Beat the rush and join us early for complimentary barista-made coffee and breakfast.

The cloud-native playbook that shaped the last decade is beginning to show its limits. Drawing on experience building global private cloud and AI infrastructure across AWS Bedrock, Azure AI Foundry and OpenShift AI with NVIDIA GPUs, this session explores how modern architecture is evolving, and why developer experience and AI readiness must now be designed into the platform.

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Key takeaways:

• Where traditional cloud-native architecture is falling short
• Why developer experience is now an architectural priority
• How platforms must evolve to support AI workloads

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AI is no longer just assisting developers; it is becoming an active, code-generating team member. While this dramatically accelerates release frequency, it also shifts the perimeter of the software supply chain directly into automated IDEs and pipelines. When code generation scales exponentially, traditional security checkpoints quickly become bottlenecks or introduce alert fatigue.

In this keynote, Mike Holland, Senior Solutions Engineer at JFrog, addresses the central tension of modern DevOps: achieving frictionless velocity while maintaining rigorous governance. We will break down why binary-level visibility is essential for AI-generated artifacts, and how to establish a single system of record to secure every release from build to production.

AI agents are now writing code, deploying systems, and making real production decisions. But when things go wrong, model drift, data leaks, or outages, most organisations can’t answer three simple questions: Did we catch it? Who’s responsible? How do we prevent it next time?

This panel brings together engineering, platform, and security leaders tackling a new reality: AI is breaking both observability and accountability.

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We’ll cover:

• Why traditional monitoring misses AI failures  and what’s replacing it
• Where accountability sits when AI makes decisions
• How to design audit trails, guardrails, and escalation paths for AI systems
• Lessons from early AI incidents what was caught, what wasn’t, and who took responsibility
• What regulators, boards, and customers now expect

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Kubernetes promised scalability and flexibility. In reality, many teams ended up with cluster sprawl, inconsistent setups, and rising operational overhead.

At the same time, AI workloads are adding new pressure and not every use case actually needs Kubernetes.

This session explores how control was restored through platform engineering, standardisation, and automation and when it made sense to simplify instead.

• How to reduce cluster sprawl and restore consistency
• When Kubernetes isn’t the right choice and when to simplify
• How to balance flexibility, cost, and control as workloads scale

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In this innovative session, attendees will be faced with a series of scenarios that they may face in their roles. Attendees will discuss the possible courses of action with their peers to consider the ramifications of each option before logging their own course of action.

Results will be tallied and analysed by our session facilitator and results will impact the way the group moves through the activity.

Will we collectively choose the right course of action?

Most companies are bolting AI onto businesses designed for a pre-AI world and struggling to prove the return. Joe is doing something harder: rebuilding Finder as a 100% AI-native business, where AI is the foundation, not a feature, and where ROI is measured at the level of the business model, not the pilot project.

In this keynote, Joe shares what AI-native really means in practice: the strategy, the platform decisions, the organisational shifts, and how to build a credible ROI case when AI moves from a productivity tool to the core operating model.

A frank, practitioner's view from a CTO making the bet in real time.

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• What "AI-native" really means and how it differs from "AI-enabled"
• The platform, team, and value-chain shifts that make the transition real
• How to measure and defend ROI when AI becomes the business, not a line item

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AI is accelerating software development, increasing both the speed and volume of code being produced. For security teams, this means more findings to assess, prioritise and remediate - without slowing developers down.

This session demonstrates how Checkmarx Developer Assist brings security directly into the developer workflow to identify, understand and fix vulnerabilities as they code, while AI-powered Triage and Remediation helps security teams quickly understand and prioritise what matters.

The result: less manual effort, faster remediation, and security that keeps pace with modern development.

Platform engineering has moved from a trend to a strategic bet. The organisations winning with AI, managing cloud spend, and scaling delivery all have one thing in common: they've treated their internal platform as a product. 

This panel brings together engineering leaders who've built platforms their teams actually use, and made the business case to fund them.

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What we’ll cover:

• Why platform-first is no longer optional for any engineering organisation above a certain size
• How to structure, staff, and fund platform teams as a strategic capability
• The hard parts: adoption, governance, and avoiding internal tooling sprawl
• Where data, environments, and developer experience intersect in a modern platform

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Select a topic of discussion and engage in an interactive roundtable discussion with a group of your like-minded peers.

Put your knowledge to the test in this fast-paced quiz covering real-world trivia, key concepts, and emerging trends. Compete for bragging rights - and a travel voucher - as the top scorer takes the crown.

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BlueApp, Blue Pearl’s high-volume consultant-matching platform, faced growing challenges on Java 11, including deprecated APIs, dependency incompatibilities, and increasing maintenance overhead. Upgrading to Java 25 (LTS) was critical for security, compliance, and performance but traditionally would have required weeks of engineering effort. In this session, we’ll walk through how we completed the full uplift in just 3 days with zero post-deployment defects using IBM Bob. You’ll gain practical insights in improving developer productivity and creating a repeatable framework for upgrading legacy applications at scale.

Attendees will learn:

• How to accelerate Java modernisation with AI-assisted upgrades, refactoring, and dependency management
• How to reduce modernisation risk using system insights, governance workflows, and integrated security scanning
• How to modernise CI/CD, testing, and deployment workflows while improving delivery speed, cost, and stability

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When your software runs on a medical device keeping someone breathing, "moving fast and breaking things" isn't an option, but moving slowly isn't either. This session looks at how to build engineering teams and delivery pipelines that are both fast and safe, and where AI genuinely helps versus where it quietly adds risk in high-stakes, regulated environments.

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We'll cover:

• Building CI/CD and test strategy that holds up under regulatory scrutiny without grinding delivery to a halt
• Scaling embedded and device software teams through multiple product cycles, capability, structure, and quality as you grow
• Where AI is earning its place in safety-critical delivery, and where the hype doesn't survive contact with real accountability

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Large organisations are cutting roles. Graduate pipelines are shrinking. AI is absorbing work that used to take teams of engineers. This is the uncomfortable conversation the industry isn't having openly yet. 

This interactive think tank puts these issues to the room. Participants vote live on the scenarios shaping the next five years of Australian engineering, explore the results together, and vote again as perspectives shift.

• What roles disappear, what roles emerge, and what stays the same?
• How should we train, hire, and develop engineers in an AI-native world?
• Is the junior engineer dead? What's the entry pathway now?
• What's the Australian engineering profession's responsibility to its own people?
• Where does this leave engineering leadership in 2030?

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Unwind with your peers for a couple of drinks on us!