Prototyping & shipping

This talk addresses a critical oversight in agent development: designing infrastructure for human interaction rather than agentic behavior. The core thesis is that many production LLM errors, particularly those related to rate limits, stem from this fundamental mismatch. Infrastructure built for human speed and interaction patterns fails when agents make thousands of calls per second, leading to performance issues and unexpected costs.

This talk emphasizes the critical need for rigorous evaluation of AI skills before deployment, arguing that shipping skills without proper testing can lead to unpredictable behavior and degraded performance. The speaker highlights the distinction between agents developers use for personal productivity and agents built for consumers, noting that end-users lack the context to troubleshoot skill invocation issues. The core thesis is that comprehensive evaluations are essential for ensuring skill reliability, managing costs, and determining when skills can be retired as models improve.

Pauline Brunet discusses the Forward Deployed Engineering (FDE) function, emphasizing its critical role in enterprise AI adoption. She outlines a framework for determining when and how to implement an FDE team, based on customer digital maturity and product customization levels. The core thesis is that FDEs are highly technical individuals who act as agents of change, embedding within customer organizations to drive digital transformation and ensure successful adoption of AI technologies.

This talk details a 30-day initiative at Automattic where employees were encouraged to pair up and build/ship projects, leveraging AI tools. The experiment aimed to accelerate product development and foster new skills, particularly for designers, by allowing them to own the entire product lifecycle from ideation to deployment. The speaker shares personal experiences building three distinct projects, highlighting the shift in their role from designer to design engineer and the broader implications for organizational agility.

This talk outlines a production AI playbook, a framework designed to guide the deployment of AI systems at an enterprise scale. It addresses common pitfalls encountered when moving AI from demos to production, emphasizing the need for structured approaches to evaluation, observability, data foundation, orchestration, and governance. The framework aims to ensure AI systems are measurable, accountable, and reliable in real-world applications.

This talk introduces Google DeepMind's Gemma family of open models, highlighting their capabilities and the benefits of using open-source AI for greater ownership and customization. The presenters emphasize that while proprietary models like Gemini offer cutting-edge performance, open models like Gemma provide crucial advantages for specific use cases, such as running models on local hardware, handling sensitive data, and adapting models to unique requirements.

This talk introduces RunPod's Flash, a Python SDK designed to streamline the development and deployment of AI models on GPU infrastructure. The core thesis is that developers can significantly reduce iteration time by deploying functions directly to the cloud from their local IDE, eliminating the need for manual commits, Docker builds, and server configuration. This allows for rapid testing and iteration on GPU-accelerated workloads.

This talk, titled 2026 AI Engineer Vibe Reel, explores the evolving landscape of AI engineering, focusing on prototyping and shipping AI-powered products. It offers insights into the current trends and future directions that AI engineers, product managers, and builders should be aware of as they develop and deploy AI solutions. The reel aims to capture the essence of what it means to be an AI engineer in the near future.

This talk introduces RunPod as a cloud AI infrastructure platform designed to simplify GPU access and model deployment for developers. The core thesis is that managing complex infrastructure, especially GPU hardware, is a significant hurdle for builders, and RunPod aims to abstract this away. The platform allows users to bring their own code and models, whether private or open-source, and deploy them quickly, focusing on enabling developers to build applications rather than manage hardware.

This talk explores the rapid development and deployment practices within the OpenClaw project, likening the process to a "dark factory" that ships code at an unprecedented velocity. It argues that the sheer speed of development necessitates a shift from traditional code review to a more managerial approach, where engineers oversee swarms of autonomous agents. The core thesis is that effective engineering in this new paradigm relies on process, intuition, and managing agent workflows rather than solely on individual coding prowess.

This talk argues that Rust is a superior language for agentic coding, often referred to as vibe coding, despite conventional wisdom favoring languages like Python and TypeScript. The speaker contends that while dynamic languages are easier for LLMs to generate initially, this ease comes at the cost of increased error potential. Rust's strict compiler and numerous constraints, though posing a steeper learning curve for LLMs, provide deterministic guardrails that significantly reduce bugs and improve code reliability.

This talk focuses on the practical aspects of evaluating and improving AI agents, moving beyond simple "vibe checks" to implement robust testing frameworks. It emphasizes that effective evaluation is crucial for shipping reliable AI applications, especially agents, which are prone to cascading failures. The session introduces methods for capturing agent behavior through tracing, categorizing failures, and implementing various types of evaluations (code-based, LLM-as-judge, human) to ensure agents perform as expected and to drive iterative improvements.

This talk introduces a framework for building AI agents that can interact with a computer, leveraging the AI SDK. The core thesis is that effective agents require an agent runtime, tools for interaction, and a computational environment like a sandbox file system for state persistence and code execution. The presentation demonstrates how to integrate these components to create more capable and autonomous AI agents.

This talk argues that reinforcement learning (RL) is crucial for moving AI models, particularly large language models (LLMs), from pilot stages to production. The speaker posits that the common failure of GenAI pilots stems from the "myth of the last mile," where initial MVPs built on proprietary models or instruction fine-tuning lack systematic improvement pathways. RL, by its nature, allows for the mathematical integration of feedback, leading to more effective model steering and enabling scaled, cost-efficient, and faster deployments.

This talk explores the "Vibe Engineering Effect" by demonstrating how to build applications from scratch using AI agents, focusing on practical development workflows rather than hype. The core thesis is that by structuring projects to leverage AI effectively, developers can significantly reduce manual coding, even for complex library-level tasks. The session emphasizes practical techniques for integrating AI into the development process, particularly when working with new or unfamiliar codebases.

This talk introduces Missions, a multi-agent system designed to tackle complex software development tasks that extend beyond a single agent's capacity. The core thesis is that human attention, not intelligence, is the current bottleneck in software engineering. Missions aims to overcome this by enabling systems to execute tasks autonomously for extended periods, allowing human engineers to focus on higher-level strategic decisions.

This talk introduces "Ralph loops," a method for building AI agents that can iteratively improve their own work by repeatedly attempting a task. The core idea is to leverage the ability of modern AI models to self-correct and refine outputs through repeated execution of a prompt or instruction. This approach moves away from complex, brittle orchestration workflows towards simpler, more robust agentic loops that can be applied to various tasks, including coding and content generation.

This talk posits that as AI capabilities advance, the role of software engineers will increasingly shift from direct code writing to planning and reviewing AI-generated code. This evolution necessitates new approaches to managing workflows, particularly as AI agents begin to run for longer durations, requiring engineers to adopt a more parallel and managerial style of work.

This talk focuses on the practical challenges of shipping complex AI applications, moving beyond initial prototypes to robust, production-ready systems. It emphasizes the need for operational rigor, structured workflows, and continuous evaluation, drawing on experiences from Braintrust and Trainline. The core thesis is that while AI models are increasingly sophisticated, the operational practices for deploying and managing them at scale have lagged, creating a significant hurdle for delivering real customer value.

This talk explores building and deploying AI-powered applications, focusing on the capabilities and accessibility of Google DeepMind's latest models and tools. It highlights the multimodal nature of Gemini models, their integration into AI Studio for rapid prototyping, and the potential for creating complex applications with features like code execution, search grounding, and dynamic world generation. The presentation emphasizes practical demonstrations over slides, showcasing how developers can leverage these advancements for diverse projects.

AI engineering represents the successor to web development, poised to shape the next decade of software. Agents are emerging as a new form of software, making previously economically unviable automation tasks feasible. This shift is expanding the scope of what can be automated and increasing the demand for software engineers.

This talk introduces the Workflow DevKit and AI SDK from Vercel, designed to simplify the process of building durable AI agents. The core thesis is that by adopting a workflow pattern, developers can abstract away the complexities of production deployment, error handling, and observability, allowing them to focus on agent capabilities. The DevKit enables agents to be resumable, reliable, and easily integrated with human-in-the-loop processes.

This talk introduces an evaluation framework for AI product managers (PMs) focused on shipping reliable AI applications. It emphasizes the critical need for robust evaluation, analogous to software testing but adapted for the non-deterministic nature of AI models. The framework aims to provide PMs with tools and methodologies to build confidence in AI product performance, moving beyond subjective "vibe coding" to data-driven "thrive coding."

This talk explores the current impact and future potential of AI-assisted engineering, emphasizing that while AI can augment developers, its effective integration requires careful consideration of productivity metrics, organizational culture, and strategic implementation across the software development lifecycle. The core thesis is that AI's true value lies in enhancing, not replacing, engineers, and achieving positive outcomes depends on addressing variability in adoption and impact through education, clear communication, and a focus on psychological safety.

This talk explores the emerging playbook for building AI-native companies, emphasizing that the process is currently being invented collaboratively. The core thesis is that achieving 100% AI adoption among engineers creates a significant, tenfold difference in organizational capability. This shift enables a single engineer to build and maintain complex production products, fundamentally altering how software development is approached.

This talk details Zapier's initiative to empower their support team to ship code, addressing the constant challenge of "app erosion" caused by third-party API changes. By enabling support to fix bugs directly, Zapier aims to improve integration reliability and customer experience. The project, named Scout, evolved from initial experiments in enabling support for bug fixing and exploring AI-assisted code generation to a comprehensive agent-based system.

This talk discusses Bloomberg's experience integrating AI into its engineering organization, focusing on practical applications and lessons learned. The core thesis is that AI tooling can significantly alter the cost function of software engineering, enabling a re-evaluation of fundamental principles and a shift towards higher-quality software development. The organization aimed to leverage AI to improve developer productivity and system reliability across its vast codebase and numerous functions.

This talk explores the evolution of front-end development, contrasting traditional coding practices with emerging AI-assisted workflows. It introduces the concept of "vibe engineering" as a more sophisticated approach to using AI agents for coding, emphasizing the need for skilled engineers to guide and refine AI-generated code. The core thesis is that while AI can accelerate development, human expertise remains crucial for quality, abstraction, and complex problem-solving.

The talk posits that the traditional Integrated Development Environment (IDE) is becoming obsolete, predicting that by 2026, most code will be generated by advanced AI systems overseen by engineers. This shift moves from current tools like code assistants, which are seen as powerful but potentially dangerous like power tools, towards more precise, automated systems akin to CNC machines. The core argument is that AI will fundamentally reshape technology organizations and the economy, enabling unprecedented productivity and new ways of building software.

This talk addresses the challenges of using AI for complex software engineering tasks, particularly within large, existing codebases. The core thesis is that current AI models struggle with brownfield projects, often leading to rework and "slop." The presented solution focuses on advanced context engineering and intentional compaction to maximize the effectiveness of AI coding agents, enabling them to tackle difficult problems without introducing technical debt.

This talk introduces a framework designed to combat "vibe coding," an approach to AI-accelerated development that prioritizes speed and intuition over planning and maintainability, leading to brittle, unmanageable software. The framework emphasizes treating AI engineering as a continuous learning experience, with the engineer acting as the architect and the AI as the implementer. It advocates for a deliberate, iterative process that compounds understanding and productivity, ultimately enabling the creation of robust, production-ready applications.

This talk introduces the concept of evals at the application layer, distinguishing them from traditional unit tests. It emphasizes that Large Language Models (LLMs) can be unreliable, leading to unexpected failures in AI applications even when basic functionality appears to work. The core thesis is that robust evals are crucial for building reliable AI products by systematically testing and measuring performance across a spectrum of user-driven scenarios.

This talk explores the evolution of AI in software development, moving beyond simple autocompletion and chat interfaces to more sophisticated, agent-driven workflows. The core thesis is that the Command Line Interface (CLI) will become a central hub for "vibe coding with confidence," enabling developers to manage complex, end-to-end tasks with greater reliability and control. This shift aims to address the limitations of current AI tools, particularly in enterprise settings, by integrating AI across the entire Software Development Life Cycle (SDLC).

This talk addresses the current limitations of AI coding agents in identifying and fixing bugs, highlighting their tendency to generate false positives and struggle with complex codebases. It proposes practical strategies for developers to improve the effectiveness of these agents, focusing on structured rules, better context management, and leveraging advanced "thinking" models. The core thesis is that while agents show promise, their current implementation requires careful guidance and configuration to be truly useful for developers.

This talk argues that the current hype around AI code generation overlooks the complexities of production software engineering. While AI can generate code, it doesn't inherently understand the nuanced decision-making, maintenance, and safety requirements of large-scale, production-ready systems. The core thesis is that professional software engineers remain essential, and the focus should shift from the quantity of AI-generated code to how AI can augment the existing, rigorous practices of software development.

This talk addresses the challenges of building and shipping products in the rapidly evolving AI agent space, where the capabilities of the underlying LLMs are not fully understood. It proposes a shift in product development from defining specific requirements to focusing on affordances and emergent behaviors, emphasizing the need for new tools and practices to navigate this uncertainty. The core thesis is that product management must transform to embrace discovery and adaptation in a probabilistic world.

The core thesis is that successful product development, especially in the age of AI, hinges on rapid, iterative shipping and continuous user feedback, rather than large, infrequent releases. The speaker advocates for building "skateboard" equivalents—minimally viable products that deliver immediate value and allow for quick iteration based on real user input, which is far more effective than a traditional, linear development approach.

This talk demonstrates how to build and deploy AI agents that can actively contribute to software development workflows using Dagger. It emphasizes creating agents with specific tools and environments, enabling them to perform tasks like code editing and testing within a sandboxed, containerized system. The core idea is to integrate AI capabilities into existing software engineering pipelines, providing guardrails and structure to manage the output of generative AI.

This talk argues that the future of software development is agent-driven, moving beyond human-driven approaches. It posits that AI agents, when properly integrated and provided with sufficient context, can handle a majority of tasks across the software lifecycle, enabling production software to be built in minutes rather than months. The core thesis is that organizations must transition to an agent-native development paradigm to unlock the true power of AI.

This talk explores the complexities of transitioning a SaaS company to an AI-first model, emphasizing that it's an evolutionary journey rather than a binary switch. It proposes a framework focusing on strategy, ways of working, and people to navigate this transformation. The core thesis is that becoming AI-first requires reimagining product development, embracing ambiguity, and fostering a cultural shift across the entire organization, not just within AI teams.

This talk shares hard-earned lessons from over 200 enterprise Generative AI deployments, emphasizing that AI is not a universal solution. It highlights the importance of understanding customer needs, robust evaluation, and efficient architecture over simply adopting the latest AI trends. The core thesis is that successful AI implementation requires a deep understanding of inputs, outputs, and user behavior, rather than relying solely on advanced models or prompt engineering.

This talk explores the evolution of whiteboarding tools, drawing parallels between the transition from physical to digital whiteboards and the current integration of AI. It emphasizes that successful AI integrations should enhance, not merely replicate, existing functionalities, focusing on AI-native interactions rather than simply adding AI features. The core thesis is that AI and human collaboration in digital whiteboarding is entering a new phase, moving beyond basic AI augmentation to a more symbiotic partnership.

This talk details the process of developing and shipping Finn Voice, an AI-powered voice agent for customer support, within a 100-day timeframe. The core thesis is that voice AI represents a significant frontier in customer service, offering substantial benefits in cost savings, availability, and user experience compared to traditional phone support. The presentation emphasizes that successful deployment requires more than just advanced AI models; it necessitates a product-centric approach that considers use cases, conversation design, integration with existing workflows, and building user trust.

This talk focuses on building production-ready AI agents, moving beyond simple prototypes to scalable, cloud-hosted solutions. It outlines the essential components of an agent, including the model, prompt, loop, history, and tools, and demonstrates how to leverage AWS services like Amazon Bedrock Agents to deploy these agents at cloud scale. The presentation emphasizes practical steps for integrating custom tools via Lambda functions and preparing agents for a production environment.

The increasing adoption of AI tools in software development, particularly in the inner loop of coding, is creating a bottleneck in the outer loop of review, testing, merging, and deployment. This talk proposes that AI can also be leveraged to streamline and automate these outer loop processes, transforming the entire developer workflow rather than just the IDE.

This talk explores "Vibe Coding," a methodology for leveraging AI assistants to accelerate software development by focusing on output rather than the intricacies of code. It outlines a progression from "YOLO vibes" for rapid prototyping to "structured vibes" for maintainability and "spectrum vibes" for scale and reliability, emphasizing trust-building and guardrails for enterprise environments.

This talk explores different approaches to AI-assisted coding, moving from rapid, experimental "YOLO vibe coding" to more structured and spec-driven methods suitable for enterprise environments. The core thesis is that by understanding and applying these different "vibes," developers can leverage AI assistants more effectively, leading to increased productivity and maintainability in software development.

This talk discusses the challenges and solutions for implementing and scaling Message Communication Protocol (MCP) clients within a large organization. It highlights how the rapid evolution of AI models capable of tool calling led to integration chaos, with duplicated functionality and inconsistent interfaces across services. The solution proposed is to standardize on MCP for providing model context, treating it as a plumbing layer for integrations rather than a competitive differentiator.

This talk demonstrates how to build AI agents using Vercel's AI SDK, progressing from fundamental concepts to a practical deep research project. It highlights the SDK's unified interface for model switching, tool-use capabilities for interacting with the external world, and structured output generation for creating type-safe data. The deep research example showcases how to break down complex tasks into agentic workflows, combining web searching, result analysis, and recursive query generation to produce comprehensive reports.

This talk demonstrates how to build, evaluate, and deploy a Retrieval-Augmented Generation (RAG) based copilot for a retail environment using Azure AI services. The core concept involves creating a chatbot that can answer customer questions by accessing product information from a vector database and customer data from a relational database, all orchestrated through Azure AI Studio's Prompt Flow.

This talk focuses on optimizing Large Language Model (LLM) inference, emphasizing the practical challenges and cost-effectiveness of deploying these models at scale. It delves into the technical intricacies of the LLM inference workload, from tokenization and attention mechanisms to the crucial role of the KV cache in managing GPU memory and performance. The presentation aims to provide attendees with a deeper intuition about these processes and actionable strategies for measuring and improving deployment efficiency.

This talk addresses the practical challenges and technical debt encountered when deploying Large Language Models (LLMs) in enterprise environments. It highlights the shift from structured to unstructured data in AI applications and emphasizes that current LLMs augment human productivity rather than replace jobs. The discussion also touches on the limitations of LLM foundations and the importance of focusing on present AI risks over speculative future ones.

This talk addresses the challenges of deploying Generative AI applications to enterprises, focusing on the critical need for accuracy and trust. It highlights how traditional methods of analyzing customer interactions through manual review or scripted analysis are insufficient at scale. Generative AI offers a solution by enabling 100% coverage of conversations, surfacing unknown insights, and transforming the process of understanding customer needs and business operations.