Microservices architecture has become a buzzword in the tech industry, promising unparalleled agility, scalability, and resilience. Yet, according to Gartner, more than 90% of organizations attempting to adopt microservices will fail. How can you ensure you're part of the successful 10%?

Success begins with looking beyond the superficial topology and understanding the unique demands this architectural style places on the teams, the organization, and the environment. These demands must be balanced against the current business needs and organizational realities while maintaining a clear and pragmatic path for incremental evolution.

In this session, Michael will share some real-world examples, practical insights, and proven techniques to balance both the power and complexities of microservices. Whether you're considering adopting microservices or already on the journey and facing challenges, this session will equip you with the knowledge and tools to succeed.

Architectural decisions are often influenced by blindspots, biases, and unchecked assumptions, which can lead to significant long-term challenges in system design. In this session, we’ll explore how these cognitive traps affect decision-making, leading to architectural blunders that could have been avoided with a more critical, holistic approach.

You’ll learn how common biases—such as confirmation bias and anchoring—can cloud judgment, and how to counteract them through problem-space thinking and reflective feedback loops. We’ll dive into real-world examples of architectural failures caused by biases or narrow thinking, and discuss strategies for expanding your perspective and applying critical thinking to system design.

Whether you’re an architect, developer, or technical lead, this session will provide you with tools to recognize and mitigate the impact of biases and blindspots, helping you make more informed, thoughtful architectural decisions that stand the test of time.

This workshop will explore the principles of the Ports and Adapters pattern (also called the Hexagonal Architecture) and demonstrate how to refactor legacy code or design new systems using this approach. You’ll learn how to organize your domain logic and move UI and infrastructure code into appropriate places within the architecture. The session will also cover practical refactoring techniques using IntelliJ and how to apply Domain Driven Design (DDD) principles to ensure your system is scalable, maintainable, and well-structured.

What You’ll Learn:

1. What is Hexagonal Architecture?
   Understand the fundamental principles of Hexagonal Architecture, which helps isolate the core business logic (the domain) from external systems like databases, message queues, or user interfaces. This architecture is designed to easily modify the external components without affecting the domain.

2. What are Ports and Adapters?
   Learn the key concepts of Ports and Adapters, the core elements of Hexagonal Architecture. Ports define the interface through which the domain interacts with the outside world, while Adapters implement these interfaces and communicate with external systems.

3. Moving Domain Code to Its Appropriate Location:
   Refactor your domain code to ensure it is correctly placed in the core domain layer. You will learn how to separate domain logic from external dependencies, ensuring that business rules are isolated and unaffected by user interface or infrastructure changes.

4. Moving UI Code to Its Appropriate Location:
   Discover how to refactor UI code by decoupling it from the domain logic and placing it in the appropriate layers. You’ll learn how to use the Ports and Adapters pattern to allow the user interface to communicate with the domain without violating architectural boundaries.

5. Using Refactoring Tools in IntelliJ:
   Learn how to use IntelliJ’s powerful refactoring tools to streamline code movement. Techniques such as Extract Method, Move Method, Extract Delegate, and Extract Interface will be applied to refactor your codebase.

6. Applying DDD Software Principles:
   We’ll cover essential Domain-Driven Design principles, such as Value Objects, Entities, Aggregates, and Domain Events.

7. Refactoring Techniques:
   Learn various refactoring strategies to improve code structure, Extract Method, Move Method, Extract Delegate, Extract Interface, and Sprout Method and Class

8. Verifying Code with Arch Unit:
   Ensure consistency and package rules using Arch Unit, a tool for verifying the architecture of your codebase. You will learn how to write tests confirming your project adheres to the desired architectural guidelines, including separating layers and boundaries.

Who Should Attend:

This workshop is perfect for developers who want to improve their understanding of Ports and Adapters Architecture, apply effective refactoring techniques, and leverage DDD principles for designing scalable and maintainable systems.

Workshop Requirements

If you wish to do the interactive labs:

1. Java 21+ Higher
2. IntelliJ (a must)
3. Maven

This workshop will explore the principles of the Ports and Adapters pattern (also called the Hexagonal Architecture) and demonstrate how to refactor legacy code or design new systems using this approach. You’ll learn how to organize your domain logic and move UI and infrastructure code into appropriate places within the architecture. The session will also cover practical refactoring techniques using IntelliJ and how to apply Domain Driven Design (DDD) principles to ensure your system is scalable, maintainable, and well-structured.

What You’ll Learn:

1. What is Hexagonal Architecture?
   Understand the fundamental principles of Hexagonal Architecture, which helps isolate the core business logic (the domain) from external systems like databases, message queues, or user interfaces. This architecture is designed to easily modify the external components without affecting the domain.

2. What are Ports and Adapters?
   Learn the key concepts of Ports and Adapters, the core elements of Hexagonal Architecture. Ports define the interface through which the domain interacts with the outside world, while Adapters implement these interfaces and communicate with external systems.

3. Moving Domain Code to Its Appropriate Location:
   Refactor your domain code to ensure it is correctly placed in the core domain layer. You will learn how to separate domain logic from external dependencies, ensuring that business rules are isolated and unaffected by user interface or infrastructure changes.

4. Moving UI Code to Its Appropriate Location:
   Discover how to refactor UI code by decoupling it from the domain logic and placing it in the appropriate layers. You’ll learn how to use the Ports and Adapters pattern to allow the user interface to communicate with the domain without violating architectural boundaries.

5. Using Refactoring Tools in IntelliJ:
   Learn how to use IntelliJ’s powerful refactoring tools to streamline code movement. Techniques such as Extract Method, Move Method, Extract Delegate, and Extract Interface will be applied to refactor your codebase.

6. Applying DDD Software Principles:
   We’ll cover essential Domain-Driven Design principles, such as Value Objects, Entities, Aggregates, and Domain Events.

7. Refactoring Techniques:
   Learn various refactoring strategies to improve code structure, Extract Method, Move Method, Extract Delegate, Extract Interface, and Sprout Method and Class

8. Verifying Code with Arch Unit:
   Ensure consistency and package rules using Arch Unit, a tool for verifying the architecture of your codebase. You will learn how to write tests confirming your project adheres to the desired architectural guidelines, including separating layers and boundaries.

Who Should Attend:

This workshop is perfect for developers who want to improve their understanding of Ports and Adapters Architecture, apply effective refactoring techniques, and leverage DDD principles for designing scalable and maintainable systems.

Workshop Requirements

If you wish to do the interactive labs:

1. Java 21+ Higher
2. IntelliJ (a must)
3. Maven

Join us for an indepth exploration of cuttingedge messaging styles in your large domain.

Here, we will discuss the messaging styles you can use in your business.

• Event Sourcing
• EventDriven Architecture
• Claim Check
• Event Notification
• Event Carried State Transfer
• Domain Events

2025 has been declared the “year of the agents”. There's a lot of potential, but there's also a lot to understand and make sense of. AI Agents are combinations of LLMs, tools, and custom roles that can autonomously perform tasks and make decisions based on context and user input. There are multiple types of agents and multiple agents can be managed together to cooperatively handle individual tasks that are part of a larger project to accomplish an overall goal.

By combining capabilities like tool access, multi-step reasoning, and real-time adjustments, agents can construct and complete complex workflows and intelligent solutions. In this presentation, we'll look at what AI agents are, how they really work, different types of agents and design patterns and touch on frameworks, challenges and more.

Ever since OpenAI added a chat interface in front of its GPT models, large language models (LLMs) have become all the rage. But with so many different players, different models, related technologies, and confusing terminology, it can be challenging to understand what each means and is used/useful for. Join us as we help you understand and navigate the ever growing LLM ecosystem.

Join expert Brent Laster to break down the different model types, creators, uses, and terminology you'll encounter across the vast array of LLMs available today. Understand the significance and meaning of GPT vs Llama vs Claude vs … Learn what the naming conventions and things like quantization mean. Learn why some models are best suited for specialized tasks like sentiment analysis, translation, classification and why some are more suited to general use. After attending this session, you'll be well-versed in the LLM semantics and ready to start working with many different models.

GitHub Copilot is a popular AI assistant that helps software developers more easily create content, get answers to coding-related questions, and handle many of the boilerplate tasks of software development. But it can also do much more in the areas where it can be used. Join Copilot expert (and author of the upcoming “Learning GitHub Copilot” book from O'Reilly) for a quick overview of some of the additional tips and tricks to allow you to make the most from this AI assistant.

Most users know GitHub Copilot can help with the basics of code generation, creating test cases, documentation, etc. But because the tool is AI based, there's a lot more that it can help you with in these areas simply by asking it and giving it the right prompts. In this session, we'll take a look at some ways to leverage beyond the basics of these tasks to creating results that are usable more deeply and widely. We'll also look at some ways to compensate when Copilot does not have the most recent information, or needs to be picking up more relevant context.

In this presentation, we'll cover the options, tips, and tricks for using GitHub Copilot to help us identify how to test code, generate tests for existing code, and generate tests before the code.

Join global trainer, speaker, and author of the upcoming book, Learning GitHub Copilot, Brent Laster as he presents material on multiple ways to leverage Copilot for testing your code on any platform and framework.

Have you wondered what options GitHub Copilot can provide for helping to not only write your code, but test your code? In this session, we'll examine some key ways that Copilot can support you in ensuring you have the basic testing needs covered. In particular, we'll cover:

• A quick overview of GitHub Copilot
• Letting Copilot tell you how to get started testing for a new language
• Creating tests for existing code through one-step commands
• Creating tests for existing code with comments
• Creating tests for existing code with explicit prompts
• Validating inputs to functions using Copilot
• Using Copilot to build out tests for edge cases
• Leveraging testing frameworks
• Building tests before the code - TDD with Copilot

The age of hypermedia-driven APIs is finally upon us, and it’s unlocking a radical new future for AI agents. By combining the power of the Hydra linked-data vocabulary with semantic payloads, APIs can become fully self-describing and consumable by intelligent agents, paving the way for a new class of autonomous systems. In this session, we’ll explore how mature REST APIs (level 3) open up groundbreaking possibilities for agentic systems, where AI agents can perform complex tasks without human intervention.

You’ll learn how language models can understand and interact with hypermedia-driven APIs, and how linked data can power autonomous decision-making. We’ll also examine real-world use cases where AI agents use these advanced APIs to transform industries—from e-commerce to enterprise software. If you’re ready to explore the future of AI-driven systems and how hypermedia APIs are the key to unlocking it, this session will give you the knowledge and tools to get started.

IDEs have provided ways to refactor code for a long time now. In spite of their effectiveness, that journey is arduous and time consuming. Reluctance to refactor increases the cost of development. However, refactoring for the sake of doing so can lead to greater productivity loss as well.

In this presentation we will use data driven approach. We will take examples of code, measure code quality, and then use automated code transformation tools to refactor the code, and then, once again, measure the quality of code and see how much we have improved. This can help us to not only refactor faster but also see the benefits realized and motivate us to move faster with greater efficiency.

Dividing a large problem into subproblems that are scheduled to run on different threads is an often used solution. We've used executors and fork join pool for such problems in the past. These solutions, in spite of being very powerful, have significant limitations.

In this presentation we will start with those solutions, discuss the issues, and learn how structured concurrency, introduced in Java 21, can help solve such problems more effectively and elegantly.

Threads are lightweight, but do not scale well. That's one of the reasons we have been focused on the elastic capabilities on the cloud. Unfortunately that has an impact both on our environment and your companies wallet.

In this presentation we will learn how virtual threads reduce those impacts and help us to create scalable applications with minimum change to code.

One of the coolest aspect of Java is the fact that the newer features simply do not live in isolation, but interplay quite nicely with each other. A place where this is clearly evident is the synergy between records, sealed classes, and pattern matching.

In this presentation we will focus on pattern matching and how its power is enhanced by records and also the sealed classes. The details presented will help you to make the best use of all the three features in your own applications.

By now, you've no doubt noticed that Generative AI is making waves across many industries. In between all of the hype and doubt, there are several use cases for Generative AI in many software projects. Whether it be as simple as building a live chat to help your users or using AI to analyze data and provide recommendations, Generative AI is becoming a key piece of software architecture.

So how can you implement Generative AI in your projects? Let me introduce you to Spring AI.

For over two decades, the Spring Framework and its immense portfolio of projects has been making complex problems easy for Java developers. And now with the new Spring AI project, adding Generative AI to your Spring Boot projects couldn't be easier! Spring AI brings an AI client and templated prompting that handles all of the ceremony necessary to communicate with common AI APIs (such as OpenAI and Azure OpenAI). And with Spring Boot auto-configuration, you'll be able to get straight to the point of asking questions and getting answers your application needs.

In this session, we'll consider a handful of use cases for Generative AI and see how to implement them with Spring AI. We'll start simple, then build up to some more advanced uses of Spring AI that employ your application's own data when generating answers.

In this example-driven session, we'll review several tips and tricks to make the most out of your Spring development experience. You'll see how to apply the best features of Spring and Spring Boot, including the latest and greatest features of Spring Framework 6.x and Spring Boot 3.x with an eye to what's coming in Spring 7 and Boot 4.

Spring has been the de facto standard framework for Java development for nearly two decades. Over the years, Spring has continued to evolve and adapt to meet the ever-changing requirements of software development. And for nearly half that time, Spring Boot has carried Spring forward, capturing some of the best Spring patterns as auto-configuration.

As with any framework or language that has this much history and power, there are just as many ways to get it right as there are to get it wrong. How do you know that you are applying Spring in the best way in your application?

Workshop Requirements

You'll need…

• A Java IDE of your choice. I'll be using IntelliJ, but you are welcome to use Spring Tools, Netbeans, or whatever you prefer for Java development. (Spring Tools–either the Eclipse or VSCode version–or IntelliJ is recommended, though).
• Java 17 or higher. I recommend using SDKMan (https://sdkman.io/) for managing multiple versions of Java on your machine. I'll be using Zulu 17.0.6 for most tasks, but may switch to a Liberica NIK version for purposes of native builds.
• Docker (optional, but may be necessary for some tasks)

Security problems empirically fall into two categories: bugs and flaws. Roughly half of the problems we encounter in the wild are bugs and about half are design flaws. A significant number of the bugs can be found through automated testing tools which frees you up to focus on the more pernicious design issues. 

 In addition to detecting the presence of common bugs, however, we can also imagine automating the application of corrective refactoring. In this talk, I will discuss using OpenRewrite to fix common security issues and keep them from coming back.

In this talk we will focus on:

Using OpenRewrite to automatically identify and fix known security vulnerabilities.
Integrating security scans with OpenRewrite for continuous improvement.
*Free up your time to address larger concerns by addressing the pedestrian but time-consuming security bugs.

If you are getting tired of the appearance of new types of databases… too bad. We are increasingly relying on a variety of data storage and retrieval systems for specific purposes. Data does not have a single shape and indexing strategies that work for one are not necessarily good fits for others. So after hierarchical, relational, object, graph, columnoriented, document, temporal, appendonly, and everything else, get ready for Vector Databases to assist in the systematization of machine learning systems.

This will be an overview of the benefits of vectors databases as well as an introduction to the major players.

We will focus on open source versus commercial players, hosted versus local deployments, and the attempts to add vector search capabilities to existing storage systems.

We will cover:

• A brief overview of vectors
• Why vectors are so important to machine learning and datadriven systems
• Overview of the offerings
• Adding vector search to other systems
• Sample use cases shown with one of the key open source engines

If you are getting tired of the appearance of new types of databases… too bad. We are increasingly relying on a variety of data storage and retrieval systems for specific purposes. Data does not have a single shape and indexing strategies that work for one are not necessarily good fits for others. So after hierarchical, relational, object, graph, columnoriented, document, temporal, appendonly, and everything else, get ready for Vector Databases to assist in the systematization of machine learning systems.

This will be an overview of the benefits of vectors databases as well as an introduction to the major players.

We will focus on open source versus commercial players, hosted versus local deployments, and the attempts to add vector search capabilities to existing storage systems.

We will cover:

• A brief overview of vectors
• Why vectors are so important to machine learning and datadriven systems
• Overview of the offerings
• Adding vector search to other systems
• Sample use cases shown with one of the key open source engines

Platform engineering is the latest buzzword, in a industry that already has it's fair share. But what is platform engineering? How does it fit in with DevOps and Developer Experience (DevEx)? And is this something your organization even needs?

In this session we will aim to to dive deep into the world of platform engineering. We will see what platform engineering entails, how it is the logical succession to a successful DevOps implementation, and how it aims to improve the developer experience. We will also uncover the keys to building robust, sustainable platforms for the future

Since ChatGPT rocketed the potential of generative AI into the collective consciousness there has been a race to add AI to everything. Every product owner has been salivating at the possibility of new AIPowered features. Every marketing department is chomping at the bit to add a “powered by AI” sticker to the website. For the average layperson playing with ChatGPT's conversational interface, it seems easy however integrating these tools securely, reliably, and in a costeffective manner requires much more than simply adding a chat interface. Moreover, getting consistent results from a chat interface is more than an art than a science. Ultimately, the chat interface is a nice gimmick to show off capabilities, but serious integration of these tools into most applications requires a more thoughtful approach.

This is not another “AI is Magic” cheerleading session, nor an overly critical analysis of the field. Instead, this session looks at a number of valid usecases for the tools and introduces architecture patterns for implementing these usecases. Throughout we will explore the tradeoffs of the patterns as well as the application of AI in each scenario. We'll explore usecases from simple, direct integrations to the more complex involving RAG and agentic systems.

Although this is an emerging field, the content is not theoretical. These are patterns that are being used in production both in Michael's practice as a handson software architect and beyond.

Architects must maintain their breadth, and this session will build on that to prepare you for the inevitable AIpowered project in your future.

Since ChatGPT rocketed the potential of generative AI into the collective consciousness there has been a race to add AI to everything. Every product owner has been salivating at the possibility of new AIPowered features. Every marketing department is chomping at the bit to add a “powered by AI” sticker to the website. For the average layperson playing with ChatGPT's conversational interface, it seems easy however integrating these tools securely, reliably, and in a costeffective manner requires much more than simply adding a chat interface. Moreover, getting consistent results from a chat interface is more than an art than a science. Ultimately, the chat interface is a nice gimmick to show off capabilities, but serious integration of these tools into most applications requires a more thoughtful approach.

This is not another “AI is Magic” cheerleading session, nor an overly critical analysis of the field. Instead, this session looks at a number of valid usecases for the tools and introduces architecture patterns for implementing these usecases. Throughout we will explore the tradeoffs of the patterns as well as the application of AI in each scenario. We'll explore usecases from simple, direct integrations to the more complex involving RAG and agentic systems.

Although this is an emerging field, the content is not theoretical. These are patterns that are being used in production both in Michael's practice as a handson software architect and beyond.

Architects must maintain their breadth, and this session will build on that to prepare you for the inevitable AIpowered project in your future.

Java’s evolution is remarkable, and the leap from JDK 17 to the current version brings a wealth of powerful features to elevate your projects. Join us for an exciting session to explore select JEPs (Java Enhancement Proposals) introduced up to today, diving into their use cases and practical benefits for your work or open-source initiatives.

What You’ll Learn:
How to enable and utilize advanced Java features introduced in JDK 23.
Real-world demonstrations of cutting-edge updates, including:

• Stream Gatherers: Handle complex data streams with ease.
• Statements Before super(): Test invariants without constructing objects.
• Stable Values
• Unnamed Variables and Parameters: Enhance code readability and maintainability.
• Launch Multi-File Source-Code Programs: Rapidly prototype with multiple source files.
• Implicitly Declared Classes & Enhanced Main Methods: Streamline application development.
• Updates on switch Expressions: We will discuss where we are with pattern matching as well as dealing with primitives
• I may also sneak in something about my never-ending love for SimpleWebServer.

Why Attend?
Learn how to advocate for and implement your organization's latest Java tools and practices. Gain the knowledge you need to sell the value of next-generation Java and stay at the forefront of software development.

In today’s data-driven world, the ability to process and analyze data in real time is no longer a luxury—it’s a necessity. Apache Flink, a powerful stream processing framework, has emerged as a game-changer for handling high-throughput, low-latency data applications.

In this session, you’ll gain a clear understanding of what Apache Flink is, how it works, and why it’s become a cornerstone for modern data infrastructure. We’ll explore key features such as its robust stream and batch processing capabilities, event-time handling, stateful computations, and fault tolerance. You’ll also discover how Flink integrates seamlessly with popular systems like Kafka, Kubernetes, and major cloud platforms.

Whether you’re working with real-time analytics, event-driven applications, or machine learning pipelines, Apache Flink provides the scalability and flexibility needed to turn massive streams of data into actionable insights. Join us to see why Flink is critical to modern data ecosystems and learn how to start leveraging its power in your projects.

One of the nice operational features of the REST architectural style as an approach to API Design is that is allows for separate evolution of the client and server. Depending on the design choices a team makes, however, you may be putting a higher burden on your clients than you intend when you introduce breaking changes.

 By taking advantage of the capabilities of OpenRewrite, we can start to manage the process of independent evolution while minimizing the impact. Code migration and refactoring can be used to transition existing clients away from older or deprecated APIs and toward new versions with less effort than trying to do it by hand.

In this talk we will focus on:

Managing API lifecycle changes by automating the migration from deprecated to supported APIs.
Discussing API evolution strategies and when they require assisted refactoring and when they don’t.
*Integrating OpenRewrite into API-first development to ensure client code is always up-to-date with ease.

REST APIs often fall into a cycle of constant refactoring and rewrites, leading to wasted time, technical debt, and endless rework. This is especially difficult when you don't control the API clients.

But what if this could be your last major API refactor? In this session, we’ll dive into strategies for designing and refactoring REST APIs with long-term sustainability in mind—ensuring that your next refactor sets you up for the future.

You’ll learn how to design APIs that can adapt to changing business requirements and scale effectively without requiring constant rewrites. We’ll explore principles like extensibility, versioning, and decoupling, all aimed at future-proofing your API while keeping backward compatibility intact. Along the way, we’ll examine real-world examples of incremental API refactoring, where breaking the cycle of endless rewrites is possible.

This session is perfect for API developers, architects, and tech leads who are ready to stop chasing their tails and want to invest in designing APIs that will stand the test of time—so they can focus on building great features instead of constantly rewriting code.

Microservices have fundamentally changed the way we develop and deploy applications. Everything from team topologies, to DevOps to observability—everything changed, and for the better.

However, it's not all rainbows and unicorns. Operationalizing microservices is hard. Microservices encourage WET (write everything twice) to ensure that services are as decoupled from each other as possible. But how does that work when we have to deal with cross-cutting concerns that we need for every service?

Enter the service mesh. Service meshes like Istio allow us to “slot” in cross-cutting architectural concerns within a kubernetes cluster, letting our services focus on solving actual business concerns.

In this fast-paced session, we will blitz through what Istio is, how it works, and what facilities it offers to DRY out your microservices. Come see how Istio can make your cluster programmable and application-aware.

In this session we'll take a tour of some features that you might or might not have heard of, but can significantly improve your workflow and day-to-day interaction with Git.

Git continues to see improvements daily. However, work (and life) can take over, and we often miss the changelog. This means we don't know what changed, and consequently fail to see how we can incorporate those in our usage of Git.

In this session we will look at some features you are probably aware of, but haven't used, alongside new features that Git has brought to the table. Examples include:

• Rebase and interactive rebase
• restore/switch and when to use them
• worktrees
• shallow-clones
• Git's filesystem monitor

By the end of this session, you will walk away with a slew of new tools in your arsenal, and a new perspective on how this can help you and your colleagues get the most out of Git.

In this session we will discuss what modular monoliths are, what they bring to the table, and how they offer a great middle ground between monoliths and distributed architectures like microservices.

Monoliths get a bad rep. Experienced software developers have seen one too many monoliths devolve into a big ball of mud, leaving everyone frustrated, with an itch to do a “rewrite”. But monoliths have their pros! They are usually simpler, easier to understand, and faster to build and debug.

On the other side of the spectrum you have microservices—that offer scale, both technically and organizationally, as well as having the badge of honor of being “the new cool kid on the block”. But productionizing microservices is HARD.

Why can't we have our cake and eat it too? Turns out, we can. In this session we will explore the modular monolith—all the upsides of a monolith with none of the downsides of distributed architectures. We'll see what it means to build a modular monolith, and how that differs from a traditional layered architecture. We will discuss how we can build architectural governance to ensure our modules remain decoupled. Finally we'll see how our modules can communicate with one another without violating modularity.

By the end of this session you'll walk away with a greater appreciation for the monolith, and see how you can leverage this within your system architecture.

Introducing Spring Modulith

Although microservices are still a useful architectural choice, the balance of additional complexity and the advantages of microservice architecture do not necessarily work out in the benefit of all applications. While most application will benefit from improved modularity, the challenges that come with distributed computing may be too much for some applications to take on. A well-structured and modular monolithic application might be a better fit.

In this session, we'll explore Spring Modulith, a relatively new Spring library that enables developers to build well-structured Spring Boot applications, guiding them in discovering domain-driven modules, and verifying that the modular arrangement is correct. We'll also see how Spring Modulith assists with modular integration testing and documentation.

Spring Security has long been a powerful guard to place around your Spring applications, providing authentication, authorization, and many more concerns around keeping your application secure.

As time has progressed, Spring Security has evolved to provide even more capabilities, but has applied some self-improvement to make working with Spring Security even easier. That is to say, the way you configure and apply Spring Security today has changed dramatically from it's early XML-oriented approach and is even different now than some of the more recent Java-based configuration strategies.

In this example-driven session, we'll explore the latest and greatest that Spring Security has to offer, with an emphasis on how to apply security aspects to your applications with the latest configuration styles supported by Spring Security. You'll see how security is enabled in modern Spring applications using the Lambda DSL configuration approach, the preferred way to configure Spring Security and the ONLY way to configure Spring Security 7.

In this session, we'll cover several useful prompt engineering techniques as well as some emerging patterns that are categorized within the “Agentic AI” space and see how to go beyond simple Q&A to turn your LLM of choice into a powerful ally in achieving your goals.

At it's core, Generative AI is about submitting a prompt to an LLM-backed API and getting some response back. But within that interaction there is a lot of nuance, particularly with regard to the prompt itself.

It's important to know how to write effective prompts, choosing the right wording and being clear about your expectations, to get the best responses from an LLM. This is often called “prompt engineering” and includes several patterns and techniques that have emerged in the Gen AI space.