AI is reshaping software engineering, creating uncertainty about how engineering roles will change as increasingly capable systems take on work that was once performed by people. While no one can predict the future with…

Tools churn. Factories commoditise. When everyone has the same models, the advantage goes to whoever gives their agents the clearest blueprint of how the business works - what exists, how it connects and the rules it…

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…

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…

Every enterprise site we work on was built for a human with a browser. When an AI agent fetches the same URL it receives navigation, cookie banners, client-rendered components and marketing prose, then has to guess at…

AI sovereignty isn’t about where the GPUs sit. It’s about who can switch the intelligence off.

Australia is spending billions on sovereign AI infrastructure. But onshore compute running models subject to another…

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,…

You want your team using the best coding agent for each job : Claude Code, Codex, Cursor, or one you built yourself , without rewiring your setup every time a better model ships, and without each agent running loose…

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…

Canva hit mid-2026 with every AI metric a vendor could hand us and no answer to the only question the business actually cared about: is this working? Adoption flattened at around 85% of ~3,000 engineers, and the number…

The default advice is to add agents: specialised roles, an orchestrator, handoffs between them. We built that. It was a genuinely useful way to explore the problem space, and under real production traffic it was slow…

LLM providers sell you a 2-million-token context window like an elephant that never forgets. If you actually build production agents that way, your latency explodes, your retrieval drifts, and your CFO will shut you…

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,…

Long context solves within-session coherence, not cross-session persistence. Once an assistant starts storing memories, a quieter failure appears: its own inferences can be written back, retrieved in later sessions, and…

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…

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…

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…

Production AI problems are rarely observable through deterministic CI checks. They surface as user feedback, bad LLM-as-a-Judge scores, alerts, or even a vague sense that something is wrong. The evidence that explains…

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,…

We were running a production LLM pipeline that classified legal documents, and every change was a guess. Swap a prompt, change a model — better or worse? Nobody could say. The outputs looked plausible either way, and…

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…

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…

If you are sceptical about how evals drive real results, or looking for more depth than introductory tutorial videos, this talk is for you.

We'll show you how Lorikeet, an AI customer service startup, built an eval…

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…

Most production AI guidance assumes you want consistency: pin the prompt, lower the temperature, eval for drift. But a whole class of enterprise use cases - idea generation, recommendation, exploration, synthesis - is…

One agent that does everything is powerful, until it’s impossible to debug, eval or secure. Break it into a graph of specialised agents and you get control, until the handoffs leak, the graph jams, and the next model…

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…

In May I rebuilt our brand-strategy pipeline as a fully agentic system, replacing a chain of LLM calls with a managed agent. I compared old and new across 44 customers, and the new system won every time. But I was the…

Thirteen months building a custom state government full-stack application with zero code written by hand.

7,137 tests, 5,631 of them unit tests. It was a textbook testing pyramid but still bugs stacked up.

The…

Giving an agent a skill sounds straightforward: it loads a Markdown file, follows the instructions and completes the task.

Ours launched a long-running, paid process and told the agent to wait for the result. But…

In March I onboarded our first team member dedicated to AI Red Teaming. That was a month before the announcement of Claude Mythos Preview, and the open letters that followed from APRA in April and ASIC in May…

Over the past nine months we've built and deployed an AI-assisted credit assessment system that's now used in production to help credit assessors process home loan applications. More importantly, it was signed off by…

Making an AI agent vulnerable to exactly one attack is harder than making it completely secure, let alone doing it six different times.

In this talk, I'll describe the process of building an agentic AI capture the…

Teachers in Australian specialist schools spend two hours or more preparing a single lesson, then adapt it again for every ability group in the room. Many are already pasting student details into consumer chatbots to…

Giving an AI agent permission to change production infrastructure removes the human pause that made plan-and-apply safe. In this talk, Jeffrey Aven shows how to put that judgement back into the system itself.

He…

We spent three months rebuilding a regulated enterprise's development lifecycle around agents, then worked out how much of the governance we had added was doing real compliance work. Less than we assumed.

Across…

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…

Your Agents Aren't Failing at Prompting

Every agent demo works. Then you run ten thousand at once, and the failures have nothing to do with the model: dead tuples piling up, a sync HTTP client freezing the event…

Engineering teams are getting smaller. A team of four with strong AI tooling now ships what took twelve people two years ago, and the org chart has not caught up. The first layer to feel it is middle management - the…

Checkout AI answers open-ended questions about retail sales data in natural language. It plans,
calls analytics tools over MCP, executes Python in a sandbox, and returns a written analysis with
charts. In a system…

The Heatseeker AI chat answers data questions for marketers who make decisions with million-dollar budgets. Wrong answers or hallucinated numbers are not an option here, as you can imagine ;)

The fight against them…

About one in four conversations with WordPress.com's AI support bot ends with a person. In about half of those, the bot suggested it.

These are two different problems. In one, the user asks for a person. In the…

Agent teams should not need to file a ticket with a central evaluation team just to learn whether a new prompt, model, or tool made their agent better. At Zendesk, we developed and deployed a trace-first evaluation…

AI applications still need regular security patches and dependency updates, but those software changes can also affect the data processing, model interaction, and evaluation paths around an LLM. In our releases,…

"Good enough" AI now runs on a reasonably beefy laptop. Qwen3.8-27B benchmarks at an Intelligence Index of 52 - roughly the best money could buy back in February - and inferences happily on consumer kit. When tokens are…

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…

Your best engineer won't use AI tools. Your tech lead is using them but won't tell anyone. Your new grads don't understand what the fuss is about. And you, the leader, are supposed to have answers for all of them.…

Autonomous agents need autonomous protection. An agent that acts alone at 3am cannot wait for a quarterly pen test, a consultant's report, or a security engineer the team does not have. We started with the data. We…

Once a product reaches the wilds of production, all sorts of things can go wrong. Carefully logging and monitoring are one thing, but what if we could do better?

Bring new levels of code stability by recreating the…