Endava, a global software contracting firm with engineers across Europe, the Americas, and Asia, has been an early adopter of Codex. For a business built around shipping quality software for banks, insurers, retailers, and media companies, the improvements have been substantial.
“We went from producing a lot of the code ourselves to now overseeing the work that Codex can produce,” says Joe Dunleavy, Endava’s regional CTO for Europe. “The quality of output has just gone up exponentially.” Endava now calls itself an agentic organization: a company where senior expertise is codified into agents that work alongside teams across the entire client engagement lifecycle, from intake to ideation and delivery.
“What Codex has really helped us do is have small teams of people deliver massive value in a very condensed timeframe,” Dunleavy notes. This fundamentally shifts the dynamic between junior and senior engineers.
Mike Krolnik, Endava’s Global SVP of Agentic Architecture, explains: “Senior architects like myself, coming from complex environments, are able to articulate what we want, and Codex makes that an accessible piece of information for the more junior people on the team. And from the junior perspective, they’re able to adopt this tool and create senior, mature-level outputs.” In practice, junior developers can take on complex tasks traditionally reserved for senior engineers, with Codex serving as a real-time guide on architectural best practices.
This instant transfer of tacit knowledge is one of the core advantages of an agentic organization. Senior judgment—which historically required years of pair programming, rigorous code reviews, and mentorship—is now digitized and made accessible in real time, magnifying the impact of a single senior expert across dozens of developers simultaneously.
[AgentUpdate Depth Analysis] Endava's transition to an "agentic organization" marks a paradigm shift in how enterprises scale intellectual capital. Instead of treating AI as a mere autocomplete assistant, Endava is digitizing senior engineering judgment—the most scarce resource in software development—and transforming it into reusable AI Agents. This directly correlates with the emerging multi-agent frameworks and protocols like MCP (Model Context Protocol), where organizational knowledge is formalized into active, executable workflows. For the broader AI Agent ecosystem, this case study proves that the long-term value of enterprise AI lies not in cost-cutting, but in the democratization of senior-level expertise. This will inevitably disrupt the billable-hour model of traditional IT outsourcing, shifting the industry toward outcome-based value pricing powered by scalable Agent swarms.
