Name: TechLead Conference 2025: AI in Orgs
Start: 2025-09-18T14:00:00.000Z
End: 2025-09-19T19:00:00.000Z

Tony Alicea

Lera

Seungho Park

Daria Shabala

Thibault Van den Broecke

Cynthia Akiotu

David Pugh

Joanna Otmianowska

Nishant Satyam

Maxim Salnikov

Hunor Márton Borbély

Georgi

João Cidade Silva

Azadeh Farshidi

Rosario Terranova

Robert

Amy Rogers

Antanina Vertsinskaya

Yauheni Niahanau

Brent Kearney

Lukas Havlicek

Boris Karaivanov

Steven Thomas

Sauparna Sarkar

Michal Štrajt

Alejandro Carrillo

Daniel Hrynusiw

Oleksii

Mathieu Habegger

Lee Burton

Cecilia Gutierrez

Paolo Mauri

MAHENDRA KUMAR

Rob Lark

¿Cómo se siente cuando tu equipo se duplica en menos de un año? Como si tu cerebro hubiera sido puesto en una licuadora y alguien siguiera presionando el botón. Me uní a una startup de 7 ingenieros y 40 empleados en agosto, para mayo éramos 14 ingenieros, 100 personas, y yo era un gerente. Estas son mis lecciones.

Leadership Insights From Growing 2X in 8 Months

Product-market fit is like being pushed by the market, needing more. A game analogy illustrates teamwork dynamics in handling tasks. Communication is key in team growth, allowing delegation for collective success. Experienced team members focus on clarity, delegation, and accountability. Prioritize assigning tasks to team members based on expertise. Embrace the role of a side quest specialist for long-term or short projects. Engage in mentorship and explain reasoning to foster team independence and growth. Mentorship occurs organically between tasks, influencing work approaches. Trust your team to make decisions by explaining your rationale. Mentor one skill at a time to ensure effective learning and application. Focus feedback, encourage newbies, address weak points of experts to enhance performance. Focus on feedback for beginners and experts, emphasize outcomes and timely feedback to enhance performance. Realize the importance of quick utility, user stories, and outcome-focused work. Utilize user stories, documentation, and flexible roadmaps for effective planning and decision-making. Embrace change, document ideas, focus on user stories, and leverage backlogs for effective planning and adaptation.

How does it feel when your team more than doubles in less than a year? Like your brain was put in a blender and someone kept pressing the button. I joined a 7-engineer, 40 employee, startup in August, by May we were 14 engineers, 100 people, and I was a manager. These are my lessons.

Swizec Teller

Ágil no es solo una metodología, es una mentalidad. Y en la era de la IA Generativa, esa mentalidad está evolucionando. Esta sesión explora cómo los equipos Ágiles pueden integrar GenAI para impulsar la colaboración, la velocidad y la innovación a lo largo del ciclo de vida del desarrollo de software. Aprende cómo la IA puede ayudar con las historias de usuario, la planificación de sprints, las retrospectivas y más, actuando como un miembro incansable del equipo. Se compartirán ejemplos del mundo real, consejos de ingeniería de prompts y las mejores herramientas para potenciar tus stand-ups, backlogs y gráficos de burndown.

Supercharging Agile Teams with Generative AI

Kanika Tover discusses supercharging agile teams with AI, emphasizing an agile mindset, adaptability, and collaboration. Generative AI tools enhance productivity gains, development speed, and efficiency in agile teams. AI integration improves planning, development, testing processes, feedback facilitation, and communication efficiency. Core AI platforms like OpenAI and Chat GPT-5 are crucial for text generation. Future trends include AI evolving into team assistants and agents for tasks. Real-life AI applications focus on efficiency and productivity enhancement. AI collaboration improves meeting efficiency, technical debt management, and customer focus.

Agile isn’t just a methodology—it’s a mindset. And in the era of Generative AI, that mindset is evolving. This session explores how Agile teams can integrate GenAI to boost collaboration, speed, and innovation across the software development lifecycle. Learn how AI can assist with user stories, sprint planning, retrospectives, and more—acting as a tireless team member. Real-world examples, prompt engineering tips, and the top tools to supercharge your stand-ups, backlogs, and burndown charts will be shared.

Kanika Tolver

Visión general:        La adopción de AI trae complejidad al sistema, especialmente cuando la autonomía, la orquestación y los microservicios nativos de la nube colisionan. Basado en mi investigación publicada y experiencia empresarial, este masterclass desglosa la complejidad de implementar sistemas agénticos de manera responsable. Este masterclass está dirigido a ingenieros senior que están navegando arquitecturas nativas de la nube mientras introducen componentes autónomos de AI, sin un plan claro.Contenidos:1. La Nueva Pila de Complejidad    a) Microservicios, AI y generadores de código de terceros    b) Dónde comienza el caos del sistema y por qué se intensifica 2. Comprendiendo el Comportamiento de AI Agéntico    a) Autonomía, memoria y riesgo de alucinación    b) Ejemplos del mundo real del soporte de decisiones financieras 3. Patrones de Diseño para la Claridad    a) Barandillas para la observabilidad del sistema    b) Compromisos: orquestación vs generación de código 4. Escalabilidad Sin Agotamiento    a) Lo que los ingenieros senior deben liderar: gobernanza, ética, fiabilidad     b) Diseñando sistemas que escalen de manera segura bajo carga 5. Conclusiones    a) Hoja de ruta para la adopción agéntica    b) Cómo probar y monitorear AI no determinista    c) Alineación con la velocidad del equipo y los resultados del negocio

De Caos a Claridad: Gestionando la Complejidad de Sistemas Agénticos en FinTech Nativo de la Nube

Workshop Event: Genetic AI Chaos to Clarity. Staff Software Engineer Expert in Agentic AI Microservices. Evolution from Monolith to Microservices Challenges. Layering AI Agents on Microservices Challenges. AI Agents Evolution and Determinism Challenges. Challenges of AI Agents in Industry Adoption. Setting Boundaries for AI Agents. Setting Boundaries and Policies for AI Agents. Setting Boundaries and Instructions for LLMs. Memory Management and Policy Guidance for LLMs. Guidance for Autonomous Agents and Data Management. Solving API Caching Issues with Autonomous Agents. Challenges in AI Agents' Failure Scenarios. Addressing Incident Response and Boundary Patterns. Establishing Boundaries and Fallback Mechanisms. Designing Agent Governance and Orchestrator. Governance for AI Agent Boundaries. Company Use Cases and Automation Initiatives. AI Tools Implementation for Workflow Automation.

Overview:        AI adoption brings system complexity, especially when autonomy, orchestration, and cloud-native microservices collide. Based on my published research and enterprise experience, this workshop breaks down the complexity of deploying agentic systems responsibly. This workshop is aimed at senior engineers who are navigating cloud-native architectures while introducing autonomous AI components, without a clear blueprint.Contents:1. The New Complexity Stack    a) Microservices, AI, and third-party code generators    b) Where system chaos starts and why it escalates 2. Understanding Agentic AI Behavior    a) Autonomy, memory, and hallucination risk    b) Real-world examples from financial decision support 3. Design Patterns for Clarity    a) Guardrails for system observability    b) Tradeoffs: orchestration vs code generation 4. Scalability Without Burnout    a) What senior engineers must lead: governance, ethics, reliability     b) Designing systems that scale safely under load 5. Takeaways    a) Agentic adoption roadmap    b) How to test and monitor non-deterministic AI    c) Alignment with team velocity and business outcomes

From Chaos to Clarity: Managing Agentic System Complexity in Cloud-Native FinTech

Sibasis Padhi

Las entrevistas técnicas están luchando por mantenerse al día con la realidad de la ingeniería de software moderna en una época de agentes de IA. Los LLMs y sus derivados ahora pueden resolver fácilmente evaluaciones para llevar a casa, y los formatos tradicionales de DSA recompensan la memorización sobre las habilidades del mundo real. Entonces, ¿cómo podemos evaluar de manera precisa y justa las habilidades técnicas de los ingenieros como parte de un proceso de contratación estandarizado? A finales de 2024, comencé un rediseño del proceso de entrevistas técnicas en HelloBetter, una empresa de tecnología de salud mental en Berlín. El objetivo era crear un formato que realmente reflejara el trabajo diario de ingeniería, al mismo tiempo que fuera consciente del tiempo, inclusivo y aceptara la presencia de IA en los flujos de trabajo de los desarrolladores. En el proceso, creamos "El Método McDougall": un nuevo y emergente marco para mejores procesos de entrevista que está obligando rápidamente a los líderes técnicos a repensar sus estrategias de mitigación de IA.

Transformando tus entrevistas técnicas en la era de los agentes de IA: El método McDougall en detalle

Anna McDougall discusses transforming tech interviews in the AI agent age and the flaws in traditional technical interviews due to lack of control in remote settings. Addressing challenges in technical interviews with AI integration and the need for adapting traditional assessment methods to combat AI influences. Balancing technical interview difficulty, focusing on psychological safety, and using a modern, realistic, and indicative approach in the McDougall method. Organizing engineer for meetings, pairing engineer for interactions, and use of all tools allowed in the McDougall method. AI tools are essential in development. McDougall method: Intro, warmup, pair programming, reflection, wrap up. Shorter interviews save candidate time. Intro warmup: 10-15 mins. Pair programming checks for implementation, 35-45 mins. Create repository based on a real codebase for pair programming. Create Mimic codebase for testing. Send repository with failing tests. Tasks represent different levels: fresh junior to senior plus. Start candidates one level under. Psychological safety in tasks. Levelling based on task difficulty. Reflecting on candidate understanding and feedback. Emphasizing timeline in wrap up. Encouraging McDougall Method adoption and experimentation.

Technical interviews are struggling to keep pace with the reality of modern software engineering in a time of AI agents. LLMs and their offshoots can now easily solve take-home assessments, and traditional DSA formats reward memorisation over real-world skills. So how can we accurately and fairly assess the technical abilities of engineers as part of a standardised hiring process? In late 2024, I began a redesign of the technical interview process at HelloBetter, a mental health tech company in Berlin. The aim was to create a format that actually reflects day-to-day engineering work, while also being time-conscious, inclusive, and accepting of the presence of AI in developer workflows. In the process, we created "The McDougall Method": a new and emerging framework for better interview processes which is quickly forcing technical leaders to rethink their AI mitigation strategies.

Transforming Your Tech Interviews in the AI Agent Age: The McDougall Method in Detail

Anna J McDougall

La mayoría de los equipos todavía están usando AI como un autocompletado glorificado, pero el futuro ya está aquí. Te mostraré cómo funcionan los equipos de ingeniería cuando los agentes de codificación manejan la implementación mientras los humanos se centran en la orientación y supervisión.He construido agentes de codificación que tienen nuestras directrices de equipo integradas: no más guías de estilo o documentos de estándares de codificación. Verás agentes que siguen automáticamente nuestros patrones de Next.js 15, la estructura de monorepo de Nx, las convenciones de shadcn/ui, y más de nuestra pila tecnológica, mientras los humanos hacen pequeñas correcciones que mejoran todo el trabajo futuro.Esto no se trata de reemplazar a los ingenieros, sino de escalar las posibilidades de tu equipo a través de la multiplicación de agentes en lugar de contrataciones y reestructuraciones. Demostraré el flujo de trabajo donde la implementación del agente es seguida por el refinamiento humano, creando una calidad consistente que no depende de la variabilidad humana.

El Futuro de los Equipos de Ingeniería: Los Humanos Guían, los Agentes Codifican

The workshop delves into vibe coding, AI, and the journey of Lovable, covering binary reality, coding agents, and cloud code demonstrations. Challenges in estimation, Agile methods, prototype development, coding iterations, and deployment strategies are discussed. Evolution of coding tools, documentation challenges, AI patterns, agent orchestration, file structuring, and AI community interaction are highlighted. The workshop also explores tasks like coding strategies, agent setup, dependency management, code corrections, middleware security, and task management within the AI coding community.

Most teams are still using AI as a glorified autocomplete, but the future is already here. I'll show you how engineering teams work when coding agents handle implementation while humans focus on guidance and oversight.I have built coding agents that have our team guidelines embedded - no more style guides or coding standards docs. You'll see agents that automatically follow our Next.js 15 patterns, Nx monorepo structure, shadcn/ui conventions, and more of our tech stack, while humans make small corrections that improve all future work.This isn't about replacing engineers - it's about scaling the possibilities of your team through agent multiplication instead of hiring and restructurizations. I'll demonstrate the workflow where the agent implementation is followed by human refinement, creating consistent quality that doesn't depend on human variability.

The Future of Engineering Teams: Humans Guide, Agents Code

Konrad Bujak

Los avances en AI nos han llevado al borde de un cambio importante en cómo interactuamos con la web. El futuro del frontend ya no se tratará solo de renderizar píxeles; se tratará de renderizar intenciones.En este discurso principal, profundizaremos en cómo AI cerrará aún más la brecha entre la intención humana y la experiencia digital. AI nos está moviendo más allá de los componentes estáticos, permitiendo a los desarrolladores crear aplicaciones web profundamente personalizadas e intuitivas con un nivel de eficiencia con el que solo hemos soñado. Únete a mí para explorar cómo AI empodera a los desarrolladores frontend para crear experiencias de usuario más ricas a través de una personalización más reflexiva, pruebas de experiencia de usuario más inteligentes a gran escala e innovación acelerada.

La Web Impulsada por la Intención: Cómo AI está Remodelando el Desarrollo Frontend

The speaker recounts a frustrating drive-through experience on a family road trip, showcasing the challenges of AI implementation in noisy environments. Emphasis is placed on user-centric software development and the importance of strategic AI application. The discussion delves into user navigation difficulties, data collaboration hurdles, and the potential of generative UI for personalized experiences. Leveraging LLM for dynamic UI generation based on user intent is highlighted, along with its capabilities in contextual understanding and task orchestration. The use of varied prompts to enhance LLM performance and the significance of AI integration in user-centric testing are also explored.

Advancements in AI have brought us to the cusp of a major shift in how we interact with the web. The future of frontend will no longer be about just rendering pixels; it will be about rendering intent.In this keynote, we'll dive into how AI will further close the gap between human intent and digital experience. AI is moving us beyond static components, empowering developers to create deeply personalized and intuitive web applications with a level of efficiency we've only ever dreamed of. Join me to explore how AI empowers frontend developers to craft richer user experiences through more thoughtful customization, smarter user-experience testing at scale, and accelerated innovation.

The Intent-Driven Web: How AI is Reshaping Frontend Development

Shaundai Person

Gregor compartirá tanto las tendencias positivas como negativas que está observando, hablando con muchos líderes de ingeniería en toda la industria. También compartirá cómo está evolucionando el rol de los líderes de ingeniería y consejos sobre qué hacer para ser un líder de ingeniería exitoso en la era de la IA.

Cómo la IA está impactando el liderazgo en ingeniería

Gregor discusses the impact of AI on engineering leadership, addressing concerns and sharing insights from industry discussions. Teams are feeling less motivated due to sensationalistic AI predictions causing fear of missing out. Company leaders face challenges managing unrealistic AI expectations and beliefs. Guidance is provided for leaders to avoid FOMO, start with small AI experiments, and prioritize good culture. Developing AI strategy involves aligning with engineering, embracing skill convergence, and educating on AI tools.

Gregor will be sharing both positive and negative trends that he's seeing, speaking with many engineering leaders across the industry. He'll also share how the role of engineering leaders is evolving, and tips on what to do to be a successful engineering leader in the age of AI.

How AI is Impacting Engineering Leadership

Gregor Ojstersek

Convertirse en un Lead o Staff Engineer no significa tener todas las respuestas, sino saber cómo hacer mejores preguntas, cómo apoyar a los demás y cómo tomar decisiones inteligentes en la ambigüedad. En esta charla, compartiré lo que he aprendido (y desaprendido) al asumir roles de liderazgo IC: cómo crecer en influencia sin gestionar personas, cómo admitir "No sé" con confianza y cómo usar herramientas de AI para ahorrar tiempo, enfocarse en lo importante y escalar mi impacto. Se trata menos de ser el experto y más de habilitar a otros—y sí, ese es el trabajo.

No Sé Todo, y Ese Es el Trabajo

Maya Shavin's tech journey, the importance of collaboration and growth, asking quality questions for problem-solving, prioritizing root causes, embracing uncertainty, admitting uncertainty and growing influence, influencing team growth, smart prioritization, building trust, efficient task management, smart delegation, mastering prioritization with AI assistance, empowering learning, and scaling impact.

Becoming a Lead or Staff Engineer doesn’t mean having all the answers—it means knowing how to ask better questions, how to support others, and how to make smart decisions in ambiguity. In this talk, I’ll share what I’ve learned (and unlearned) stepping into IC leadership roles: how to grow influence without managing people, how to admit “I don’t know” with confidence, and how to use AI tools to save time, focus in where it's important and scale my impact. It’s less about being the expert, and more about enabling others—and yes, that’s the job.

I Don’t Know Everything, and That's the Job

Maya Shavin

¿Qué pasaría si dejaras que la IA codificara junto a tus desarrolladores? ¿Acelerarían las cosas o las ralentizarían? 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 toda tu organización de ingeniería, sin exageraciones.

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

The discussion covers the integration of AI tools in software development and the productivity gains observed in an experiment at Modus Create. AI significantly enhanced productivity and code quality, with a Python engineer successfully adapting to TypeScript with AI assistance. Human oversight is crucial for optimal results in AI coding, ensuring quality and complementing human expertise. Clear communication with AI, contextual feeding, effective task management, and workflow optimization were highlighted as key factors in leveraging AI tools for software development.

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

He visto a programadores bien intencionados, líderes técnicos y sus gerentes volcar sus corazones y almas en la gestión de lo que llaman "deuda técnica" y me preocupa que no estén avanzando mucho. Creo que un malentendido fundamental desde el principio ha llevado a casi todos por un camino oscuro y doloroso hacia una amarga decepción, interminables disputas, proyectos fallidos, creciente Síndrome del Impostor y una debilitante indefensión aprendida. Afortunadamente, creo que podemos revertir estas tendencias, al menos para nosotros mismos, con una combinación de técnicas de Extreme Programming y psicología general. ¡No, en serio! Podrías sorprenderte al saber que todos realmente quieren lo mismo, pero que algunas complejidades de la mente humana han hecho que eso sea difícil de ver. Superar este malentendido fundamental encaja perfectamente en la categoría de "simple, no fácil", así que si queremos avanzar, ¡será mejor que comencemos ahora!

Superando el Malentendido Fundamental Sobre la Deuda Técnica

JB Rainsberger discusses the impact of technical debt, emphasizing the cost of delaying essential work and its implications on feature delivery. Reflecting on past attempts to address technical debt and the diverse interpretations of its meaning, highlighting the challenges in managing technical debt and the resulting dysfunction in teams. Exploring the true nature of technical debt as a reflection of current understanding and the ongoing risk of a mismatch between programmers and code. Managing technical debt without specialized techniques or tools, addressing distortions, and collaborating to face ongoing risks. Improving code alignment and understanding reduces future regrets. Embracing regular code realignment reduces resistance to necessary changes. Test-driven development ingrains managing technical debt in daily programming.

I have watched well-meaning programmers, technical leaders, and their managers pour their hearts and souls into managing so-called "technical debt" and I worry that they're not getting very far. I believe that a fundamental misunderstanding from the very beginning has led almost everyone down a dark, painful road towards bitter disappointment, endless bickering, failed projects, mounting Imposter Syndrome, and debilitating learned helplessness. Fortunately, I think we can reverse these trends, at least for ourselves, with a combination of techniques from Extreme Programming and general psychology. No, really! You might be surprised to learn that everyone truly wants the same thing, but that some complexities of the human mind have made that difficult to see. Overcoming this fundamental misunderstanding fits squarely into the category of "simple, not easy", so if we want to make progress, then we'd better start now!

Overcoming the Fundamental Misunderstanding About Technical Debt

J. B. Rainsberger

"Tolerancia a la Entropía" es una idea original para ayudar a los líderes técnicos a navegar las decisiones de adopción de AI. Este concepto mide cuánta incertidumbre generada por AI pueden manejar tus sistemas y procesos antes de que se rompan. Aprende este marco de toma de decisiones para guiar a tus equipos sobre cuándo adoptar AI y cuándo proceder con precaución.

Tolerancia a la Entropía: La Pregunta Más Importante de Software Que No Estás Haciendo

In software development, the discussion focuses on understanding determinism vs. non-determinism and its impact on reliability and trustworthiness. The examples provided highlight the implications of non-deterministic behavior, especially in critical systems like language model autopilots on planes. Technical leaders are advised to consider entropy tolerance when integrating AI and LLMs into business processes using a decision framework. Different entropy tolerance levels exist, influencing the need for AI-human collaboration based on the process's value and tolerance. The decision-making framework for AI integration emphasizes the importance of human involvement in processes with varying levels of entropy tolerance.

"Entropy Tolerance" is an original idea to help technical leaders navigate AI adoption decisions. This concept measures how much AI-generated uncertainty your systems and processes can handle before they break. Learn this decision-making framework to guide your teams on when to embrace AI and when to proceed with caution.

Entropy Tolerance: The Most Important Software Question You Aren't Asking

Mientras que las herramientas de IA sobresalen en la generación de código, no pueden navegar las complejidades humanas que hacen o deshacen las decisiones técnicas. Como ingenieros senior, nuestro valor reside cada vez más no en lo que codificamos, sino en cómo guiamos a los equipos a través de elecciones técnicas que reflejan nuestros valores y cultura.A través de ejemplos reales de cómo los conflictos de valores se disfrazan de desacuerdos técnicos, esta sesión explora lo que los líderes tecnológicos necesitan preservar y fortalecer a medida que la IA transforma nuestra industria. Descubrirás herramientas prácticas para descubrir los valores del equipo y aprenderás a tomar decisiones técnicas que tengan en cuenta tanto el código como la cultura en un mundo impulsado por la IA.

Mantenerse Humano en la Era de la IA: Lo que los Líderes Tecnológicos Necesitan Preservar

The speaker reflects on the evolution of AI, its impact on coding accessibility, and the value of understanding human complexity in decision-making. Leadership challenges with junior developers highlight the importance of human dynamics and autonomy. Teamwork involves exploring values and navigating conflicts, emphasizing the significance of aligning team values in decision-making processes. Implementing human-centered technical decisions and fostering value alignment within teams are key strategies for successful adaptation and community building.

While AI tools excel at generating code, they can't navigate the human complexities that make or break technical decisions. As senior engineers, our value increasingly lies not in what we code, but in how we lead teams through technical choices that reflect our values and culture.Through real examples of how values conflicts disguise themselves as technical disagreements, this session explores what tech leaders need to preserve and strengthen as AI reshapes our industry. You'll discover practical tools for uncovering team values and learn to make technical decisions that account for both code and culture in an AI-driven world.

Staying Human in the Age of AI: What Tech Leaders Need to Preserve

Amy Dutton

AI está cambiando el juego - rápido. Diseñadores, PMs, e incluso equipos no técnicos ahora pueden prototipar, construir y lanzar sin tocar una línea de código. Herramientas como Lovable, Figma AI, Copilot y otros agentes de AI están difuminando los límites entre diseño, implementación y despliegue. Entonces, ¿dónde deja eso a los desarrolladores?Es tentador decir que nuestros trabajos están siendo reemplazados. Pero quizás solo estamos entrando en una nueva fase.A medida que AI maneja más de las tareas visibles y repetitivas, diseños de UI, código boilerplate, incluso conectando APIs—la complejidad no desaparece. Se mueve. El verdadero trabajo se está desplazando detrás del telón: hacia la orquestación, lógica, validación, gobernanza y rendimiento. Esta charla explora cómo la ingeniería está evolucionando en un mundo de todo generado por AI. Pregunta: con gran poder (de automatización) ¿viene qué tipo de responsabilidad? ¿Cómo mantenemos el control, la responsabilidad y la calidad cuando cualquiera puede “construir” algo que funcione?Te irás con una visión clara de lo que AI puede y no puede (todavía) reemplazar, por qué el juicio humano es más crítico que nunca y cómo los desarrolladores pueden reclamar la propiedad, no sobre píxeles, sino sobre sistemas. Si alguna vez te has preguntado, “¿Cuál es mi papel cuando todos son constructores?”—esta charla te ayudará a responder eso.

Repensando los Roles de Ingeniería en la Era de la Disrupción de AI

Discussing the impact of AI on engineering and the role of developers. Keren Fanan, CEO of MyApp, shares insights on AI's potential to empower engineers and reduce dependency on manual coding. AI's role in development: balancing AI automation with human expertise. Engineers post-AI: orchestrating human-AI code interaction, with examples from Uber and Netflix. Balancing ownership of AI-generated code with different ownership assignments and connecting AI and developer-generated code. AI as a teammate, organizational shift to AIR, tips for management: creating AI squads, having weekly AI rituals, prioritizing transparency, and determining code ownership by the creator.

AI is changing the game - fast. Designers, PMs, and even non-technical teams can now prototype, build and ship without touching a line of code. Tools like Lovable, Figma AI, Copilot and other AI agents are blurring the boundaries between design, implementation and deployment. So where does that leave developers?It’s tempting to say our jobs are being replaced. But maybe we’re just entering a new phase.As AI handles more of the visible, repetitive tasks, UI layouts, boilerplate code, even connecting APIs—the complexity doesn’t disappear. It moves. The real work is shifting behind the curtain: into orchestration, logic, validation, governance and performance. This talk explores how engineering is evolving in a world of AI-generated everything. It asks: with great power (of automation) comes what kind of responsibility? How do we maintain control, accountability and quality when anyone can “build” something that runs?You’ll walk away with a clear view of what AI can and can’t (yet) replace, why human judgment is more critical than ever and how developers can reclaim ownership, not over pixels, but over systems. If you’ve ever wondered, “What’s my role when everyone’s a builder?”—this talk will help you answer that.

Rethinking Engineering Roles in the Age of AI Disruption

Keren Fanan

Cómo un COO tecnológico utiliza la ingeniería de prompts para gestionar las finanzas, dirigir equipos y escalar operaciones con claridad.Compartiré casos prácticos de ingeniería de prompts desde dentro de Epicmax, una agencia de desarrollo en crecimiento. Explicaré cómo utilizo LLMs para:- Informes financieros, previsión y control de márgenes- Gestión de la capacidad del equipo y riesgos de entrega- Seguimiento de KPIs y creación de estrategias a corto y largo plazo- Propuestas de ventas, lanzamiento de nuevos productos, búsqueda de ajuste en el mercado- Gestión de productos de código abierto- Comunicación con el propietario de la empresa, presentación y validación de ideas Obtendrás prompts reales, flujos de trabajo y estrategias de toma de decisiones para hacer de la IA una verdadera parte de tu kit de herramientas de liderazgo, no solo otra herramienta en el estante.

Operaciones impulsadas por prompts: Manual del COO

Anastasia Zmiengorodzka discusses using GPT as a strategic partner, leveraging her tech background at EpicMax. She outlines enhancing tech leadership skills with GPT, emphasizing clear communication, trend identification, and business integration.

How a tech COO uses prompt engineering to run financials, manage teams, and scale operations with clarity.I’ll share practical use cases of prompt engineering from inside Epicmax, a growing dev agency. I’ll walk through how I use LLMs for:- Financial reporting, forecasting, and margin control- Managing team capacity and delivery risks- Tracking KPIs and creating short-term and long-term strategy- Sales proposals, launching new products, finding market fit- Open source products management- Communicating with the company owner, pitching and validating ideas You’ll get real prompts, workflows, and decision-making strategies to make AI a true part of your leadership toolkit - not just another tool on the shelf.

Prompt-Driven Operations: COO’s Playbook

Anastasiia Zvenigorodskaia

La IA ya no es solo una herramienta, se está convirtiendo en un participante activo en nuestros sistemas. Desde agentes autónomos que toman decisiones hasta modelos de IA que acceden a datos sensibles, la pregunta ya no es “¿Podemos adoptar IA?” sino “¿Cómo controlamos lo que la IA puede hacer?”En esta sesión, mostraré cómo la Gobernanza de Identidad es el eslabón perdido entre la adopción responsable de IA y la gestión de la complejidad del sistema.- Por qué la adopción de IA introduce nuevos riesgos de identidad y superficies de ataque ocultas- Cómo simplificar la complejidad del sistema de IA a través de modelos de gobernanza sólidos- Lo que los líderes tecnológicos deben saber sobre la gobernanza de sistemas impulsados por IA e identidades de máquinas- Patrones del mundo real para controlar el acceso de IA utilizando herramientas como Microsoft Entra ID y Privileged Identity Management (PIM), Security Co-pilot, DSPM para IA y másEsta charla combina estrategia con ejemplos prácticos, ofreciendo un camino claro para los líderes que desean escalar la IA de manera segura sin aumentar el riesgo o la complejidad. Ya sea que esté navegando por la integración de IA, liderando equipos tecnológicos o escalando plataformas empresariales, esta sesión le proporcionará ideas accionables para generar confianza en su ecosistema impulsado por IA. La IA ha llegado para quedarse. Los líderes que la gobiernen sabiamente definirán el futuro.

Del Control de Acceso al Control de IA: Por Qué la Gobernanza de Identidad es Clave para Escalar la IA en la Empresa

A bank faces fraudulent transactions caused by a poisoned prompt from an AI co-pilot. AI plays a vital role in sectors like health, retail, and enterprise, but its integration poses challenges in identity and access management. Generative AI vulnerabilities lead to shadow AI emergence, with independent agents creating access control and data security challenges. Identity governance is crucial for managing AI, with a playbook emphasizing Identification, Security, Governance, and Auditing. Microsoft's Entra Agent ID ensures visibility and policies for AI agents to prevent disclosure of sensitive information.

AI is no longer just a tool, it’s becoming an active participant in our systems. From autonomous agents making decisions to AI models accessing sensitive data, the question is no longer “Can we adopt AI?” but “How do we control what AI can do?”In this session, I will show how Identity Governance is the missing link between responsible AI adoption and system complexity management.- Why AI adoption introduces new identity risks and hidden   attack surfaces- How to simplify AI system complexity through strong governance models- What tech leads must know about governing AI-powered systems and machine identities- Real-world patterns for controlling AI access using tools like Microsoft Entra ID and Privileged Identity Management (PIM), Security Co-pilot, DSPM for AI and more This talk blends strategy with practical examples, offering a clear pathway for leaders who want to scale AI securely without increasing risk or complexity. Whether you are navigating AI integration, leading tech teams, or scaling enterprise platforms, this session will arm you with actionable insights to build trust in your AI-driven ecosystem. AI is here to stay. The leaders who govern it wisely will define the future.

From Access Control to AI Control: Why Identity Governance Is Key to Scaling AI in the Enterprise

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In this edition of TechLead Conf we will focus on two pressing topics in 2025: adopting AI in organizations and challenges around it from tech leadership perspective, and how Senior engineers and leaders can work on reducing systems complexity. Learn directly from industry practitioners and real case studies ranging from startups to The Big Tech.

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