Programme topic
Coding agents
Coding agents are moving from autocomplete into planning, implementation, review and delivery. These talks examine the harnesses, codebase design, tests, skills and controls that let them contribute without simply producing more code faster.
13 published talks
Talks about coding agents
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…
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…
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…
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…
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 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…
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…
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…