Restive named a finalist for Best Use of Agentic AI at the Australian AI Awards 2026
Melbourne-based Restive is a finalist for Best Use of Agentic AI at the Australian AI Awards 2026. Here is the AI software factory behind the entry and what we learned
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Key takeaways
- Restive, a Melbourne-based AI software delivery consultancy, is a finalist for Best Use of Agentic AI at the Australian AI Awards 2026.
- Our entry covers an AI software factory: AI agents embedded across a client’s whole software delivery lifecycle, from requirements and testing through to release and documentation.
- The point of the work was end-to-end delivery speed, so the gains from AI coding tools carry all the way through to production.
- Winners are announced on 30 October 2026 at the Hyatt Regency Sydney.
We’re thrilled to share that Restive has been named a finalist for Best Use of Agentic AI at the Australian AI Awards 2026, run by Momentum Media, with winners announced at the gala at the Hyatt Regency Sydney on Friday 30 October.
It’s a category we care a lot about, because agentic AI in Australia is still mostly being talked about in pilots and demos, while the harder question of what happens when AI agents go to work inside an engineering team every day gets far less airtime. As a Melbourne-based consultancy that spends most of its time on AI software delivery, that question is pretty much our whole job, and it’s what our entry was about.
What problem does agentic AI solve in software delivery?
If your team has rolled out AI coding tools over the past year, you’ve probably seen the same thing we kept seeing with clients. Code gets written faster, sometimes a lot faster, and then it lands in a testing, documentation and release process that hasn’t changed at all, so the bottleneck simply moves downstream.
We wrote about this in the AI productivity paradox, and it’s the reason a lot of AI investment struggles to show up in delivery numbers. The speed dashboard looks great while the business is still waiting just as long for features to reach customers.
Usually the gain is real but contained to one part of the lifecycle, because adoption has been individual. Different engineers use different tools in different ways, so value gets created in pockets instead of as a consistent practice across the team. Meanwhile requirements are prepared the same way, regression scope is decided the same way and work moves into UAT the same way it always has.
So when one of our enterprise clients asked us to help them get more out of the AI tooling they were already paying for, we started with everything around the code.
When code gets written faster and nothing else changes, the bottleneck moves to testing and release. That’s where agentic AI earns its keep.
What is a software factory?
A software factory is a way of organising software delivery as a repeatable, measurable production line, with standard workflows, shared tooling, automated quality gates and clear handoffs from idea to production. The term has been around for decades, and the idea is simple enough: make delivery predictable so a team can ship more often with fewer surprises.
An AI software factory (sometimes called an agentic software factory) takes that same model and puts AI agents to work at each stage. Agents check requirements, draft tests, generate test data, prepare releases and write documentation, while engineers set the standards, review the output and make the calls that need judgement.
The difference from simply handing your team an AI coding assistant is scope. A coding assistant speeds up one person at one step, while a software factory changes how work flows through the whole lifecycle, which is where the bigger and more durable gains tend to sit.
How agentic AI works across the software delivery lifecycle
For this engagement we put a ring-fenced squad inside the client’s engineering team and worked alongside their CTO to build an AI software factory on live production work. Here’s what changed at each stage.
1. Requirements readiness
Work items are assessed by AI for ambiguity, missing context and incomplete acceptance criteria, producing a readiness score. Where an item falls short, the tooling uses business, technical and codebase context to resolve what it can, and anything left over goes back for clarification before development starts. This cuts down on false starts and gives AI-assisted development much better material to work with.
2. AI-assisted engineering
Engineers use the client’s approved tooling to speed up implementation, and the squad sets practical patterns for which work suits agentic execution, how it should be structured and what checks it needs before it moves on.
3. Developer verification
Using the requirement and acceptance criteria as context, AI drafts test scenarios, API requests and test data that run before work reaches QA. QA then receives work that’s already been structurally verified, and can spend its time on regression and higher-risk areas.
4. AI-augmented QA and test data
Regression scope is worked out from the code that changed, with much less reliance on what individual testers happen to know, and where the right test data doesn’t exist, AI helps define the conditions and generate representative data so testing isn’t blocked halfway through.
5. UAT, release and documentation
Each tester gets their own UAT sandbox, which removes a lot of the contention of a shared environment, and CI/CD automation cuts down manual release steps. A documentation agent summarises commits and writes them back to Jira automatically, so the record of what changed stays current without anyone having to chase it.
None of these pieces is especially flashy on its own, and that’s sort of the point. Agentic AI earns its keep when it’s wired into the repetitive parts of delivery that quietly eat a team’s week, with a human approving the plan at each step (our lead software engineer Gimyong wrote up how that human-in-the-loop pattern works in practice).
What we learned along the way
Context matters more than the model
The quality of what an AI agent produces depends heavily on the quality of what you give it, which is why requirements readiness ended up being one of the highest-value changes, even though it’s the least glamorous.
Measure from day one
We agreed a baseline with the client before delivery started, covering cycle time, defect escape rate, test coverage and release frequency, so the conversation with the board could be grounded in evidence. If you’re heading into that conversation yourself, we’ve written about how to talk to your board about AI without overpromising.
Start small and ring-fence it
A contained scope on live production work meant results could be attributed to the new way of working, and if something wasn’t landing, we found out early and cheaply.
Bring the client's team with you
The client’s engineers and QA team had already started experimenting with AI, and our job was to make that consistent across the squad so the capability stays with them once we step back.
What to look for in an AI software delivery consultancy in Australia
If you’re weighing up partners for AI delivery, whether in Melbourne, Sydney or anywhere else in Australia, these are the things worth checking for, because they’re what tends to show up in teams who’ve done this in production:
- They start with your delivery lifecycle. Ask where they think your bottleneck sits today and where it will move once code gets faster.
- They embed engineers in your team. AI delivery capability is built inside the team doing the work, alongside your people, so it sticks once the engagement ends.
- They measure against a baseline. Cycle time, defect escape rate and release frequency, agreed up front, so you can show a return.
- They work with the tools you already pay for. Most enterprise teams already have AI tooling, and the quickest return usually comes from using it consistently across the lifecycle.
- They can point to live production work. Pilots are easy, so ask what’s running in production and what changed as a result.
Why does this matter if you lead a tech team?
Most Australian tech leaders we talk to have already done the hard bit, which is getting their people to use AI tools. What’s often missing is the step from individual productivity to a delivery model that’s built around AI from end to end, and that’s usually where the return on the investment sits. It’s the work our delivery and product teams do every day out of Melbourne.
It’s also how we run Restive itself. We’re a member of the Anthropic Claude Partner Network, and we run our own internal AI agents across marketing, sales and operations through Agent Restive, so we’ve felt the same friction our clients do.
If your team is writing code faster but your release dates haven’t moved, we’d love to have a chat about where the bottleneck has gone and what it would take to shift it. Get in touch with the Restive team.
Frequently asked questions
What are the Australian AI Awards?
The Australian AI Awards are run by Momentum Media and recognise organisations doing standout work with artificial intelligence across Australia. The 2026 winners are announced at a gala dinner at the Hyatt Regency Sydney on 30 October 2026.
What does "best use of agentic AI" mean?
Agentic AI refers to AI systems that can plan and carry out multi-step tasks with some independence, using tools and context to get work done, usually with a human approving key decisions. The category recognises organisations putting that capability to practical use.
What is a software factory?
A software factory is an approach to software delivery that treats it as a repeatable, measurable production line, with standard workflows, shared tooling, automated quality gates and clear handoffs from idea to production, so teams can ship more often and more predictably.
What is an AI software factory?
An AI software factory, also called an agentic software factory, applies AI agents at each stage of the software delivery lifecycle, including requirements checks, test generation, test data, release preparation and documentation, while engineers set standards and review the output.
How is agentic AI used in software delivery?
In software delivery, AI agents can assess requirements, generate test cases and test data, work out regression scope, automate release steps and keep documentation up to date, so the speed gained from AI-assisted coding carries through to production.
How do I choose an AI software delivery consultancy in Australia?
Look for a consultancy that starts with your whole delivery lifecycle, embeds engineers inside your team, measures results against an agreed baseline, works with the AI tools you already pay for and can point to agentic AI running in live production.
Does Restive help Australian companies implement agentic AI?
Yes. Restive is a Melbourne-based tech consultancy that embeds engineers and AI specialists inside client teams across Australia to design and deliver agentic AI and AI software factories across the software delivery lifecycle. Find out more about our data and AI services.
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