Name: AI Coding Summit
Start: 2025-10-23T14:00:00.000Z
End: 2025-10-24T17:00:00.000Z

Varun

Petra vos

Stefan Wille

Daria Shabala

Joran Quinten

Kristiyan Velkov

Thibault Van den Broecke

John Adib

Pascal Moser

Nick Taylor

Trust Jamin

Erik Hanchett

Julián Duque

Dimitris Kiriakakis

Seungho Park

Hunor Márton Borbély

Innocencia Ndembera

David Pugh

Robert

Thijs Broerse

Jennifer

Bruno da Hora

Ian Sutherland

Thibo De Reu

Azadeh Farshidi

Emerson Marini

Diogo Pedro

Nick M

Natalia Sabadysh

Adam Macejak

Norm Crandall

Lee Burton

khouchane Mahmoud

Filip Verswijver

Esta masterclass muestra cómo integrar flujos de trabajo agénticos en CI/CD moderno para que el código sea más seguro y rápido en producción: revisión de código LLM como primer paso, sugerencias de remediación automatizadas y orquestación estilo enjambre para seguimientos. Verás lo que realmente funciona hoy con Copilot Code Review, CodeQL Autofix, Semgrep Assistant y marcos de agentes como LangGraph o AutoGen, además de cómo controlar las fusiones con evaluaciones automatizadas y ligeras.

CI/CD Agéntico: de Pull Request a Producción sin los Pequeños Inconvenientes

Discussing the evolution of CI CD pipelines to true autonomy, focusing on intelligent workflows, automation challenges, and paper cuts affecting developer productivity. Addressing challenges in software development, including slow reviews, security bottlenecks, complex debugging, and inefficient testing. Transitioning to autonomous decision-making with agentic systems and introducing Continuous Agentic and Continuous Deployment (CCD) as a deeper architecture change. Exploring agentic workflows with autonomous systems that make intelligent decisions and anticipate problems. Transitioning from an assembly line to a collaborative system with an AI pit crew for autonomous CACD workflows. Intelligent triaging filters false positives, ensuring developers focus on real issues. Orchestration of specialized agents into a cohesive team through an orchestrator agent. Vision of self-adaptive pipeline learning, adjusting workflows for optimal balance, elevating roles in agentic systems.

This talk shows how to wire agentic workflows into modern CI/CD so code gets safer and faster to prod: LLM code review as a first pass, automated remediation suggestions, and swarm-style orchestration for follow-ups. You’ll see what actually works today with Copilot Code Review, CodeQL Autofix, Semgrep Assistant, and agent frameworks like LangGraph or AutoGen, plus how to gate merges with lightweight, automated evaluations.

Agentic CI/CD: from Pull Request to Production without the Paper Cuts

Chaitanya Rahalkar

Sub Agent Context Sharing: Cómo habilitar subagentes efectivos para codificación

Cloud Code introduced sub-agent feature for better performance. Understanding the sub-agent concept and its role. Challenges of sub-agents in direct implementation. Parent agents' limited visibility. Efficient context management with MD files for sub-agents' tasks and reports boosting Cloud Code performance. Guidelines for creating useful sub-agents with specific MCP tools. Utilizing TweakScene MCP for scene design reference and defining rules for sub-agent tasks. Setting goals, output format, and rules for sub-agents. Building a chat GPT replica with chat.cn UI and Versatile AIS. Integration with VerCell AI SDK for seamless implementation.

Sub Agent Context Sharing: How to Enable Effective Sub Agents for Coding

 Zhou Jason

Los asistentes de codificación de IA son excelentes para completar pequeñas tareas o características. Sin embargo, ¿qué haces cuando trabajas con bases de código más complejas y necesitas construir características detalladas que requieren planificación previa?Esta charla explora el desarrollo impulsado por especificaciones como una solución a este problema. Te mostraré cómo los asistentes de codificación de IA modernos (como Kiro) pueden ayudar a descomponer tareas complejas en tres fases distintas. Veremos las compensaciones del mundo real de este enfoque, y lo más importante, cómo puedes usarlo en tus propios proyectos de inmediato.

Using Spec-Driven Development for Agentic Coding

Eric Hanton introduces spec-driven development, contrasting traditional waterfall methods with agile approaches. Qiro, an AI IDE coding assistant, evolved from AWS history and garnered positive feedback. QIRO facilitates in-depth feature planning with detailed requirements and design documents. Spec-driven development in QIRO emphasizes checking and rechecking, allowing flexibility in task management. MCP integration enhances task flexibility and information retrieval. Utilizing MCP servers in spec-driven development improves smartness and information integration. Agent steering files enhance JIRA context and accuracy in information retrieval.

AI coding assistants are great at completing small tasks or features. However, what do you do when you are working with more complex code bases, and you need to build in-depth features that need upfront planning?This talk explores spec-driven development as a solution to this problem. I'll show you how modern AI coding assistants (like Kiro) can help break down complex tasks into three distinct phases. We'll look at the real-world tradeoffs of this approach, and most importantly and how you can use it in your own projects right away.

¿Qué pasaría si dejaras que la IA codificara junto a tus desarrolladores? ¿Aceleraría las cosas o las ralentizaría? En Modus Create, realizamos un experimento interno de 3 meses para averiguarlo. Se asignaron dos equipos para construir la misma aplicación: un equipo tenía acceso completo a agentes de codificación de IA como GitHub Copilot y Cursor, mientras que el otro trabajaba sin ninguna asistencia de IA. ¿Los resultados? Sorprendentes, matizados y extremadamente relevantes para cualquier empresa que esté pensando en incorporar herramientas de IA en su proceso de entrega de software. En esta sesión, compartiremos métricas de productividad, ideas sobre la calidad del código, el impacto cultural y lo que aprendimos sobre emparejar desarrolladores con IA. Te irás con una perspectiva del mundo real y una guía práctica sobre cómo escalar el desarrollo potenciado por IA en tu organización de ingeniería, sin el bombo.

Vibe Coding a Escala Empresarial: ¿Qué Sucede Cuando la IA se Une a Tu Equipo de Desarrollo?

Wesley discusses the potential benefits of integrating AI tools in software development, demonstrating a 45% productivity improvement with AI usage. The importance of engineers providing clear direction for AI tasks and overseeing AI-generated code is highlighted. Engineers are reminded that AI is a complement, not a replacement, for human expertise, and that effective AI usage involves clear communication and human oversight. Contextual guidance and effective task management strategies for AI interaction are emphasized.

What if you let AI code alongside your devs—would it speed things up or slow them down? At Modus Create, we ran a 3-month internal experiment to find out. Two squads were tasked with building the same app: one squad had full access to AI coding agents like GitHub Copilot and Cursor, while the other worked with zero AI assistance. The results? Surprising, nuanced, and extremely relevant for any enterprise thinking about bringing AI tools into their software delivery process. In this session, we'll share productivity metrics, insights on code quality, cultural impact, and what we learned about pairing developers with AI. You'll walk away with a real-world perspective and a practical guide on how to scale AI-powered development across your engineering org—without the hype.

Vibe Coding at Enterprise Scale: What Happens When AI Joins Your Dev Team

Wesley Fuchter

A medida que la IA se adentra en el corazón del desarrollo de software, el papel de los desarrolladores está cambiando de escribir código a dar forma a sistemas. Esta charla explora cómo el principio clásico de Responsabilidad Única evoluciona en un mundo AI-first, por qué es la clave para un diseño escalable y cómo transforma a cada desarrollador en un arquitecto.

Arquitectura AI-First: Por Qué la Responsabilidad Única Importa Más Que Nunca

AI accelerating coding; principles crucial. Embrace change with structure for stable systems. AI as a new team player in system building. Single responsibility principle key for AI integration. Clear architecture aids AI recognition and prevents chaos. Enforcing rules and tests for code integrity in the AI era. Documentation provides context for humans and AI. Importance of investing in GuardDays for improved AI understanding.

As AI moves into the heart of software development, the role of developers is shifting from writing code to shaping systems. This talk explores how the classic principle of Single Responsibility evolves in an AI-first world, why it’s the key to scalable design, and how it transforms every developer into an architect.

AI-First Architecture: Why Single Responsibility Matters More Than Ever

¿Qué sucede cuando la inteligencia artificial generativa no es solo una palabra de moda, sino un verdadero compañero de equipo en ingeniería? Durante el último año, he liderado a mi equipo de ingeniería a través de un cambio radical, integrando herramientas GenAI y construyendo nuestras propias soluciones personalizadas impulsadas por IA. ¿El resultado? Hemos multiplicado la productividad, reducido los tiempos de entrega y desbloqueado nuevos flujos de trabajo creativos que antes eran imposibles.En esta charla, compartiré ejemplos del mundo real de cómo usamos herramientas como Cursor y Devin, así como integraciones de IA personalizadas, para acelerar la codificación, automatizar pruebas, mejorar la documentación e incluso resolver errores complejos en minutos. Desglosaré los cambios de proceso que hicieron exitosa la adopción de IA: cómo reestructuramos nuestros flujos de trabajo, incorporamos al equipo y aprendimos a "programar en pareja" con IA de manera efectiva. Espere lecciones prácticas y conocimientos probados en batalla: qué funcionó brillantemente, qué falló (y por qué), y los cambios culturales necesarios para hacer de GenAI un multiplicador de fuerza en lugar de solo otra herramienta llamativa. Ya sea que seas un líder de equipo, ingeniero o constructor de productos, te irás con ideas accionables para potenciar a tu propio equipo usando GenAI, sin perder el toque humano que impulsa la gran ingeniería.

10x Ingeniería: Cómo las Herramientas GenAI Transformaron la Productividad de Mi Equipo

Welcome to my talk on transforming team productivity with JNI technologies. Meet Adir, an AI enthusiast. I'm Dennis, the director of AI engineering at Elementor. I'll share how AI has revolutionized my team's work. The turning point? Cursor. Cursor's organic adoption revolutionizes developer workflows. Streamlining PR reviews with Cursor automation. Simplifying GCP Secrets Manager updates with Cursor. Efficiency boost with PR automation using Cursor. Simplifying GCP Secrets Manager updates effortlessly. Empowering engineers with AI hacks for enhanced productivity. AI-driven efficiency boost through Devin's workflow integration and onboarding transformation with Rovo. AI-driven transformation of onboarding process with Devin and Rovo for efficient code understanding and task delegation. Improving code efficiency by formalizing cursor rules on individual, team, and company levels for consistent outcomes and implementing AI-powered code generation for enhanced quality and productivity. Enhancing Workflow Efficiency with Zapier AI Integrations and Automated Newsletter Creation for Streamlined Communication and Enhanced Productivity. Unlocking Insights from User Data with AI-Categorized Feedback for Product Improvement and Quality Enhancement. AI-Powered Insights, Fixes, and Adoption Strategies for Team Efficiency and Quality Enhancement. AI Adoption Challenges and Strategies: Start Small, Gain Trust, Invest in Documentation. Over-Reliance on AI and Maintaining Engineering Mindset

What happens when generative AI isn't just a buzzword but a real engineering teammate? Over the past year, I've led my engineering team through a radical shift-integrating GenAI tools and building our own custom AI-powered solutions. The result? We've multiplied productivity, cut delivery times, and unlocked new creative workflows that were impossible before.In this talk, I'll share real-world examples of how we use tools like Cursor and Devin, as well as custom-built AI integrations, to speed up coding, automate testing, improve documentation, and even resolve complex bugs in minutes. I'll break down the process changes that made AI adoption successful-how we rewired our workflows, onboarded the team, and learned to "pair program" with AI effectively. Expect practical lessons and battle-tested insights: what worked brilliantly, what failed (and why), and the cultural shifts needed to make GenAI a force multiplier instead of just another shiny tool. Whether you're a team lead, engineer, or product builder, you'll walk away with actionable ideas to supercharge your own team using GenAI-without losing the human touch that drives great engineering.

10x Engineering: How GenAI Tools Transformed My Team's Productivity 

Dennis Nerush

En esta sesión, exploraré cómo las herramientas impulsadas por AI están transformando la refactorización a gran escala y las migraciones de bases de código, haciendo que estas tareas complejas sean más rápidas y eficientes. Al aprovechar herramientas como Large Language Models (LLMs), análisis estático y frameworks de refactorización, podemos automatizar transformaciones de código repetitivas, acelerar rutas de migración y reducir el error humano. Compartiré ejemplos prácticos de cómo podemos migrar sistemas heredados a frameworks modernos, descomponer arquitecturas monolíticas en estructuras basadas en servicios y automatizar cambios de código a gran escala en miles de archivos.Demostraré cómo herramientas como OpenAI Codex, GitHub Copilot y modelos ajustados para transformaciones específicas de dominio pueden asistir en estos procesos, mientras se integran con herramientas de migración tradicionales y sistemas CI/CD. También cubriré la importancia de la supervisión del desarrollador, destacando lecciones aprendidas de implementaciones en producción del mundo real y cómo equilibrar la automatización con revisiones manuales.Al final de la charla, quiero que los asistentes se vayan con una comprensión clara de cómo usar AI para mejorar sus flujos de trabajo de refactorización y migración, asegurando que puedan manejar transformaciones a gran escala mientras mantienen la calidad del código, la consistencia y la gobernanza.

Refactorización y Migraciones con AI: Transformación de Código más Inteligente a Escala

AI's role in refactoring and migrations. Tools like GitHub, Copilot, OpenAI Codecs improve transformations. Challenges in large-scale projects and AI's impact on modernizing legacy systems. Google and Reddit's success with AI-powered migrations. Framework migrations with AI: pattern recognition, syntax transformation, and seamless transitions. Successful AI refactoring integrates into CI/CD pipelines, providing critical capabilities like automatic quality gauges and continuous improvement through small changes. Challenges and mitigation strategies in AI refactoring, focusing on testing, human oversight, and organizational obstacles.

In this session, I’ll explore how AI-powered tooling is transforming large-scale refactoring and codebase migrations, making these complex tasks faster and more efficient. By leveraging tools like Large Language Models (LLMs), static analysis, and refactoring frameworks, we can automate repetitive code transformations, accelerate migration paths, and reduce human error. I’ll share practical examples of how we can migrate legacy systems to modern frameworks, break monolithic architectures into service-based structures, and automate large-scale code changes across thousands of files. I’ll demonstrate how tools like OpenAI Codex, GitHub Copilot, and fine-tuned models for domain-specific transformations can assist in these processes, while still integrating with traditional migration tools and CI/CD systems. I’ll also cover the importance of developer oversight, highlighting lessons learned from real-world production rollouts and how to balance automation with manual reviews.By the end of the talk, I want attendees to walk away with a clear understanding of how to use AI to enhance their refactoring and migration workflows, ensuring they can handle large-scale transformations while maintaining code quality, consistency, and governance.

Refactoring & Migrations with AI: Smarter Code Transformation at Scale

Nikolay Gushchin

Descubre cómo usar Docker para empaquetar, enviar y ejecutar herramientas de IA construidas sobre el Model Context Protocol (MCP). Aprende a crear servidores y clientes MCP reproducibles, integrarlos en flujos de trabajo de desarrolladores y escalarlos en producción, desde el desarrollo local hasta pipelines CI/CD agénticos.

Docker MCP: Empaquetado y Despliegue de Herramientas de IA con Model Context Protocol

Kristiyan Velkov discusses Docker, MCP, AI tools. TypeScript utility types book creation. Anthropic's open-source protocol for AI communication. Docker introduces MCP toolkit for AI server connection. Configuring servers and connecting to clients securely in Docker. Adding and connecting MCP server and client in Cursor. Creating and showcasing MCP Docker repository. Deploying servers with Docker, tagging and deploying Docker images for MCP. Utilizing Docker server locally and with Docker, exploring AI enhancement. Introduction to Gordon AI tool for Docker enhancement and GitHub sharing.

Discover how to use Docker to package, ship, and run AI tools built on the Model Context Protocol (MCP). Learn how to create reproducible MCP servers and clients, integrate them into developer workflows, and scale them in production — from local dev to agentic CI/CD pipelines.

Docker MCP: Packaging & Deploying AI Tools with Model Context Protocol

Todos hemos oído hablar de la ingeniería de prompts. Pero ahora, con la aparición de la ingeniería de contexto, puede que te estés rascando la cabeza sobre cuál es la diferencia. La realidad es que para construir agentes confiables, necesitamos usar todas las herramientas en nuestra caja de herramientas de contexto para asegurar que proporcionen respuestas precisas en el formato correcto.Adentrémonos en el mundo de la ingeniería de contexto y comprendamos los elementos del contexto que podemos ajustar y modificar para construir agentes de IA más confiables y sistemas asociados.

Creación de Agentes Inteligentes con Ingeniería de Contexto

Carly emphasizes the importance of context engineering for agents, addressing challenges like model cutoff dates and biases in data. The discussion delves into user prompts, exploring zero-shot prompts and the role of user input in LLMs. Custom instructions and system prompts are highlighted in prompt engineering. The talk covers system prompts, RAG for document retrieval, and vector search for semantic relevance. Effective tool utilization, structured output, and the significance of context in tool building are also key points.

We've all heard about prompt engineering. But now with the emergence of context engineering you may be scratching your head about what the difference is. The reality is that to build reliable agents, we need to use all tools in our context toolbox to ensure they provide accurate responses in the correct format.Let's dive into the world of context engineering, and understand the elements of context that we can tune and tweak to build more reliable AI agents and associated systems.

Crafting Intelligent Agents with Context Engineering

Carly Richmond

En los últimos años, ha habido un cambio notable en cómo los desarrolladores interactúan con la documentación.Antes, los desarrolladores a menudo recurrían a Docs y Stack Overflow para obtener respuestas sobre herramientas, pero ese ya no es el caso.Hoy en día, los API Docs, tutoriales y especificaciones no son solo para humanos, también se utilizan para entrenar agentes LLM para responder preguntas sobre tus herramientas. Esta charla explorará cómo documentar API y tutoriales de una manera que sirva tanto a desarrolladores como a LLMs, haciendo tu documentación más accesible, práctica y a prueba de futuro.A través de estudios de caso y conocimientos prácticos, cubriremos cómo las empresas se están ajustando a este nuevo cambio dinámico y el auge de GEO (Generative Engine Optimization).

Cerrando la Brecha: Documentando APIs para Humanos y Agentes LLM

Discussing the importance of writing for humans and LLMs, optimizing content for AI and LLM accessibility, and enhancing signups through AI referrals. Exploring methods to structure content for LLMs, optimize documentation for both human and LLM accessibility, and enhance AI-friendly documentation. Utilizing MCP server for better content structuring, adopting GEO for future AI tool optimization, and targeting audiences with content optimization by companies like MongoDB and Stripe.

Over the past few years, there has been a notable shift in how developers interact with documentation.Before now, developers often turned to Docs and Stack Overflow for answers about tools, but that is no longer the case.Today, API Docs, tutorials, and specifications are not just for humans—they are also utilized to train LLM agents to answer questions about your tools. This talk will explore how to document API and tutorials in a way that serves both developers and LLMs, making your documentation more accessible, actionable, and future-proof.Through case studies and practical insights, we'll cover how companies are adjusting to this new dynamic shift and the rise of GEO (Generative Engine Optimization).

Bridging the Gap: Documenting APIs for Humans and LLM Agents

Los agentes de IA ya no son experimentales. Los desarrolladores ya los están utilizando para consultar APIs, modificar contenido y encadenar servicios utilizando protocolos emergentes como MCP (Model Context Protocol). La última especificación de MCP introduce autenticación moderna OAuth 2.1 y soporte para Resource Indicators (RFC 8707), fortaleciendo la identidad en sistemas basados en agentes. Pero la autenticación por sí sola no garantiza el control. Una vez que un agente ha iniciado sesión, ¿cómo se gobierna lo que se le permite hacer? Sin controles de autorización adecuados, los agentes pueden acceder a muchos más recursos de los que necesitan, creando riesgos de seguridad significativos. Esta masterclass explora cómo aplicar los principios de Zero Trust a los flujos de trabajo de agentes combinando protocolos de identidad abiertos con infraestructura consciente de políticas. Verás una demostración de un cliente MCP interactuando con un servidor MCP asegurado detrás de Pomerium, un proxy de identidad consciente de código abierto que aporta control de acceso detallado a las interacciones de agentes. Más allá de la autenticación básica, Pomerium evalúa políticas por solicitud basadas en identidad, ruta y contexto, y puede auditar y bloquear llamadas de herramientas específicas dentro del protocolo MCP. Incluso puede gestionar flujos OAuth hacia herramientas upstream como Notion o Reddit, para que los agentes nunca manejen tokens de acceso en bruto. Lo que aprenderás: - Por qué OAuth es necesario pero no suficiente para la seguridad de agentes - Cómo aplicar Zero Trust a herramientas de desarrollo y flujos de trabajo de IA - Un ejemplo práctico de asegurar servidores MCP con infraestructura de código abierto A medida que los agentes de IA se convierten en parte de los flujos de trabajo de desarrolladores del mundo real, los estándares abiertos y las configuraciones seguras son clave para construir confianza sin añadir fricción. Estos patrones de seguridad se aplican más allá de los sistemas de IA a cualquier herramienta automatizada que necesite acceso controlado a APIs y servicios.

Acceso Agente: OAuth Te Deja Entrar. Zero Trust Te Mantiene Seguro

Welcome to the talk on Agentic Access, covering MCP, Zero Trust Security, and Identity-Aware Proxy. MCP offers a standardized interface for LM agents, while Zero Trust ensures continuous authentication and context-aware enforcement. The Identity-Aware Proxy enforces authentication, authorization, and context-aware policies. Policy Engine checks ensure secure access to internal resources through authentication and proxy validation. Transitioning from human to agentic access requires adherence to MCP security best practices like using proxies, validating tokens, and auditing access. VPNs, although common, pose security and access control challenges, prompting the need for a secure tool exposure approach. Placing MCP servers behind a proxy ensures secure access. VPNs pose limitations for securing MCP servers, especially with hosted services like Cloud or ChatGPT. Integration of OAuth into MCP systems enhances security but doesn't enforce device trust or session-based policies. Zero Trust principles guide access control and evaluation of requests. OAuth scopes in OAuth are often coarse-grained, limiting fine-grained access control. GitHub MCP server demo illustrates challenges with coarse-grained scopes. Centralized logs provide auditing for better tracking and control. MCP servers with Pumarium simplify OAuth implementation. Identity-aware proxy manages OAuth flow and policy enforcement. Demo shows secure data handling through an MCP server with auditing. MCP client simplifies OAuth token management. Demo showcases successful repo operations with real-time auditing. Successful operations except merge pull request. Resources, MCP spec docs, Zero Trust Security insights, and contact information.

AI agents are no longer experimental. Developers are already using them to query APIs, modify content, and chain services using emerging protocols like MCP (Model Context Protocol). The latest MCP specification introduces modern OAuth 2.1 authentication and support for Resource Indicators (RFC 8707), strengthening identity in agent-based systems. But authentication alone does not guarantee control. Once an agent is logged in, how do you govern what it is allowed to do? Without proper authorization controls, agents can access far more resources than they need, creating significant security risks. This talk explores how to apply Zero Trust principles to agent workflows by combining open identity protocols with policy-aware infrastructure. You will see a demo of an MCP client interacting with a secured MCP server behind Pomerium, an open source identity-aware proxy that brings fine-grained access control to agent interactions. Beyond basic authentication, Pomerium evaluates per-request policies based on identity, route, and context, and can audit and block specific tool calls within the MCP protocol. It can even manage OAuth flows to upstream tools like Notion or Reddit, so agents never handle raw access tokens. What you will learn: - Why OAuth is necessary but not sufficient for agent security - How to apply Zero Trust to developer tools and AI workflows - A practical example of securing MCP servers with open source infrastructure As AI agents become part of real-world developer workflows, open standards and secure defaults are key to building trust without adding friction. These security patterns apply beyond just AI systems to any automated tooling that needs controlled access to APIs and services.

Agentic Access: OAuth Gets You In. Zero Trust Keeps You Safe

Join a focused 2-hour session covering MCP's purpose, architecture, hands-on server implementation, and future directions. Designed for developers and system architects aiming to integrate contextual data with ML models effectively. Agenda:- Introduction &amp; Why MCP? Key challenges MCP solves and core benefits.- Architecture Deep Dive: components, interactions, scalability principles. - Building Your Own MCP Server: guided walkthrough with code snippets and best practices; live demo or code review.- Future of MCP Developments: potential enhancements, emerging trends, real-world scenarios. Key Takeaways:- Clear understanding of MCP's rationale.- Insight into design patterns and scaling considerations.- Practical steps to implement a prototype server.- Awareness of upcoming trends and how to apply MCP in projects.

Model Context Protocol (MCP) Deep Dive: 2-Hour Interactive Workshop

Stepan Suvorov

This workshop introduces Context Engineering - a simple yet structured approach to getting consistent, high-quality results from AI coding assistants. You'll learn how to move beyond basic prompting to create comprehensive workflows to give your AI coding assistants all the architecture, best practices, project rules, code examples, and validation processes it needs to get the job done effectively. Through hands-on exercises with Claude Code, we'll demonstrate how proper Context Engineering unlocks a whole new level of agentic engineering while teaching practical techniques, templates, and best practices that help AI assistants understand your project standards and deliver production-ready code consistently.

Advanced Claude Code Techniques: Agentic Engineering With Context Driven Development

Cole Medin

In this workshop, I’ll guide participants through a process for building full-stack applications using Cursor. We’ll cover setting up Cursor workflows, including rules, commands, and custom modes, to streamline development. We will use Cursor to draft PRDs, database schema, etc and a simple SOW. We’ll turn notes into actionable checklists to guide the build, connecting the front end, API, and database.Additionally, we’ll create workflows for debugging, testing, reviewing, and fixing the app while reducing hallucinations with precise prompts. The session will also include a security review workflow and techniques for applying prompts to achieve a modern UI design. Attendees will leave with a functioning app on their machine and a reusable development process.

How to Build Full-Stack Apps Using Cursor

 Shrey Shah

Workshop Objective:Show how teams can go from AI-generated UI to live production components in under an hour.Workshop Agenda:Part 1: Kickoff &amp; Setup <ul><li>- Quick intro to the Pluggable AI method</li><li>- Walk through a real example already running in production</li><li>- Align on the selected AI/visual tool (Claude, Gemini, etc.)</li><li>- Choose 1–2 UI components to work with</li><li>- Define what’s needed to make the component pluggable</li><li>- Select the host app </li></ul>Part 2: Generate the Component <ul><li>- Use the selected AI tool to generate the UI component</li><li>- Myop team helps ensure it fits integration guidelines (structure, format)</li></ul>Part 3: Integration Preps<ul><li>- Identifying where the component connects (logic hook)</li><li>-Review generated component and prepare for connection</li></ul>Part 4: Plug It Live <ul><li>- Plug the component into the live app using Myop's open source</li><li>- Optional: test versioning, A/B testing, segmentation, gradual rollout</li></ul>Part 5: Wrap-Up<ul><li>- Recap effort vs. outcome</li><li>- Q&amp;A  </li></ul>

From Prompt to Production: Build and Plug AI UI Into a Live App

Keren Fanan

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