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Fabric is one of the first official SpaceXAI partners in Australia and across Asia Pacific & Japan. Agents do the building. Our engineers decide where they belong, and own what ships.
Official SpaceXAI partners in Australia and the APJ region.
Fabric Forward Deployed Engineers in Grok's certification.
lines of legacy code modernised in three months
Code completion was the small version of this. Agents now take the repetitive middle of delivery: scaffolding, migrations, test coverage, boilerplate. That gives engineers their time back for the work that needs judgement.
This isn't about replacing engineers. It's about amplifying them and pointing them at the work that actually matters.
The question was never whether to use agents. It's which judgements they take, which stay with a person, and who decided. Get that wrong in one direction and quality quietly drops. Get it wrong in the other and your engineers become a review queue: the same bottleneck, one step later and harder to see.
Cursor's multi-model platform lets agents take on the repetitive, high-volume engineering work — scaffolding, implementation, refactoring, tests and documentation — at a speed no manual team can match.
Our engineers decide which calls agents take and which stay with a person, then build the rules and workflows that hold that line when delivery gets busy.
Security, compliance and quality are engineered into the workflow from day one, not reviewed at the end. That's what turns a promising pilot into something a risk team will approve for production.
The result: the velocity of agentic development, with the assurance enterprise organisations can't operate without.
Fabric has been technology agnostic since day one. We choose tools that fit the problem in front of us, not the vendor we're closest to. Our engineers tested Grok on real codebases before we committed to it.
A model on its own is only part of the equation. Cursor makes models more productive by giving them the right context, tools and execution environment. In practice, that means we can get more useful engineering output from the same underlying model capacity.
Abhijeet Kesarkar · Head of Technology
Cursor treats agents as part of the engineering workflow, rather than a separate tool. We can start work locally, hand a longer-running task to a Cloud Agent, run several agents in parallel, and pull the work back into the IDE when we want to take control.
James Garner · Head of Engineering
By embedding agentic AI across engineering workflows — and governing it properly — we help organisations modernise the entire software delivery lifecycle.
Agentic workflows compress delivery timelines without trading away quality or control.
Agents absorb the repetitive build; your people focus on architecture, security and customer experience.
Security, compliance and regulatory readiness are built into the workflow from the start, not retrofitted before go-live.
A repeatable, agent-powered practice that scales across complex enterprise environments — instead of dying as a promising experiment.
The partnership standardises a practice we've been running on client work for a while.
An Australian waste management company's 20-year-old platform, modernised without the usual months of manual reverse-engineering. Agents mapped the estate and drafted implementations, 1,323 automated tests did the checking, engineers owned the architecture. Standing start to deployed software in three months.
For a Southeast Asian telecommunications provider building TM Forum-compliant APIs, we built an agentic workflow that turns user stories into structured test scenarios. Where a requirement is ambiguous, it flags the gap for a person instead of guessing. QE preparation and review fell from 1.5 weeks to 3 days.
If a working prototype takes days instead of months, the risk in a programme moves. It stops being can we build this in time and becomes are we building the right thing, a question no agent answers.
So we apply the same thinking to discovery. Transcripts become themes in hours, and concepts reach real users while they're still cheap to change. Every Fabric discovery pairs a product lead, a technical lead and a design lead, so the customer, technical and commercial questions get argued out at the same table.
The checks sit where they earn their place: when AI names a theme, it has to produce the quotes behind it. That's cheap, and it's exactly where a plausible-sounding insight falls apart. Reading every draft line by line is neither.
Every Fabric engineer gets the training and certification to use agents across the delivery lifecycle, and more room to spend their time on the problems that made them want to do this work.
Whether you're exploring agentic software development or modernising your engineering capability, we can help you unlock the full potential of AI-native delivery — safely, and at scale. Tell us where you're up to.
We reply within two business days to arrange a 30-minute conversation.
We walk through where agentic, AI-native delivery would move the needle for you — honestly, including where it wouldn't.
If it's a fit, we scope a starting point sized to your environment.
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