Roca-I

Welcome speech by Michal and Lydia about the idea behind AWS Community Day Slovakia.

The most exciting part of your job, solving hard problems, is being reclaimed by AI agents that work 24 hours a day without fatigue.

Routine coding is no longer where your real value lives. The developers who lead this era move up the stack, think architecturally, and orchestrate intelligent systems.

This session shows you how AI is reshaping the development lifecycle, what the Renaissance Developer mindset looks like in practice,and how to position yourself as the architect of AI-driven solutions, not a passenger.

As organizations scale AI agent development, robust enterprise architecture patterns become essential. This session explores proven best practices for building production-ready AI agents using Amazon Bedrock AgentCore and its core components.

Learn how to work backwards from business problems and start with focused use cases. Discover practical strategies for secure runtime deployment, standardized tool integration, evaluation frameworks, and end-to-end monitoring.

Explore agents collaboration, shared memory, identity-based access controls, and integrated observability. Walk away with actionable strategies for scaling AI agents across your organization through proper governance and team structures.

Karpenter has changed how we manage Kubernetes nodes on EKS — but deploying it is just the beginning. The real savings come from how you architect your NodePools, split workloads between Spot and On-Demand instances, and configure consolidation policies.

In this session, we’ll explore Karpenter’s configuration capabilities in depth: NodePool strategies (e.g., 50/50 Spot/On-Demand split), instance type diversification, disruption budgets, and consolidation tuning.

A significant portion of the talk is a live demo where we’ll show how Karpenter works during scaling events (scaling to 3,000 pods), observe consolidation in real time, and demonstrate splitting workloads 50/50 across On-Demand and Spot instances.

You’ll leave with concrete configurations ready to deploy in your own clusters.

AI agents are easy to prototype. Running them securely, observably, and at scale? That’s where things get complicated.

In this session, we move beyond agent frameworks and focus on what it takes to operate AI agents in production using Amazon Bedrock AgentCore. While frameworks like Strands provide modular, composable agent architectures, AgentCore delivers the managed runtime, identity, memory, gateway, and observability services needed for real-world deployments.

We’ll explore:

• Secure agent runtimes with session isolation and VPC support
• Built-in identity and fine-grained authorization
• Memory services for short- and long-term context
• Gateway integration for MCP-compatible tools
• Native support for Agent-to-Agent (A2A) communication
• Observability and tracing with CloudWatch and OpenTelemetry

Through practical architecture examples, we’ll show how to take an open-source Strands agent and deploy it on AgentCore — moving from proof-of-concept to production-ready AI systems.

As you move into Senior+, Tech Lead, or Engineering Manager roles, your calendar fills up fast, and your time to actually build steadily shrinks. Yet your responsibility for technical outcomes, architectural decisions, and knowledge sharing doesn’t disappear.

This talk shows how an AI-Native Software Development Lifecycle, powered by Kiro, can become a real leverage for senior engineers and engineering leaders.

Through a hands-on story of vibecoding between meetings, we’ll explore a practical setup designed for the challenges that dominate senior roles: problem framing, architectural reasoning, documentation, and alignment. We’ll build a first MCP server / Kiro power and show how AI can amplify impact - helping us learn faster, reduce cognitive load, and keep architecture and documentation in sync with reality, even when hands-on time is scarce.

While the focus is on Senior+ and Engineering Manager workflows, the tools and patterns shown are just as valuable earlier in a career, offering a powerful boost for learning, exploration, and day-to-day development, and you’ll leave with concrete examples and a repeatable blueprint for applying an AI-native SDLC in your own context.

What happens when photography domain expertise meets AWS service flexibility? This session demonstrates how to bridge specialized knowledge with cloud architecture, creating solutions that AWS services weren’t originally designed for. Using AWS Community Day DACH 2024’s 8,000-photo challenge, we’ll explore how Amazon Rekognition, Bedrock, and Step Functions became an unexpected photography automation pipeline.

Learn the methodology for translating industry-specific workflows into serverless architectures, discovering how AWS services can be creatively combined beyond their intended use cases. Attendees will gain frameworks for identifying automation opportunities in their domains and practical strategies for connecting disparate AWS services to solve problems they weren’t originally built to address.