Coding agents can generate thousands of lines of code in minutes. The problem: most of it can’t be deployed. It breaks internal standards, fails compliance checks, or creates more cleanup work than it saves.
“You can generate a ton of code, but it doesn’t mean really anything, right? It’s got to be code that is integratable, that is compliant, and you don’t want to create more work on the back end just because you sped up the code generation process on the front end,” said Stephen Newman, EY Global CTO Engineering Leader.
EY’s product development team solved this by connecting coding agents to their engineering standards, code repositories, and compliance frameworks. The result: 4x to 5x productivity gains across teams building EY’s suite of audit, tax, and financial platforms.
But the gains didn’t come from just turning on a tool. Newman’s team spent 18 to 24 months building the cultural foundation and technical integrations that made semi-autonomous coding work at scale.
The first step was cultural. EY started with GitHub Copilot-style tools, letting engineers get comfortable with prompt engineering and assistive AI. Newman said the key learning was making AI adoption organic rather than forced from leadership. “It’s important to bring AI capabilities as a ground-up organic adoption rather than force them onto the users,” he said.
Developers wanted to move beyond code generation to building, deployment, and operationalization. But productivity gains plateaued without deeper integration.
Newman realized agents needed access to EY’s code repos, engineering standards and source catalogs to generate deployable code. Without that “context universe,” as Newman calls it, agents produce generic output that requires extensive rework.
EY evaluated multiple agent platforms: Lovable, Replit and Factory’s IDE-based Droids. Rather than mandate a tool, Newman’s team measured adoption, usage and productivity across all three.
“We didn’t want to be too prescriptive as a leadership team to identify a tool and dumb it down,” Newman said. Developers “really gravitated and navigated” to Factory, which became the signal that it delivered real value.
Factory adoption “took off like wildfire” once elevated from evaluation to pilot. EY had to throttle traffic to Factory and Droids and restrict which repos could connect before getting compliance and security sign-off.
The workload classification framework
The enthusiasm from developers made it clear EY needed discipline around which workloads to delegate to agents. Newman’s team separated tasks into two categories:
High-autonomy tasks agents handle well:
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Code review
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Documentation
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Defect fixing
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Greenfield features
Complex tasks that still need human oversight:
EY also shifted developer roles. Rather than writing all code themselves, engineers became orchestrators directing agents to the correct databases and repos.
With security guardrails in place and integration into code repositories complete, EY measured efficiency gains ranging from 15% to 60% across different personas in the early adoption phase.
“There’s a leap that we’ve made on many of our products where we jumped on what I call horizon model development, where we have semi-autonomous agent execution at scale, a team of orchestrators as opposed to doers and we have the integrations into the context universe,” Newman said.
Newman acknowledged it’s difficult to attribute the 4x to 5x productivity gains solely to coding agents. The improvements came from trial and error combined with cultural and behavioral shifts in developer teams.
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