Code 2025

This talk explores the challenges and potential of measuring AI capabilities, particularly focusing on developer productivity and long-term task completion. It questions the extrapolation of current AI progress trends, suggesting that physical and economic constraints, alongside potential technological breakthroughs, could alter the trajectory of AI development. The discussion also delves into the complexities of evaluating AI in real-world scenarios beyond controlled benchmarks, highlighting the gap between AI capabilities and practical application in fields like software engineering and data science.

This talk addresses the critical need for robust identity and authorization mechanisms for AI agents. It introduces new features and concepts designed to enable agents to securely interact with resources and perform actions on behalf of users. The core thesis is that as agents become more autonomous and capable, establishing clear identity, managing permissions, and ensuring user consent are paramount for safe and effective integration into various workflows.

This talk explores building durable, production-ready AI agents by integrating the OpenAI Agents SDK with Temporal. It highlights how Temporal's distributed systems capabilities can provide essential durability, visibility, and scalability to agentic applications, which are often inherently stateful and prone to failures. The presentation demonstrates how to leverage Temporal's workflow and activity abstractions to manage agent execution, tool invocation, and error handling, ensuring that agent processes can reliably resume after interruptions.

This talk argues that MCP servers, which act as interfaces for AI agents, should be designed with agents' unique strengths and weaknesses in mind, rather than simply mirroring human-facing APIs. The core thesis is that agents have different needs regarding discovery, iteration, and context compared to humans, and MCP server design must account for these differences to create effective and usable interfaces.

This talk introduces Spec-Driven Development as a method to enhance AI agent development, focusing on improving control, code quality, and reliability. It proposes a structured workflow that compresses the software development lifecycle, moving from prompt to requirements, design, and execution, with an emphasis on creating reproducible results. The approach aims to integrate established software engineering practices with AI capabilities to tackle more complex problems.

This talk introduces DSPy, a framework designed to streamline the development of applications that leverage large language models (LLMs). It proposes a shift from traditional prompt engineering to a more programmatic approach, treating LLMs as first-class citizens within Python programs. DSPy aims to simplify the creation of modular, optimizable, and maintainable AI systems by providing a structured way to define intents, manage logic, and integrate LLM calls.

This talk explores automating large-scale software refactoring and maintenance tasks using parallel AI agents. The core idea is that while individual agents are effective for smaller tasks, complex, multi-faceted operations like dependency updates, code modernization, or vulnerability remediation require orchestrating multiple agents working in parallel. This approach aims to tackle significant technical debt and improve developer productivity by automating laborious processes that are currently too large or complex for a single agent.

This talk introduces prompt learning, a method for optimizing AI agent prompts by creating a feedback loop that incorporates human and LLM-generated evaluations. The core idea is to move beyond static prompts and leverage detailed feedback, including explanations for failures, to continuously improve agent performance. This approach aims to enhance agent reliability and effectiveness without requiring extensive fine-tuning or architectural changes.

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 the Claude Agent SDK, a framework designed to simplify the creation of autonomous AI agents. The core thesis is that by leveraging Unix primitives like bash and the file system, developers can build more powerful and flexible agents. The SDK aims to encapsulate best practices learned from building agents like Claude Code, offering an opinionated yet effective approach to agent development.

This talk introduces the AI Engineering Code Summit, emphasizing AI coding as the most critical problem in technology today. It highlights the event's focus on bringing together experts to advance the AI coding industry across all companies, distinguishing it as a single-track summit dedicated to this theme. The summit aims to explore the patterns, systems, and products enabling AI's transformation of software development.

This talk introduces Manus, an AI action engine designed to go beyond providing answers and instead execute tasks, automate workflows, and extend human capabilities. The presentation highlights the platform's evolution, including Manus 1.5, which offers increased speed and quality. Manus aims to be a general AI agent accessible through various interfaces like web applications, Slack, and a newly launched API, meeting users wherever they are.

Poolside is developing AI models from scratch, focusing on pairing next-token prediction with reinforcement learning to bridge the gap between models and human intelligence. Their second-generation model, Malibu agent, is designed for high-consequence code environments, demonstrating capabilities in code conversion, testing, and feature implementation. The company emphasizes building foundational intelligence and scaling it, with plans for public API access and integration with platforms like Amazon Bedrock.

This talk explores the advancements in coding agents, particularly focusing on how Claude Code works and the underlying innovations that have made these tools effective. The core thesis is that simplicity in architecture, coupled with better underlying models and a focus on tool calling, has been the key breakthrough. The presentation emphasizes a philosophy of "give it tools and get out of the way," advocating for leaning into the model's capabilities rather than over-engineering complex systems.

This talk addresses the discrepancy between AI capabilities suggested by benchmarks and real-world performance, particularly in developer productivity. It introduces two distinct methods of evaluation: benchmark-style assessments measuring AI performance on diverse tasks against human baselines, and field experiments examining AI's impact on experienced developers in complex, real-world coding environments. The core thesis is that while benchmarks show rapid AI advancement, practical application, especially in messy, high-context scenarios, reveals a more nuanced and sometimes even negative impact on productivity.

This talk explores how to optimize developer experience in the era of AI coding agents. It argues that investments in foundational aspects of software development, such as standardized environments, robust validation, and clear documentation, will benefit both human developers and AI agents. The core thesis is that practices good for human developers are also good for AI, ensuring long-term value regardless of AI advancements.

AMP Code is an opinionated frontier AI coding agent designed to help developers navigate the rapidly changing landscape of AI-assisted software development. It aims to embrace the awe and absurdity of agents writing significant amounts of code, positioning itself as a research lab exploring the future of AI in coding. AMP offers both terminal and editor interfaces, focusing on providing the right amount of information without overwhelming the user and streamlining the code review process.

This talk argues that making codebases "agent-ready" is crucial for unlocking the full potential of AI in software engineering. The core thesis is that robust, automated validation and verification systems within a codebase are the primary enablers for successful and scalable AI agent adoption, rather than solely focusing on the capabilities of the AI tools themselves. By enhancing these internal validation mechanisms, organizations can significantly increase development velocity and the effectiveness of AI-powered workflows.

This talk explores the concept of autonomy in AI coding agents, particularly for non-technical users. It argues that true autonomy for this audience means offloading all technical decision-making, allowing users to focus solely on their desired outcomes. The presentation redefines autonomy beyond just long runtimes, emphasizing the importance of scoped tasks and robust verification to build powerful and stable agents.

This talk declares war on "slop," defined as low-quality, inauthentic, or inaccurate content, whether generated by humans or AI. The speaker argues that while AI has advanced rapidly, there's a growing problem of low-taste output that dilutes genuine progress. The core thesis is that the AI engineering community must actively combat this trend by prioritizing quality, taste, and accountability in AI-generated content and code.

This talk details the journey of The Browser Company in evolving from their initial browser, Arc, to their AI-native browser, Dia. It emphasizes the critical lessons learned in building AI-powered products, focusing on optimizing iteration speed, treating model behavior as a craft, and integrating AI security as a fundamental aspect of product development. The core thesis is that embracing technological shifts with conviction is essential for company-wide evolution, not just product updates.

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 proposes a shift in how engineers are compensated, moving away from traditional hourly or salary models towards a system that directly incentivizes productivity and the adoption of AI tools. The core idea is to pay engineers based on completed work units, similar to how salespeople are compensated based on sales, to encourage faster and more efficient delivery of high-quality code. This approach aims to unlock greater potential by aligning compensation with the increased capabilities offered by AI.

This talk, delivered by Alex Lieberman at the AI Engineer Code Summit 2025, serves as an introduction to the event. Lieberman, co-founder of 10x.co, an AI transformation firm, frames the summit as a look back at the year's advancements in AI and a tactical view of future directions. The event aims to bring together diverse perspectives from leading AI labs, startups, academics, consultants, and major brands.

The Code World Model (CWM) project aims to build AI models capable of reasoning, planning, and decision-making, using code as a constrained environment for developing these capabilities. Instead of solely focusing on code syntax, CWM models program execution more explicitly by predicting program states and transitions. This approach seeks to bridge the gap between traditional world models and large language models, enabling more efficient agentic reasoning through simulated execution.

This talk explores the use of AI for generating and optimizing low-level computational kernels, which are crucial for the performance of complex AI workloads. The core idea is to leverage AI to automatically port and optimize these kernels for diverse hardware, addressing the scarcity of human experts in this specialized field. While AI shows promise in tasks like kernel fusion and translating optimizations, it's not a replacement for human expertise in highly complex algorithmic advancements.

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 introduces Google DeepMind's latest advancements, Gemini 3 Pro and Nano Banana Pro, highlighting their capabilities in building AI-powered applications. The core thesis is that these models empower anyone to build software by simplifying complex tasks through intuitive interfaces and powerful AI features, enabling a new generation of engineers to create at an unprecedented scale.

This talk explores the evolution of evaluating AI models for coding tasks, from simple code snippets to complex codebases. It highlights challenges like data contamination and brittle test suites, proposing dynamic evaluation sets and LLM-based judges to ensure reliable and relevant assessments as AI capabilities advance. The discussion covers various stages of coding evaluation, emphasizing the need for benchmarks that reflect real-world performance and adapt to the rapid progress in AI.

This talk introduces the concept of proactive AI agents, moving beyond reactive tools that require explicit user commands. The core thesis is that AI agents should act as collaborators, anticipating developer needs and handling tasks autonomously to reduce mental load and increase productivity. This shift aims to free developers to focus on creative and high-level problem-solving rather than managing agent workflows.

This talk argues that the rapid advancements in AI necessitate a fundamental shift in software development operating models, moving beyond traditional Agile methodologies. While AI tools offer significant individual productivity gains, realizing broader organizational value requires addressing bottlenecks in collaboration, code review, and work allocation. The presenters propose a transition to AI-native workflows and roles, emphasizing smaller teams, continuous planning, and spec-driven development to achieve substantial improvements in delivery speed and quality.

This talk argues that the effectiveness of AI coding agents is primarily determined by the underlying model's capability, not by complex engineering scaffolds. Frontier models, when unhindered, can outperform many agent combinations. The core message is to simplify agent engineering and focus on improving model training through robust benchmarks and reinforcement learning environments derived from real-world coding tasks.

This talk addresses the gap between the hype surrounding AI code generation and the reality of its impact on software quality. While AI tools can significantly boost productivity in writing code, they introduce new challenges related to quality assurance, security, and maintainability. The core thesis is that achieving substantial, reliable gains requires moving beyond basic code generation to implement robust, agentic quality workflows throughout the entire software development lifecycle.

This talk explores the impact of AI tools on software engineering productivity, questioning whether current enterprise adoption is driven by genuine ROI or hype. Research spanning two years and multiple companies uses a machine learning model, trained on expert evaluations of code commits, to measure productivity gains. Initial findings suggest a median productivity increase of around 10% for AI-using teams, but with a widening gap between top and bottom performers, highlighting the need for companies to understand their position in this trend.

This talk explores scaling AI research not just through increased data and compute, but by making the practice of AI research more accessible. It introduces the concept of "environments" as a key abstraction for experimentation, akin to web applications for AI research, enabling broader participation beyond large labs. The discussion highlights how these environments facilitate model customization, evaluation, and the development of more effective AI systems.

This talk introduces an efficient reinforcement learning (RL) approach for training large language models, focusing on making the process faster and cheaper for enterprise use cases. The core idea is to break the synchronous dependency between sampling data and training the model, enabling asynchronous operations. This method aims to improve ROI by moving AI beyond simple productivity tasks into real-world automations.

This talk proposes a shift from building standalone AI agents to developing modular "skills" that agents can utilize. The core argument is that while agents possess intelligence, they often lack the specific domain expertise required for real-world tasks. Skills, defined as organized collections of files containing procedural knowledge and tools, offer a more composable and expertise-driven approach to enhancing agent capabilities. This paradigm aims to make AI more accessible and adaptable by packaging expertise into shareable units.

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 explores the architecture and development of coding agents, emphasizing the crucial role of the "harness" – the interface layer that connects models to users and tools. It highlights the rapid evolution of AI models and the challenges in adapting agents, proposing that building models and harnesses together, as exemplified by OpenAI's Codeex, offers a more robust and future-proof approach. The discussion also touches upon emerging patterns for integrating these agents into various products and workflows.

This talk outlines Anthropic's platform evolution to empower developers in building advanced agentic systems with Claude. The core thesis is that by providing tools to harness Claude's capabilities, manage its context window effectively, and grant it access to computational resources, developers can achieve peak performance in their AI-driven applications. The platform aims to enable agents to operate more autonomously within secure, sandboxed environments.

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.

Anti-gravity is a new AI developer platform from Google DeepMind designed with an agent-first approach. It integrates an editor, a browser, and an agent manager to provide a cohesive environment for AI-powered software development. The platform aims to leverage advancements in AI models, particularly in areas like reasoning, multimodal capabilities, and tool use, to enable more ambitious and complex agentic workflows.

Cursor Composer is an AI model designed for real-world software engineering, aiming to balance speed and intelligence. It performs better than top open-source models and rivals frontier models in intelligence, while being significantly more efficient in token generation. The development focused on creating a model that could serve as a daily driver for coding tasks, integrating features like parallel tool calling and semantic search to enhance its capabilities.

This talk introduces Meta AC, a novel framework designed to optimize AI agents by orchestrating multiple adaptation strategies beyond single-dimensional approaches. It addresses limitations in existing context engineering methods by employing a meta-controller that dynamically selects and allocates strategies based on task complexity, uncertainty, verifiability, and resource constraints. This multi-dimensional adaptation aims to improve agent performance, robustness, and efficiency across various tasks.

BacklogMD is a terminal-based Kanban board designed for managing tasks with AI agents and humans. It addresses the common issue of AI agents going off-track or running out of context by breaking down large features into smaller, manageable Markdown tasks. This approach facilitates clearer communication, better scope definition, and a more robust review process for AI-assisted development.

This talk draws parallels between building self-driving cars and developing AI agents, emphasizing that agents, like robots, operate in complex environments and require more than just sophisticated models. The core thesis is that the 99% of work beyond the AI model—encompassing infrastructure, tooling, feedback loops, and robust offline stacks—is crucial for reliable agent development and deployment.

This talk presents a vision for achieving zero software bugs by drawing parallels with highly reliable systems in industries like aerospace. It argues that while current AI coding agents introduce new challenges, established techniques for building robust software can be adapted and scaled, potentially making high-assurance code significantly more accessible and affordable. The core thesis is that the path to widespread adoption of AI in software development hinges on solving the quality and reliability problem.

This talk addresses the fundamental challenges AI engineers face in deploying models, moving beyond the hype of voice agents and MCP tools. The core thesis is that building a Python compiler to generate self-contained binaries offers a simpler, more standardized approach to running AI models anywhere, from local devices to the cloud, without extensive infrastructure changes. This method aims to replicate the ease of using models like OpenAI's API but with the flexibility of any open-source model.

This talk introduces Command Code, a coding agent designed to learn and adapt to an individual developer's unique coding style and preferences, referred to as "taste." Unlike standard AI coding assistants that provide generic code, Command Code aims to internalize a developer's decision-making process, preferences for tools, and architectural choices. This is achieved through a meta-neuro-symbolic reinforcement learning approach, creating a more personalized and efficient coding experience.

This talk introduces Pydantic AI's integration with durable execution frameworks like Temporal, addressing the challenge of maintaining state and preventing data loss in long-running AI agent workflows. It demonstrates how these frameworks enable agents to recover from failures and resume execution without starting from scratch, significantly improving reliability for complex tasks. The presentation also touches upon Pydantic Evals for model evaluation.

This talk introduces enterprise deep research as a powerful application for AI agents, focusing on its ability to conduct in-depth, multi-step investigations within private enterprise data. It highlights the challenges of factual accuracy in generative AI and presents an agent operating system designed to mitigate hallucinations and ensure robust information retrieval for enterprise-grade deployments.

This talk analyzes data from 20 million pull requests across 200,000 developers to reveal real-world trends in AI adoption and its impact on software engineering. It highlights that while interactive AI coding tools are seeing widespread adoption and significant productivity gains, fully autonomous agents are still in early stages. The data suggests that code architecture plays a crucial role in realizing AI-driven productivity benefits.

This talk argues that AI copilots for tech architecture represent the highest ROI use case currently underutilized by organizations. While coding copilots have become standard, an architecture co-pilot can prevent costly mistakes by ensuring development efforts are directed correctly from the outset. This approach aims to move beyond tribal knowledge and gut instinct, providing data-backed guidance for architectural decisions.

This talk introduces a system where an AI coding agent monitors application infrastructure for issues and automatically generates pull requests to fix them. Instead of just alerting developers to problems like memory leaks or high error rates, the system aims to detect, diagnose, and propose solutions, significantly reducing manual debugging and speeding up the resolution process. The core idea is to shift from reactive alerting to proactive, automated self-healing infrastructure.

This talk introduces context platform engineering as a method to reduce token anxiety and improve AI agent performance. The core thesis is that by optimizing how context is managed and cached, developers can significantly increase KV cache hit rates, leading to more efficient and cost-effective AI systems. The presenters announce the open-sourcing of their context platform engineering toolkit, designed to help engineers achieve these optimizations.

This talk explores context engineering as a method to enhance AI applications by moving beyond simple prompt engineering. It emphasizes the importance of providing AI models with dynamic, curated, and structured context to improve signal over noise, leading to more relevant and reliable outputs. The core thesis is that by treating AI development as information architecture, engineers can achieve superior results and gain greater control over AI behavior.

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 explores a method for integrating sub-agents into the CodeX CLI, enabling users to leverage existing workflows with alternative tools. The core idea is to create a wrapper script that manages the execution of child CodeX processes as sub-agents, allowing them to perform tasks and return results to the parent session. This approach aims to overcome the limitations of being locked into a single tool or model family while retaining the benefits of sub-agent context management.

This talk argues that despite the rapid advancements in AI, the fundamental principles of building successful products remain unchanged. The speaker contends that AI does not inherently solve the core challenges of product development, such as capturing user attention, guiding them to a product's value, and retaining them long-term. Instead, these critical aspects still require human creativity, strategic thinking, and relentless execution.

This talk introduces Z.ai's GLM 4.6 model series, highlighting its advancements in both language and multimodal understanding. The series has achieved over 100 million downloads across various platforms, indicating significant community adoption and contribution. GLM 4.6 demonstrates strong performance on public benchmarks, rivaling and sometimes surpassing commercial models, particularly in coding and reasoning tasks.