Devnexus 2027: Helping Developers Navigate the New Age of AI

Software development is undergoing one of its most significant changes in decades.

AI is no longer something we encounter only in specialist machine-learning projects. It is becoming part of how we write code, explore unfamiliar systems, test applications, review changes, manage infrastructure and make architectural decisions. At the same time, many of us are being asked to incorporate AI capabilities into the products and platforms we build.

For Devnexus 2027, we are reframing our conference content around this new reality.

We are not moving away from Java or the technologies that have always formed the foundation of Devnexus. Instead, we are examining how those technologies—and our responsibilities as developers—are evolving as AI becomes part of everyday software development.

Building a conference around what we need next

Devnexus is the largest and longest-running Java ecosystem conference in the United States. The Atlanta Java Users Group founded the conference in 2004, and today we bring together more than 1,500 developers for hands-on workshops, expert-led sessions, live demonstrations and direct interaction with speakers and Java Champions.

We have always evolved Devnexus alongside the developer community. In recent years, we have expanded the program to include generative AI, AI engineering, developer tools, architecture, security and technical leadership. At Devnexus 2026, we featured sessions on AI coding platforms, multi-agent systems, MCP, enterprise AI frameworks, AI-assisted software engineering and building production-ready applications with Spring AI.

For 2027, we are taking the next step.

Rather than treating AI as a single, self-contained topic, we are considering its impact across the entire technology landscape. We believe developers need to understand not only how AI models work, but also how AI is changing application design, development workflows, production operations, security, leadership and career development.

Our ten tracks for Devnexus 2027

Java & the JVM Ecosystem

Java remains at the heart of Devnexus.

In this track, we will cover the continuing evolution of Java, the JVM and the frameworks we depend on. Topics may include new language and platform capabilities, performance, concurrency, testing, observability, Spring, Jakarta EE and other important JVM technologies.

AI may be changing how we create software, but we still need a deep understanding of the platforms on which that software runs.

System Design

AI does not remove the need for sound architecture. In many cases, it makes system design even more important.

We will explore how we can design scalable, resilient and maintainable systems. This track will examine established architectural concerns alongside emerging patterns for systems that incorporate models, agents, retrieval, data pipelines and probabilistic components.

Our goal is to help developers make better design decisions, whether they are building a traditional distributed application, an AI-enabled service or a combination of the two.

Developer Tools

Our productivity has always depended on more than a programming language.

This track will cover the tools and practices that support the complete software-development lifecycle, including IDEs, build tools, testing, CI/CD, observability, platform engineering and automation.

We will focus on practical improvements that help us understand our systems, reduce friction and deliver dependable software.

AI Developer Tools

AI coding assistants are quickly becoming part of our standard toolkit, but using them effectively requires more than installing a plugin.

We will examine coding agents, AI-assisted IDEs, prompt and context engineering, code generation, testing, documentation, code review and the emerging protocols that allow AI tools to interact with development environments.

We also want to look beyond impressive demonstrations and ask harder questions: Where do these tools genuinely improve productivity? Where do they introduce risk? How should we review and validate AI-generated work?

Building AI-Native Applications

Adding an AI-powered feature to an existing application is not the same as designing an application around AI from the beginning.

In this track, we will focus on the architecture and development of AI-native applications. Topics may include agents, retrieval-augmented generation, knowledge graphs, vector search, tool use, multimodal interfaces, memory, context management and model integration.

Our emphasis will be on building applications that use AI to solve meaningful problems—not simply attaching a chat interface to an existing product.

Production AI Engineering

A prototype that works during a demonstration is only the beginning.

When we take AI systems into production, they must be observable, secure, scalable, testable and economically sustainable. We need ways to evaluate output quality, manage latency and cost, protect sensitive data and respond when model behavior changes.

This track will address the engineering disciplines we need to move AI applications from experimentation into reliable production environments, including evaluation, monitoring, infrastructure, data pipelines, model selection and operational governance.

Security

AI creates new opportunities, but it also changes the attack surface.

We will continue to cover application, platform and software-supply-chain security while also addressing the risks introduced by AI systems. These may include prompt injection, data leakage, insecure tool access, model and dependency risks, identity, authorization and the safe use of coding agents.

We cannot treat security as an additional step performed after an AI system has been built. We must make it part of the architecture and development process from the start.

Tech Leadership

Technology leaders now face decisions that extend far beyond selecting an AI tool.

We must determine where AI can produce meaningful value, establish appropriate guardrails, rethink development workflows and help our teams adapt without sacrificing engineering quality. As we have already emphasized through our AI leadership content, effective AI adoption changes the entire software-development lifecycle—it is not simply the purchase of a coding assistant.

This track will provide practical guidance for engineering managers, architects, technical leads and senior individual contributors responsible for guiding teams through that transition.

Soft Skills in an AI Era

As AI assumes more routine technical work, we believe distinctly human capabilities will become even more valuable.

We still need to communicate decisions, challenge assumptions, mentor colleagues, collaborate across disciplines and explain complex systems to people with different levels of technical knowledge. We also need the judgment to recognize when an AI-generated answer is incomplete, misleading or simply wrong.

This track will explore communication, critical thinking, mentorship, career development, collaboration and the changing meaning of technical expertise.

Unobtainium: Frontier Tech & Spring

Some technologies do not fit neatly into an established category—but may still shape what we build next.

Unobtainium is our space for frontier technologies, ambitious ideas and unconventional technical work. It will also provide a home for advanced Spring content that pushes beyond introductory framework use.

We expect deep technical explorations, emerging approaches and sessions that challenge conventional thinking. Not every idea presented here will become mainstream, but each should give us something new to consider.

More signal, less noise

We are surrounded by increasingly bold claims about what AI can do. At Devnexus 2027, we will focus on the knowledge developers need to separate lasting engineering change from short-lived hype.

Across ten tracks and more than 100 sessions, we want to help attendees answer practical questions:

• How should we use AI tools responsibly and effectively?
• What changes when AI becomes part of an application’s architecture?
• How do we evaluate and operate AI systems in production?
• Which established engineering principles become even more important?
• What skills will developers and technology leaders need next?

We will continue to make Devnexus a place where developers can learn directly from experienced practitioners, see technologies demonstrated in real applications and have honest conversations about what is and is not working

Devnexus 2027 takes place April 5–7 at the Georgia World Congress Center in AtlantaWrite your content here using either markdown or html