Organizations face a significant challenge in balancing the rapid pace of AI innovation with the critical need for risk management and trust. While projections for AI investment are high, with some estimates suggesting it could account for 10-12% of technology budgets, a more cautious and critical approach is necessary.

Andy's talk will explore why firms should resist the pressure to blindly adopt AI and instead treat it like any other new technology investment. We'll discuss the importance of being agile while also maintaining a strong focus on managing risk, building trust, and critically evaluating the real-world implications of AI implementation before committing significant resources and how to differentiate ourselves, beyond the GenAI hype.

While we, as individuals, recognise our limitations and biases, what about our AI systems?

With the expansion of AI technologies and algorithms into fields like UI personalisation, are we prepared to advance further? Or are we risking a widespread failure?

In this talk, we'll be looking at issues with algorithmic user profiling and how this can lead to marginalisation and poor user experiences and what to do about it.

AI coding agents feel like magic... right up until they collide with production code.

For teams maintaining legacy systems, these agents often hallucinate APIs, run off on tangents, and shatter trust faster than an unreviewed hotfix at 5pm. Ignoring the past won't save us, because new code becomes old!

We can do better, and we will. In this session we'll go over emerging strategies for improving the accuracy of coding agents on real codebases, benchmarks such as SWE-bench that evaluate our progress, and their limitations. Expect to walk away with actionable techniques and a renewed respect for code that came before us and the challenges ahead.

Let's build an Exploratory Tester with AI. We have the ability to harness AI in ways we never could before. You have code, projects, or websites that you need to discover potential issues, blind spots, or even vulnerabilities in. Can you have AI do that? Can you build things that find things that you don't even know you don't know? Let's explore it together.

AI agents are everywhere, but how do you go from demo to dependable? In this talk, we’ll build agents using LangGraph, and take it beyond toy examples into enterprise-grade readiness.

We’ll start with context engineering concepts like: control flow, prompt engineering, prompt management and tool calling. From there, we’ll build an agent step-by-step, enabling contextual reasoning via Retrieval-Augmented Generation (RAG), and integrating tool use for dynamic task execution. We'll also cover agent observability with open source observability tools such as LangFuse to bring full observability into the decision our agents make.

Whether you're exploring AI agents or planning your first production deployment, you'll leave this session with the patterns, tools, and clarity to do it right.

How can we expect LLM products to impact software engineering?

Join Chelsea Troy for an examination of how LLM products are built. She'll walk through the domain: what data LLMs are trained on and how they're trained, and spell out how those factors impact what LLMs tend to do well with and where they tend to struggle.

Then, we'll talk about how that affects the design of the products built around LLMs, like ChatGPT, Gemini, Claude, Copilot, or Devon.

Finally, take a tour with Chelsea through current research elucidating the impact of LLM product use on developer workflows, and dig into what that means for us as practitioners in an LLM-enabled world.

AI’s potential in software development extends far beyond code generation or chatbot-style assistants in the IDE. This talk explores how five families of AI capabilities (Learning & Onboarding, Coding Assistants, Automation & Agents, Planning, and Data Exploration & Synthesis) can transform the entire CI/CD lifecycle.

Whether it’s assessing processes, building pipelines, planning large-scale VM migrations, or summarizing an ocean of information, we’ll talk about how to identify high-impact integration points often untapped by chatbot or IDE based AI integrations. Centering AI integration around developers throughout the entire lifecycle of planning, coding, deployment, and monitoring unlocks greater ROI than simply generating more lines of code.

Overview of how the guild.host works.

An overview of Three.js and how to integrate it into a React app.

Multi-agent AI frameworks are everywhere right now—OpenAI Swarm, CrewAI, Langflow—but most of them are built Python-first. Meanwhile, a huge chunk of AI startups (60–70% in YC, by some counts) are building in TypeScript. And honestly, spinning up a whole Python backend just to leverage some of these frameworks didn’t feel worth it.

In this talk, I’ll walk through how we approached building a multi-agent AI system in TypeScript—by focusing on the core principles behind these frameworks and building something similar that fits the JS stack.