For decades, engineering teams treated code like a vintage Ferrari – expensive to build, painstakingly maintained and too precious to ever throw away. Every line represented a significant investment of human capital and time, and has led to a culture where code was cherished and its longevity was a marker of success.
But at the AWS Summit in London this week, Ryan Cormack, principal engineer at online used car marketplace Motorway, consigned that philosophy to the scrapyard. In the age of agentic artificial intelligence (AI-)driven software development, he says, engineering teams can become more productive and are able to build, revise and maintain code at speeds previously unthinkable.
In this article, we look at Motorway’s radical shift from manual coding to an AI-first development pipeline powered by AWS Kiro. Cormack talks about how the company achieved a 4x increase in engineering output, the challenges that come with the ability to produce more code, why the future of software development lies in treating code as disposable, and the core benefits of codifying organisational culture into AI steering files.
The mindset shift: Disposability vs polish
The most profound change at Motorway is speed of delivery but also a psychological break from the past. Historically, writing code was a “time-expensive process”, Cormack says, adding: “We wanted to have code that was so good that we could cherish it for years to come, because we had invested so much time into making it.”
But since starting to use Kiro – AWS’s agentic AI-capable IDE – that mindset became a bottleneck. “We shifted away from, ‘We need the most well-polished code for every line we write, all the time’, because we can rewrite it again tomorrow at a speed that’s never been possible before,” says Cormack.
This has led to a strategy of “evaluation over production”. Motorway now generates vast amounts of code – a million lines a month – much of which may never reach a customer, says Cormack. Instead, it is used to test and evaluate multiple different ways to solve a problem before committing to it.
The lesson for other organisations is clear. Don’t aim for a perfect first pass. Use AI to cycle through iterations, then use human expertise to refine exactly what you want from the options the AI helps provide.
Managing the ‘volume crisis’: Rigour over speed
While a 4x increase in output sounds like an engineering dream, it creates a real “review bottleneck”. If you write 400% more code but maintain 100% manual review processes, the system collapses. To combat this, Motorway hollowed out the “manual middle” of the development process and moved human energy to the ends of the process – namely, the spec and the review.
“We find ourselves spending more time planning code and the whole process up front, and a little bit more time reviewing what comes out,” Cormack says. “But we lose all this time in the middle where we previously had to manually write all the code.”
To ensure AI doesn’t just produce any code but “Motorway code”, the team utilises “steering files”. These files augment the AI’s system prompts with the company’s specific DNA. They are specific to Kiro and are markdown documents that contain instructions, standards and preferences to guide the AI behaviour and coding style.
They include, for example, naming conventions that standardise how application programming interfaces (APIs) are labelled across Motorway’s 7,500-dealer network, and design patterns that enforce specific software architectures.
By injecting these rules via the AI, generated code looks and feels like it was written by a veteran Motorway engineer.
And AI isn’t just used for the build; it’s used for the full lifecycle. “We need to use AI to help us debug, analyse, understand, and evaluate systems as they run,” Cormack adds, noting that agents now monitor logs and metrics to help humans manage a massive fleet of services.
The ‘Kiro’ engine and model agnosticism
A critical component of Motorway’s success is that Kiro acts as an agentic loop rather than just a simple “autocomplete” tool.
“Kiro knows how our CI pipelines work,” says Cormack. “It knows how our infrastructure is code-driven and it knows how our internal applications work together. It’s able to help guide us every step of the way.
“We’re using Kiro across our full software development lifecycle. Our product and UX teams can ship real prototypes into our customers’ hands quicker than we’ve ever been able to before. What would take weeks now takes hours.”
His team can leverage its model agnosticism too. Cormack explained they aren’t locked into a single LLM: “We use Kiro with Claude’s latest Opus 4.7 model, we use it with some of the open weight models, things like Meta’s Llama models … we’re able to selectively pick the LLM that we know is going to be able to best perform the specific task.”
This flexibility helps to mitigate the risk of hallucinations. Motorway relies on a spec-driven approach where the AI must think through the problem and generate a technical design before writing a single line.
“It will help us write automated tests that are able to prove that each of these points has been accurately done,” Cormack says. This means the AI provides its own proof of work before a human ever touches it.
Legacy transition from Heroku to AWS
Motorway wasn’t always this agile. The company was “born in the cloud”, on Heroku, which Cormack acknowledges was “great for scaling and getting going”. But as the company grew, it hit friction points.
The transition to AWS was driven by a need for “flexibility, adaptability, and scalability”, says Cormack, who views their Kiro-enabled AI-first pipeline as the ultimate tool for such transitions.
If he were to do things all over again, Cormack says he would “adopt this model of thinking much earlier on”. The ability to use AI to map migration logic and service dependencies would have saved months of manual effort during the move off their legacy platform, he believes.
Lessons for the boardroom
For organisations that want to replicate Motorway’s 250% increase in deployment frequency, Cormack warns against automating the grind of coding without also automating the rigour of testing.
“If you try to build just by writing code faster, it doesn’t solve the problems,” he says. “I don’t think our customers necessarily want code; they want features and functionality.”
The winners of the AI era won’t be the ones who write the most code, but the ones who build the most rigorous frameworks to manage its disposability.
As Cormack says: “Kiro’s now writing over a million lines of code for us every single month. So, before we start any new piece of work, our engineering team chooses Kiro to help understand exactly what it is that we want to build.
“The rigour at the start of this process helps enable the precision we want in our engineering at the end. So, every piece of work that we do starts with a spec, understanding the intent of what it is that we’re building and why.”