A new generation of artificial intelligence is transforming how software is conceived, tested and delivered across financial services.
From automating regression testing to managing complex CI/CD pipelines, agentic AI, systems that can make autonomous decisions within defined parameters, is rapidly becoming a core component of the software lifecycle.
For banks and insurers navigating strict compliance regimes and the constant demand for faster releases, this shift represents both a challenge and a competitive advantage.
According to Jyothish R, chief technology officer and global delivery officer at Bangalore, India-based AIMLEAP, “the rise of agentic AI in software development has surpassed all previous revolutions in scope and speed.”
Unlike traditional AI assistants, agentic AI doesn’t simply respond to prompts, it takes initiative within defined boundaries.
“Agentic AI systems act autonomously,” said Jyothish, adding “they don’t just answer queries; they decide, act and iterate.”
In practice, this means AI can now generate, test and refactor code, manage CI/CD pipelines, and even optimise infrastructure with minimal human input.
“Agentic AI is not the death of software engineering careers.”
– Jyothish R
The transformation has been gradual but decisive. Jyothish noted that early tools like GitHub Copilot and large language models such as GPT-4 “showed the potential for full-stack code generation,” but the technology has since evolved into something more profound.”
He added: “Agentic AI platforms not only generate code but also manage product backlogs, integrate APIs and execute test suites autonomously.”
For QA and software testing teams in banking and financial services, where compliance, reliability and delivery speed are non-negotiable, the implications are significant.
Jyothish pointed out that “in my experience, we’ve already crossed the 30 per cent automation threshold in many mid-sized teams. For some projects, I’ve seen productivity gains closer to 50 per cent.”
From validation to supervision
The shift, Jyothish said, goes beyond productivity metrics. “Traditional metrics like ‘lines of code’ or sprint velocity quickly lose meaning in an AI-augmented workflow,” he explained.
“What matters now are outcomes—how fast features are delivered, how reliably they perform and whether they meet customer expectations.”
In one case, he recalled, integrating an AI development agent into a legacy insurance platform reduced delivery time from six months to just 10 weeks.
“That shift didn’t just cut costs; it accelerated the company’s ability to launch a new product line and stay competitive,” Jyothish said.
The evolution also demands a cultural rethink. “The hardest part wasn’t technical, it was cultural,” he added. “Developers worried about being replaced, when in reality, their responsibilities evolved into higher-value tasks: designing architectures, improving UX and aligning technical direction with business outcomes.”
Human–AI partnership
Concerns around security, quality and control remain, but Jyothish believes these echo earlier transitions such as the move to cloud computing.
“We worried about data breaches and vendor lock-in,” he said. “Ultimately, the benefits outweighed the risks.”
By 2026, he predicts, the most effective software teams will combine human oversight with AI execution. “Agentic AI won’t replace developers,” he emphasised.
“It will redefine what it means to be one. The best teams I’ve seen are not AI-driven or human-driven, but AI-augmented.”
As the QA landscape continues to evolve, Jyothish sees agentic AI as “the new standard” for modern software engineering. “This is not the death of software engineering careers,” Jyothish concluded.
“If anything, it’s their evolution, the seamless partnership between human creativity and machine precision.”
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Looking for more news on regulations and compliance requirements driving developments in software quality engineering at financial firms? Visit our dedicated Regulation & Compliance page here.
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