Programme topic
Software engineering
AI changes more than how code is generated. These talks look at the codebase, architecture, testing, review, delivery and team practices required when agents participate across the software lifecycle.
18 published talks
Talks about software engineering
Your spaghetti code has an invoice now: what complexity does to coding agents
Artem YakimenkoEngineering Director, Site Reliability, Culture Amp
We've told engineers for decades that high complexity makes code harder for humans to reason about. It turns out it makes code measurably more expensive for agents too. Unlike human frustration, this shows up directly on your API bill. I show and discuss my…
Compounding lessons into skills: How we performed a large code migration using AI (with no new bugs!)
Tom IslesStaff Software Engineer, Canva
Canva's "Ingredient Generation" service powers all of our media generation experiences. Every image, video, audio and 3d object request for generation goes through this service, which offers 50+ AI Models. One of its core capabilities is the ability to…
The BAML Programming Language
Vaibhav GuptaCEO, Boundary
Whether you like it or not, it’s no longer possible to read all the code that’s generated by a model. The solution cannot be, "Just ship it," or you end up with slop everywhere. Neither can the solution be "Read everything," because that’s impractical and…
209 ports in six days, in a language the model barely knew
Burin ChoomnuanPrincipal Engineer/Team Lead/AI Engineer, NewsCorp Australia
Jolt and jank are two young Clojure implementations, one running on Chez Scheme and one compiling to native code through C++/LLVM. Between them they have almost no public code for a model to have learned from. Ask Claude for jank and it confidently writes JVM…
AI Janitor: Making Architecture Review Executable for Coding Agents
Dave CurrieAI Engineer, Square Peg
AI coding agents accelerate code generation, but they also accelerate architectural drift, regressions and false confidence. While building a multi-service production platform with only two of us, I found that a normal human review loop could not keep up with…
Dependency hell is back. This time it's your agent's config.
Jack RudenkoChief AI Officer (CAIO), 10X Labs
Everyone has an agentic harness now. Ours is not special. What nobody has solved is running one across a whole team without every engineer drifting into a private setup. We run Claude Code across 50 engineers and dozens of client codebases at 10xlabs. Within a…
Your Coding Agent Is Fast. Your Codebase Is the Bottleneck.
Shrey SomaiyaPrincipal Engineer - Jira Frontend Platform, Atlassian
AI agents can write code faster than teams can land it. Jira’s frontend codebase contains over 15 million lines of code, supports thousands of contributors, and doubles in size roughly every two years. At this scale, verification, slow CI, defense against…
Why software factories can't be trusted (and how the Systems Engineering V-model helps)
Mark JohnsonCEO, Cardiobase
Coding agents will do anything to get to a PR. Ours marked tasks complete that didn't exist, wrote "next steps: verification" under unreviewed pull requests, and quietly skipped every boring stage between the ticket and the diff. In our world, that isn't a…
Should You Build a Software Factory? Tales from an Open-Source Maintainer
Harlan WiltonOpen-source developer, Self Employed
Coding agents helped me write code faster, but I kept running into problems elsewhere in my engineering workflow. Tests passed without proving much. Parallel tasks changed the same files. Reviews piled up. This talk follows the changes I made while building a…
Trust is engineered, not granted: why we focus on verifying before background coding agents
Vivek KatialEngineering Lead, Applied AI, Heidi Health
Heidi is an AI scribe used by 130K clinicians a week. Our 150 engineers ship 100+ PRs daily into prod, and AI made writing code so cheap that review became the bottleneck: our P75 review wait was 14 hours, almost all of it queue time. A 14-hour queue is a…
It's time for a new kind of software
Rupert ManfrediHead of Design, Telepath
Applications package a predetermined interface, data model and set of capabilities around somebody else’s idea of a task. Generative models can now produce software on demand, but generative UI today either sits atop the existing application stack or replaces…
Don't stop, won't stop. Automated verification of long-running agentic loops
AJ FisherVP Digital Science, Tetratherix
Long-running agentic loops create different engineering problems than code generation. Once agents work unattended for many hours, iterate through 20 or more review cycles, and build stacked PRs towards a larger goal, the challenge changes to keeping that…
The CLI is dead, long live the CLI
Jan Peer StöcklmairSenior Software Engineer, Sentry
Coding agents didn't kill the CLI. They became its most demanding users: they can't see a spinner, stall on prompts they can't answer, and treat error messages as instructions. So we rewrote Sentry's CLI from scratch for humans and agents. This talk is a…
From REST to Agentic: Trusted APIs in the age of AI
Leigh WhitingPrincipal Engineer, Atlassian
Enterprise platforms don't get to start from scratch. When AI agents need to act on behalf of users, they inherit the API surfaces those users already depend on — surfaces evolved over many years for human-driven workflows, not autonomous reasoning. This talk…
My Agents broke APIs - Fixing Multi-Agent Systems with MCP
Anannya Roy ChowdhuryGenAI Developer Advocate, AWS
Modern AI agents struggle not because of reasoning limits, but because of interaction with tools on interfaces designed for humans. In agentic systems, this mismatch leads to incorrect tool selection, redundant calls, increased latency, & weak workflows that…
AI Sandboxes: Running Coding Agents Safely in Production-Grade Environments
Shivay LambaLead Machine Learning | Developer Experience Engineer, Qualcomm
The number of cyber attacks and security risks related to Coding Agents has sky rocketed. AI coding agents like Claude Code, Codex CLI, and Gemini CLI don’t behave like your typical developer tools. They install system packages, modify configurations, delete…
The Model Wasn't the MOAT: How 10 Design System Engineers Turned Platform Knowledge into Enterprise AI
Sudharsanam NarasimhanSenior engineering manager, Design Systems & AI Developer Platforms, Atlassian
Most enterprise AI strategies begin with models, central AI teams, and a list of potential use cases. We started somewhere else: with a platform organisation that already shaped how more than 2,000 frontend engineers built software. A team of 10 engineers…
Shipping together in an AI native team
Sandra AratoSenior Software Engineer, Canva
While building an AI agent for marketing workflows at Leonardo.Ai, we discovered that the traditional design-to-engineering handoff was failing. A static mock could describe a happy path, but not how an agent would group assets, select tools, recover from…