data science teams at OPAP, a leading betting and gaming operator in Greece. Their work centers on creating holistic views of customer behavior by intelligently integrating digital engagement metrics with transactional and financial records in complex, high-volume, real-time environments.
Abstract
In this practical, production-focused case study presented at Devoxx Greece 2026, Sotiris Lyras and Andreas Athanasopoulos detail the implementation of a unified data architecture that successfully merges digital analytics signals with transactional business records. Beginning with comprehensive instrumentation of web and mobile applications through Google Tag Manager and flowing into BigQuery for transformation and standardization, behavioral data is then joined with financial and account activity within Azure Databricks. This creates a true 360-degree customer profile that powers enhanced segmentation, propensity modeling, churn prediction, customer lifetime value calculations, and personalized experiences—even for unregistered users exhibiting strong digital intent but lacking transactional history.
The Persistent Challenge of Fragmented Customer Data
Modern online platforms typically maintain parallel but disconnected data ecosystems. Digital analytics platforms capture rich behavioral signals including session patterns, device characteristics, navigation flows, and engagement metrics. Meanwhile, core transactional systems record financial movements, betting activity, deposits, withdrawals, and account status changes. Without systematic integration, organizations lack a complete, unified understanding of their customers’ journeys.
This fragmentation manifests in several critical ways. Marketing teams struggle to connect campaign exposure with actual revenue outcomes. Product development operates with incomplete behavioral context when prioritizing features or measuring success. Machine learning models trained on partial datasets deliver suboptimal performance and limited predictive power. Most significantly, users who demonstrate clear digital interest and engagement but have not yet converted to registered, paying customers remain largely invisible to business systems optimized for transactional signals.
Architecting and Implementing the Unified Data Foundation
The solution begins with meticulous, structured instrumentation across all customer touchpoints. Google Tag Manager provides a robust framework for consistent event tracking, ensuring that every meaningful user interaction—whether on web or mobile applications—is captured as an atomic event enriched with relevant contextual metadata.
These raw digital events are ingested into Google BigQuery, where comprehensive transformation, cleansing, standardization, and enrichment processes occur. The team invested significant effort in identity resolution strategies that accurately link user activity across devices and sessions while respecting privacy constraints and avoiding duplication.
Azure Databricks serves as the central integration and unification layer. Here, transformed behavioral data streams are intelligently joined with transactional and financial records at the individual customer level. The architecture maintains rigorous data governance, auditability, privacy compliance, and quality controls throughout the entire pipeline.
Realizing Business Value Through Holistic Customer Intelligence
The unified dataset fundamentally transforms organizational capabilities:
- Complete Journey Analysis: Teams can now analyze how factors such as device type, acquisition channel, personalization segments, and behavioral patterns influence conversion probability, engagement depth, and long-term value.
- Accurate Acquisition Attribution: Digital marketing campaigns can be evaluated based on their actual contribution to first deposits, betting activity, and customer lifetime value rather than surface-level engagement metrics.
- Proactive Unregistered User Engagement: Behavioral scoring models identify high-intent anonymous visitors, enabling targeted nurturing and conversion optimization before formal registration occurs.
- Significantly Enhanced Machine Learning: Predictive models for deposit propensity, churn risk, reactivation likelihood, and customer lifetime value achieve substantially higher performance when trained on rich, combined feature sets.
Measurable outcomes include more precise customer segmentation, dramatically improved personalization relevance, earlier and more accurate identification of at-risk customers, and overall more effective resource allocation across marketing, product, and customer experience initiatives.
Broader Implications for Product Development, Marketing, and Operations
Access to unified customer data accelerates product development cycles by providing complete information for hypothesis validation and success measurement. Customer support and retention teams gain richer contextual understanding, enabling more personalized and effective interventions. Marketing organizations can optimize budget allocation by clearly connecting digital behaviors with downstream business results.
Beyond specific functional improvements, the unified architecture represents a profound shift in organizational perspective. Rather than viewing users through disconnected lenses of “digital visitors” versus “paying customers,” the business now understands each individual as progressing through a single, continuous journey with multiple observable dimensions.
Technical and Organizational Lessons Learned
Successful implementation demanded careful attention to several critical areas: achieving reliable identity resolution across fragmented touchpoints, aligning disparate data schemas and freshness requirements, maintaining strict governance and compliance standards, and fostering close collaboration between analytics, data engineering, product, and business stakeholders.
The speakers emphasize that effective data unification initiatives should begin with clearly articulated business questions and use cases rather than purely technical elegance. They also stress the importance of treating the unified dataset as a strategic organizational asset requiring ongoing investment in quality, governance, and accessibility.
Conclusion: Unified Data as Foundation for Modern Customer-Centric Organizations
A thoughtfully designed unified data architecture converts previously fragmented signals into powerful, actionable intelligence. For organizations operating in data-intensive industries, successfully bridging digital behavioral data with transactional records provides the essential foundation for genuinely customer-centric decision-making across product development, marketing, operations, and strategic planning.
The OPAP implementation demonstrates that even in complex, regulated, high-volume environments, technical data unification can deliver substantial, measurable business impact when approached with clear objectives, robust engineering practices, and strong cross-functional collaboration.
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[DevoxxGR2026] What You Need to Know (And Why You Should Care) About AI Governance
Lecturer
M. Frost is a recognized AI ethicist, governance specialist, and technologist with nearly a decade of hands-on experience bridging artificial intelligence development with policy, risk management, and responsible innovation practices. She has advised numerous organizations on implementing practical AI governance frameworks, contributed to bioethics initiatives, and helped develop trustworthy AI standards. Frost excels at translating complex regulatory and ethical concepts into actionable guidance for technical practitioners.
Abstract
In this essential session at Devoxx Greece 2026, M. Frost makes a compelling case that AI governance has evolved from a specialized legal and policy concern into a fundamental responsibility shared by developers, designers, architects, and product leaders. With regulations such as the EU AI Act moving into active enforcement phases and a dynamic compliance landscape in the United States, technical decisions now carry direct implications for legal compliance, ethical integrity, and business risk. Frost equips attendees with practical frameworks, decision-making tools, and real-world strategies to integrate governance considerations throughout the development lifecycle while preserving innovation and creativity.
Understanding Why Governance Matters for Technical Teams
AI governance is no longer confined to boardroom discussions or legal reviews. It directly influences architectural choices, data handling practices, model selection, and feature design. The EU AI Act establishes a risk-based regulatory framework with specific requirements for prohibited uses, transparency obligations, human oversight mechanisms, and documentation standards for high-risk systems. In the US, a patchwork of state-level initiatives creates additional complexity, while industry standards and corporate policies attempt to establish consistent practices.
Frost argues that treating governance as an afterthought inevitably leads to higher remediation costs, potential legal exposure, and damaged user trust. Developers who incorporate governance principles early can make more informed technical decisions, reduce downstream risks, and build systems that are both innovative and sustainable.
The Interconnected Pillars of Responsible AI Development
Effective AI governance rests on several foundational pillars that technical teams must consider holistically:
- Fairness and Bias Mitigation: Addressing different forms of algorithmic bias, developing appropriate measurement techniques, understanding intersectionality across demographic factors, and implementing continuous monitoring throughout the model lifecycle.
- Transparency and Explainability: Tackling the challenges of black-box systems, implementing mechanisms that support the “right to explanation,” and designing human-AI interactions that foster appropriate trust and understanding.
- Security and Safety: Protecting against adversarial attacks, ensuring robust data protection measures, and maintaining system integrity when deployed in real-world, unpredictable environments.
- Privacy Protection: Establishing meaningful informed consent processes, applying differential privacy techniques where appropriate, and minimizing unnecessary surveillance or data collection risks.
- Accountability Structures: Clarifying liability assignment, implementing effective auditing and review processes, and establishing clear organizational ownership for AI system behavior and outcomes.
- Broader Societal Considerations: Evaluating potential impacts on employment patterns, accessibility for diverse user groups, mental health implications of AI interactions, and preservation of human autonomy and agency.
These pillars frequently create tensions and trade-offs. Privacy protections may conflict with security requirements. Fairness improvements can sometimes reduce model performance. Governance work involves making these trade-offs explicit and deliberate rather than accidental.
Practical Frameworks for Integrating Governance into Development
Frost introduces several actionable tools designed specifically for technical practitioners. A straightforward four-question decision framework helps evaluate new features, models, or system changes:
- What do we need to do? — Clearly articulate the intended product goals, use cases, and desired outcomes.
- What should we do? — Identify and prioritize relevant ethical principles and organizational values.
- What must we do? — Map applicable legal, regulatory, and industry-specific requirements.
- What can we do? — Assess technical feasibility, resource constraints, and organizational capabilities.
This iterative process, drawing inspiration from established standards such as NIST’s AI Risk Management Framework and corporate responsible AI programs, encourages teams to address governance questions proactively during design and development phases rather than as compliance checkboxes after implementation.
Additional practices include maintaining comprehensive decision documentation, identifying appropriate points for human oversight or intervention, and ensuring audit trails that support both internal review and potential regulatory examination.
Addressing the Challenges of Agentic and Multi-Agent Systems
The emergence of multi-agent and increasingly autonomous systems introduces additional governance complexities. Key considerations include managing agent autonomy levels, controlling tool access and permissions, handling memory and context persistence, and monitoring for goal drift or unintended optimization behaviors.
Frost advocates designing such systems with clear modular boundaries, implementing comprehensive logging and traceability mechanisms, and maintaining appropriate human oversight capabilities, particularly for high-stakes decisions or actions with potential for significant impact.
She cautions against “agent washing”—the tendency to overstate the autonomy or capabilities of systems that still operate within relatively narrow, human-defined parameters—and encourages rigorous, evidence-based assessment of actual system behaviors.
Building AI Systems That Earn Trust Through Responsible Practices
Governance should not be viewed as a constraint on innovation but as a discipline that enables the creation of systems worthy of user and societal trust. Frost encourages technical teams to engage with governance questions from the earliest stages of projects, participate actively in shaping both internal practices and external standards, and recognize their role as active contributors to AI’s broader societal impact.
The choices made during development—around data selection, model training approaches, feature design, and deployment strategies—collectively determine whether AI systems ultimately serve to benefit or inadvertently harm individuals and communities.
Conclusion and Resources for Continued Learning
The session concludes by reinforcing that responsible AI development is a shared responsibility requiring collaboration across technical, product, legal, and leadership functions. Frost provides curated resources and recommended reading for teams seeking to deepen their governance capabilities, emphasizing practical starting points rather than overwhelming comprehensive overviews.
Attendees leave equipped with mental models, decision frameworks, and concrete strategies for incorporating governance considerations into their daily work, enabling them to build AI systems that are not only technically excellent but also ethically sound and regulatorily compliant.
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[DevoxxGR2026] The Pragmatic Path: Structured Adoption of Agentic AI in Software Development
Lecturers
Dimitris Papageorgiou and Konstantina Mavrodimitraki are Senior Solutions Architects at Amazon Web Services in Greece. With extensive experience as software and data engineers, they have supported numerous enterprise customers in adopting cloud-native and AI technologies. Their work focuses on practical implementation strategies that deliver measurable business value while addressing real-world concerns around quality, security, and team readiness.
Abstract
Dimitris Papageorgiou and Konstantina Mavrodimitraki present a pragmatic framework for integrating agentic AI into software development lifecycles. Based on hands-on implementations across multiple customer environments, the session addresses common barriers such as code quality fears, lack of structure, and resistance to change. Through concrete examples—including optimized code reviews returning over 16,000 developer hours annually and 65-80% faster issue resolution—they outline a phased approach from individual experimentation to cross-team standardization and organizational scaling.
The Current State of AI Adoption in Development Teams
Many organizations purchase AI tool licenses and distribute them broadly, expecting immediate productivity gains. In practice, developers experiment individually—often engaging in “vibe coding”—without shared practices or metrics. This leads to fragmented adoption, inconsistent quality, and difficulty demonstrating return on investment to leadership.
The speakers identify a critical gap: while tools proliferate, teams lack a common language and structured methodology. Success requires moving beyond ad-hoc usage to deliberate integration aligned with specific pain points.
A Framework for Systematic Agentic AI Adoption
The proposed framework operates along two dimensions: organizational pain points and AI maturity levels. Pain points—such as code review bottlenecks, testing coverage, or feature development velocity—must be identified first. Maturity progresses from individual experimentation to team standardization and finally cross-team integration.
Teams begin at their current maturity level and implement solutions appropriate to that stage. For code review bottlenecks, level-one teams conduct structured experimentation with various tools, followed by retrospectives to select winners. Level-two teams document guidelines, define success metrics, and establish processes. Level-three organizations embed AI into pipelines with shared patterns and governance.
Applying the Framework: Code Reviews and Testing
For code reviews, a real-world AWS customer in betting and gaming implemented an agentic workflow using Amazon Bedrock. Pull request events trigger enrichment via data pipelines before an agent analyzes changes against coding standards, security rules, and business requirements. The system posts comments directly, with optional human validation.
Metrics showed over 16,700 developer hours returned annually, allowing focus on higher-value work. Similar patterns apply to testing: starting with AI-assisted unit test generation, teams progress to standardized pipelines and shared test patterns across the organization.
Feature Development with Spec-Driven Approaches
Spec-driven development extends AI assistance across the lifecycle. Rather than isolated prompts, teams collaborate with agents to refine requirements, architectural decisions, and task breakdowns. Amazon Q Developer exemplifies this, generating user stories, acceptance criteria, designs, and implementation tasks from high-level intents.
This approach reduces back-and-forth during sprint planning and ensures generated code aligns with broader context. Workshops help teams adapt the process to their needs, fostering ownership and continuous improvement.
Scaling and Avoiding Common Pitfalls
Successful scaling requires executive sponsorship, dedicated time for experimentation, and clear metrics. Leadership must treat AI adoption as a strategic initiative rather than a side project. Engineers should share learnings and metrics to build momentum.
Pitfalls include unstructured experimentation leading to technical debt, over-reliance on AI without human oversight, and failure to measure impact. The speakers recommend divide-and-conquer: tackle one pain point thoroughly before expanding.
Conclusion: AI as a Multiplier of Good Practices
Agentic AI amplifies existing strengths in clean code, testing, documentation, and collaboration. By following a pragmatic, maturity-aligned path, teams achieve faster delivery, higher quality, and greater developer satisfaction. The framework transforms AI from a hype-driven experiment into a structured capability delivering tangible results.
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[DevoxxGR2026] GenAI on Kubernetes: Training, Inference, and Serving in Production Environments
Lecturer
Alessandro Vozza is a seasoned cloud-native advocate and technologist with deep expertise in Kubernetes and AI/ML operations. He contributes actively to open-source communities and focuses on practical, scalable deployments of generative AI workloads. As a speaker and practitioner, Alessandro emphasizes operational excellence, resource efficiency, and the integration of modern AI tools within established cloud-native platforms.
Abstract
In this hands-on tutorial at Devoxx Greece 2026, Alessandro Vozza guides developers through the complete lifecycle of running generative AI workloads on Kubernetes. From distributed training jobs with GPU scheduling to optimized inference and scalable model serving, the session demonstrates how to leverage operators, autoscaling, vector stores, and frameworks like KServe, Ray, vLLM, and Kubeflow. Attendees gain actionable insights into designing efficient GPU clusters, fine-tuning models securely, and deploying production-grade architectures that integrate seamlessly with existing Kubernetes expertise.
The Convergence of Kubernetes and Generative AI
Kubernetes has evolved into the de facto platform for orchestrating complex, resource-intensive workloads, including those powered by generative AI. Vozza begins by contextualizing the challenges: training large models demands massive parallel computation across GPUs, inference requires low-latency serving under variable traffic, and the entire pipeline must remain observable, secure, and cost-effective. Traditional approaches struggle with these demands, but Kubernetes patterns—scheduling, autoscaling, and declarative resource management—provide a robust foundation.
The session highlights how the community has responded with specialized tools. Projects like Kubeflow address the full ML lifecycle, while KServe and vLLM focus on high-performance inference. These build upon core Kubernetes capabilities, allowing teams to treat AI workloads with the same rigor applied to microservices.
Distributed Training and GPU Orchestration
Training generative models is computationally intensive and benefits enormously from Kubernetes’ scheduling strengths. Vozza demonstrates launching distributed training jobs, emphasizing GPU-aware scheduling through device plugins and resource requests. Nodes are labeled with GPU capacity, enabling the scheduler to place pods on suitable hardware.
The tutorial covers hyperparameter tuning with tools like Katib, which automates experimentation across multiple configurations. Fine-tuning involves augmenting base models with domain-specific data, a process that Kubernetes orchestrates reliably through persistent volumes and checkpointing. Attendees learn to monitor training progress using built-in observability and handle failures gracefully with retries and job controllers.
Resource efficiency emerges as a key theme. Techniques such as multi-instance GPU (MIG) partitioning allow a single physical GPU to support multiple smaller workloads, maximizing utilization without over-provisioning expensive hardware.
Inference Serving and Model Deployment
Once trained, models must be served efficiently. Vozza walks through deploying inference endpoints with KServe, which abstracts the complexities of scaling and routing. vLLM serves as the high-throughput inference engine, leveraging continuous batching and paged attention for superior performance.
The architecture supports multi-model serving, where a single deployment handles various models based on request characteristics. Gateway API extensions make the ingress layer LLM-aware, enabling intelligent routing based on factors like key-value cache state or model specialization. This ensures optimal resource allocation and minimal latency.
Autoscaling plays a critical role. Horizontal Pod Autoscaler (HPA) combined with KEDA reacts to custom metrics such as queue depth or tokens processed per second, dynamically adjusting replicas to match demand while controlling costs.
Operational Considerations and Best Practices
Production readiness demands comprehensive observability. Vozza integrates Prometheus exporters and logging to track token throughput, latency, and GPU utilization. Security best practices include least-privilege access for model endpoints and encrypted communication.
The tutorial addresses common pitfalls: managing model registries for versioning, handling cold starts through caching, and ensuring reproducibility across environments. By treating models as first-class Kubernetes citizens, teams achieve consistent deployments from development to production.
Practical Roadmap and Future Directions
Participants receive a working reference setup they can adapt immediately. Vozza encourages starting small—perhaps with a single-model inference service—before scaling to distributed training and multi-model architectures. The session reinforces that Kubernetes knowledge directly transfers to AI operations, lowering the barrier for traditional platform teams.
Looking ahead, evolving features like dynamic resource allocation and improved GPU topology awareness will further streamline GenAI workloads. The message is clear: Kubernetes is not merely compatible with generative AI; it is becoming the preferred operational layer for the entire lifecycle.
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[DevoxxGR2026] Code That Moves the World: The Rise of Physical AI
Lecturer
Will Sentance is the founder of Standard Material and Codesmith, organizations at the forefront of physical AI infrastructure and AI/software engineering education. A speaker, educator, and practitioner, Sentance bridges software engineering expertise with emerging robotics and autonomous systems. He contributes to research at Oxford and leads initiatives training talent for the next wave of intelligent physical systems.
Abstract
In this forward-looking keynote at Devoxx Greece 2026, Will Sentance explores the profound convergence of software engineering and physical intelligence. Robots and autonomous systems are transitioning from specialized, brittle demonstrations to capable, generalizable agents operating in real-world environments. Sentance details the technological breakthroughs in hardware, data, and foundation models driving this transformation and argues that traditional software engineering skills are central to building the platforms, data pipelines, and integrations required for scalable physical AI deployment.
The Remarkable Progress in Physical Intelligence
Physical AI—systems that sense, understand, and act upon the physical world—has advanced dramatically. Robots now follow natural language instructions, handle novel objects, and demonstrate emergent capabilities. Foundation models for robotics enable zero-shot generalization and long-horizon planning across diverse embodiments.
Companies like Physical Intelligence, Agility Robotics, and others are moving from laboratory experiments to industrial and domestic applications. This shift is fueled by massive investment and rapid iteration.
Core Technological Enablers
Three key areas have transformed the landscape:
Hardware Revolution: Affordable, off-the-shelf components—from full humanoids to grippers and sensors—dramatically lower barriers. Edge computing platforms provide sufficient power for onboard inference.
Data Explosion: Teleoperation, simulation (including sophisticated world models), and real-world deployment generate multimodal datasets at unprecedented scale. Techniques like action chunking address real-time requirements.
AI Models: End-to-end learning replaces traditional control theory. Vision-language-action models predict continuous action trajectories, enabling flexible behavior without exhaustive manual programming.
The Physical AI Technology Stack
Sentance outlines a layered architecture:
- Real-time Control: Low-level, deterministic operations managing actuators and safety at high frequency.
- Platform and Middleware: Abstractions like ROS providing integration, simulation interfaces, and developer tools.
- Intelligence Layer: Foundation models processing vision, language, and proprioception to generate actions.
- Data and Learning Loop: Continuous collection, training, evaluation, and deployment cycle.
Opportunities for Software Engineers
Contrary to initial impressions, software engineers are perfectly positioned to lead this revolution. Approximately 80% of the required work involves familiar disciplines: systems architecture, platform engineering, data pipelines, low-level optimization, and agentic integration.
Roles at leading organizations emphasize scalable frameworks, reliable deployment, observability, and integration of AI models into production—skills honed in cloud-native and distributed systems development.
New challenges center on real-time constraints, physical dynamics, and managing massive multimodal datasets, but these build directly upon existing expertise.
Getting Started with Physical AI
Sentance encourages practical experimentation using affordable hardware like the SO-101 and open tools. Developers can quickly train policies for simple tasks such as closing a laptop lid, experiencing the full cycle from data collection to deployment.
The physical world represents the next major platform for code. Software engineers who embrace this frontier will shape the coming industrial transformation.
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[DevoxxGR2026] Bootiful Spring Boot 4: Exploring the Latest Advancements with Java 25
Lecturer
Josh Long is a Spring Developer Advocate at VMware, widely recognized as one of the most prominent voices in the Spring ecosystem. Known affectionately as “Mr. Spring,” he is the author of numerous books and a prolific speaker who travels the globe sharing insights on modern Java development. Long co-hosts the “Coffee with a Java Champion” YouTube channel and continues to champion practical, production-ready Spring applications.
Abstract
In this engaging session at Devoxx Greece 2026, Josh Long showcases the transformative capabilities of Spring Boot 4 alongside Java 25. Through live coding of a dog adoption service, he demonstrates powerful new features including virtual threads, API versioning, modular architecture with Spring Modulith, resilient patterns, and seamless integration with AI capabilities. The presentation highlights how the Spring ecosystem empowers developers to build scalable, observable, secure, and intelligent applications with remarkable efficiency.
Java 25 and the Evolution of Spring Boot 4
Spring Boot 4 represents a significant generational leap, aligned with Spring Framework 7. Long emphasizes the decomposition of auto-configuration, resulting in leaner classpaths and faster startup times. Java 25 introduces compelling enhancements, most notably the ability to run simple applications with a single void main() method, effectively delivering the first truly elegant Java scripting experience.
These advancements set the stage for building modern, efficient services that leverage the full power of the JVM while maintaining developer productivity.
Building a Modular Dog Adoption Service
Long begins with a practical example: a service to help adopt dogs. Using Spring Initializr, he configures a project with PostgreSQL, Spring Data JDBC, web support, security, observability through OpenTelemetry and Actuator, and development tools.
The application employs a clean, feature-oriented package structure rather than traditional layered architecture. Records simplify domain modeling, while Spring Data repositories provide type-safe data access with compile-time query generation via AOT processing—beneficial for both JVM and native image deployments.
API Versioning and Resilience Features
To handle evolving requirements, Long demonstrates Spring’s new API versioning capabilities. Multiple endpoint versions coexist, with sensible defaults and header-based selection, ensuring backward compatibility.
Resilience4j integration showcases retryable methods and circuit breakers. Long simulates downstream failures to illustrate automatic recovery, highlighting how declarative resilience patterns simplify robust service design.
Modular Architecture with Spring Modulith
A standout demonstration involves refactoring into feature modules—dogs, cats, and veterinary services—using Spring Modulith. This enforces architectural boundaries at compile time while supporting event-driven communication between modules through ApplicationModuleListener and the outbox pattern for reliable, eventually consistent inter-module interactions.
The framework automatically generates documentation, C4 architecture diagrams, and verifies module dependencies, bridging the gap between intended design and runtime reality.
Security and Production Readiness
Security configuration leverages Spring Security 7’s additive customizers, preserving sensible defaults while enabling features like one-time token login and password migration. Passkeys (WebAuthn) integration provides passwordless authentication using biometrics, representing a significant usability and security improvement.
Observability is built-in through Actuator and OpenTelemetry, with production considerations like resource limits addressed from the start.
Integrating AI Capabilities
Long concludes by incorporating Spring AI 2.0, demonstrating how to augment the application with intelligent assistants. Using skills and tool calling, the service can answer domain-specific questions about dogs and cats, showcasing the natural convergence of Spring Boot with modern AI workflows.
The Bright Future of Java and Spring Development
Throughout the session, Long reinforces that the combination of Java’s efficiency, Spring’s comprehensive ecosystem, and new generative AI tools positions developers exceptionally well. Despite industry hype cycles, the fundamentals of solid engineering—modularity, resilience, observability, and security—remain paramount.
Spring Boot 4 and Java 25 deliver the tools necessary to build systems that are faster, more scalable, more maintainable, and more intelligent than ever before.