In the new AI-enabled coding environment, vibe-coding is both an enticing and often-misunderstood reality.  Let's spend some time with startup experts who have evaluated hundreds of MVPs and coders who can help sort fact from fiction, and position vibe-coding to be a resource for both technical and non-technical founders.

The explosion in agentic AI frameworks like LangGraph, AutoGen, and CrewAI has unlocked incredible possibilities for automating complex tasks. However, this revolution has been overwhelmingly centered in the Python ecosystem, leaving a massive community of skilled mobile, backend, and JVM developers on the side-lines or forcing them into a language they don't use daily. This session introduces Koog by JetBrains, a powerful framework that brings agentic AI development to Kotlin. We will explore how developers can build sophisticated, multi-agent solutions without writing a single line of Python. We'll directly compare Koog's architecture and capabilities for creating agentic workflows against its popular Python counterparts, highlighting the similarities in power and the differences in approach. The true power of this stack lies in its deployability. We will demonstrate how, by leveraging Kotlin Multiplatform (KMP), you can write your AI agent logic once and seamlessly integrate it anywhere: as a backend service with Ktor, directly within an Android app, or even in the browser with Kotlin/JS. Attendees will leave this talk with a clear understanding of how to build their own AI agents using the tools they already know, ready to integrate intelligent solutions across their entire stack.

Agentic AI is racing into prod—often without a safety envelope. This session shows how to ship micro-LLMs and MCP-based agents that are actually verifiable: zero-trust boundaries, policy-as-code guardrails, signed tool invocations, prompt-injection defenses, evaluation harnesses, and audit-ready provenance. We’ll dissect the top failure modes (unsafe tool use, data exfil via prompts, RAG leakage, jailbreaks) and map them to concrete countermeasures: least-privilege tool adapters, contract tests for tools, adversarial evals in CI, and immutable event logs for post-mortems. Expect a gritty, pattern-driven discussion: reference architectures, checklists, and “red team to fix” playbooks that teams can lift on Monday. If you care about regulated stacks or multi-tenant safety, this panel is your north star for provable agent safety.

Abstract: Cloud costs are spiraling out of control for most organizations, with poor visibility, inconsistent tagging, and manual optimization processes leaving money on the table every month. This talk introduces a new paradigm: autonomous AI agents that continuously monitor, analyze, and optimize your cloud spending

What You'll Learn: Discover how to build and deploy AI agents that transform cloud cost management from a reactive, manual process into a proactive, intelligent system. We'll explore real-world implementations of agents that automatically unify data across multiple cloud providers, standardize resource tagging, create cost-to-business mappings, and execute optimization recommendations—all while learning from your organization's unique patterns and constraints.

Key Takeaways: Architecture patterns for building cloud cost management AI agents. Data unification strategies across AWS, Azure, GCP, and hybrid environments Automated tagging and resource categorization using ML techniques. Real-time rightsizing algorithms that balance performance and cost. Implementation of guardrails and approval workflows for autonomous actions. ROI metrics and success stories from production deployments

Target Audience: DevOps engineers, cloud architects, platform engineers, and engineering leaders looking to reduce cloud costs while maintaining system reliability and developer productivity.

We turned academic search into a conversation by designing, building, and deploying a production GenAI Research Assistant that lets students, professors, and researchers ask natural questions about the papers they find and receive grounded answers, summaries, and related content. This talk walks through the full lifecycle from domain grounding and corpus preparation to retrieval augmented generation, LLM selection and adaptation, and prompt plus context strategies that reduce hallucinations while preserving a great user experience. We will cover the architecture behind scalable pipelines, hybrid use of embeddings and multiple models, why and when we combine providers, and the practical trade-offs among cost, latency, and accuracy. Because trust is essential in scholarly workflows, we will dive into privacy and governance for sensitive content, human in the loop review, and the telemetry that drives iterative quality gains and adoption. Finally, we will share our automated evaluation framework that blends overlap and semantic metrics with groundedness checks and rubric based judging, including Databricks MLFlow, and LLM-as-a-judge, to ensure faithful, high quality results. Attendees will leave with concrete patterns for designing, evaluating, and operating GenAI applications at scale in real production environments.

Tired of AI agent theory? Ready to actually build something? In this interactive session, we'll forget the slides and jump straight into the code. For one hour, you're not an attendee, you're an AI architect. I'll guide you through a rapid prototyping sprint using the powerful MCP framework and ADK Toolkit. You will learn by doing, and you will succeed. What You'll Walk Away With: A repeatable blueprint for prototyping AI agents. A working agent, built by you, ready to be customized. The confidence to turn your own AI ideas into reality. This isn't a lecture. It's a launchpad. Let's build.

Prerequisites: None! This session is beginner-friendly. All you need to bring is your curiosity.