AI Engineer Melbourne 2026 — Software Engineering (Day 1 Afternoon)

Over the last two years, our customer web traffic changed: today around 50% of visitors were unknown browsers and AI agents. The era of aligning with the traditional search engine crawlers with Core Web Vitals is shifting; the new challenge is feeding focused low-noise context to autonomous agents and Large Language Models (LLMs).

Traditional Search Engine Optimisation (SEO) relies on techniques such as keyword density, backlink tracking, and human-readable formatting, but when a significant part of your traffic suddenly becomes AI agents, how do you ensure your content is being parsed correctly by machines?

Our team will share the strategic and architectural shifts the organisations are facing to embrace this new web reality. This isn't about meta-tags; it's about re-architecting how a business presents itself and its content in the AI-driven internet, including

1. Identifying Agent Traffic: How to identify & separate agent traffic.

2. Realigning Context: Trade-offs in modifying traditional website structure vs. serving structured markdown, llms.txt files, and specialised API endpoints.

3. Omni Channel Content: Serving dual-experiences with web applications for humans versus data streams for agents.

4. Lessons Learned: To block or embrace agent traffic, and how embracing "LLM Instructions" can increase content's reach.

We've spent a decade codifying infrastructure, Terraform, Pulumi, CDK. This session explores what happens when you treat infrastructure as a queryable data layer: exposing cloud state, Skills giving agents reusable operational knowledge, and LLM gateways routing and governing model access.

You'll see how policy-as-code, using Cedar, becomes the guardrail, agents reason against, not just something humans enforce. Drawing from real builds, we'll break down the architecture of an AI control plane, LLM proxy, tool registry, Skills libraries, and governance controls and show how platform teams can give agents rich infrastructure context without giving away the keys.

StackQL provides a unified control plane data model for agents, tools, processes and humans to interact with. The StackQL MCP server exposes this unified interface to AI agents, allowing them to query, provision, and update cloud resources, including executing lifecycle operations.

In this session we will demonstrate agents performing multi-cloud inventory analysis, security posture queries, cost optimization, and provisioning. We'll discuss lessons learned building an MCP server against multiple stateful provider backends.

Every API you've shipped was designed for a human reading docs. Agents don't read docs - they load your entire tool schema into a context window every call, then burn tokens guessing which endpoint to try.

Take a standard accounting API — clean REST, solid docs, every endpoint you'd expect. Point an agent at it with one task: get an invoice status. Watch it burn through tokens, pick the wrong endpoints, and maybe even give up. I'll demo this failure live, then rebuild it into a handful of outcome-oriented tools — and the same query that 'failed' now works in a single call at a fraction of the cost.

The fix isn't adding more endpoints or better docs. It's rethinking what a "tool" means when your consumer is an LLM, not a developer. These design principles have emerged in teams building agent-facing APIs keep converging on the same patterns, and they look nothing like good REST design.

Your pipeline breaks at 2am. Nobody's watching. By morning, it's already fixed. That's not wishful thinking — that's agentic self-healing in production. In this talk, we'll explore how AI agents can monitor, diagnose, and autonomously recover failing pipelines without human intervention. We'll cover the patterns and architectures that make self-healing possible, the guardrails that keep agents from making things worse, and real-world lessons from building systems that fix themselves while you sleep.

At Canva, our support experience is powered by multiple AI systems, from real-time assistance to asynchronous ticket resolution that handles complex, multi-step workflows and escalates to humans when needed. In this talk, we’ll share how we took these systems from MVP to serving Canva’s 250M+ users, and the infrastructure we built along the way to get there safely.

We’ll cover how traces helped us debug complex agent workflows, how prompt management unlocked safe iteration through shadowing and localisation, and how we built continuous evaluation loops using LLM-as-judge, offline datasets, and human feedback, using tools like Langfuse alongside internal tooling we developed.

We’ll also share practical lessons from running experiments, replaying real support scenarios, and the things we wish we’d known earlier about scaling AI systems in production.