The promise of artificial intelligence in the enterprise has long been a tale of automation and efficiency, often couched in terms of copilots and intelligent assistants. Yet, for many, the true revolution has remained just beyond the horizon—until now. A new breed of AI, known as agentic systems, is beginning to reshape the very fabric of how companies operate, moving beyond mere augmentation to take on complex, multi-step tasks autonomously. Nowhere is this transformation more evident than in the experience of Endava, a global software contracting firm, which has redefined itself as an “agentic organization,” dramatically accelerating its software delivery lifecycle and setting a new benchmark for enterprise AI adoption.
From Weeks to Hours: Endava’s Agentic Leap
On May 28, 2026, the discussion around enterprise AI shifted decisively from theoretical potential to tangible, transformative impact, largely thanks to companies like Endava. This multinational firm, with engineering talent spanning Europe, the Americas, and Asia, has been an early and aggressive adopter of advanced AI. Their journey showcases how embedding sophisticated AI agents, specifically through a system they call Codex, into core operational workflows can yield previously unattainable levels of speed and quality.
For a business built on delivering high-quality software solutions to a demanding client base—ranging from banks and insurers to retailers and media giants—the improvements have been nothing short of staggering. Joe Dunleavy, Endava’s regional CTO for Europe, articulates the magnitude of this shift with remarkable clarity. “We went from producing a lot of the code ourselves to now overseeing the work that Codex can produce,” Dunleavy explained. This isn’t just about faster coding; it’s about a fundamental re-evaluation of human and machine collaboration, where the AI takes on the heavy lifting of generation, and human experts pivot to strategic oversight and refinement. The result, according to Dunleavy, is that “the quality of output has just gone up exponentially.”
Perhaps the most compelling metric of this transformation is the reduction in requirements analysis time. What once took weeks of painstaking effort by human teams can now be accomplished in mere hours with the assistance of Codex. This accelerated initial phase, often a bottleneck in traditional software development, has profound implications across the entire project lifecycle, allowing Endava to respond with unprecedented agility to client needs and market demands.
What Defines an Agentic Organization?
Endava’s experience offers a concrete definition of what it means to be an “agentic organization.” It is not simply about using AI tools; it is about systematically codifying senior expertise into autonomous agents that work alongside human teams. These agents are integrated across the entire client engagement lifecycle, from the initial intake and ideation phases all the way through to final delivery. This model moves beyond the reactive, query-response paradigm of many current AI applications. Instead, agentic systems like Codex are designed to understand complex goals, break them down into sub-tasks, interact with various tools and data sources, and even initiate actions, all with minimal human intervention.
Mike Krolnik, Endava’s Global SVP of Agentic Architecture, highlights how this shift fundamentally reconfigures the dynamic between senior and junior engineers. In the traditional model, senior engineers are often swamped with detailed coding tasks, leaving less time for mentorship or strategic planning. Junior engineers, while eager, require significant guidance. With Codex, the AI handles much of the boilerplate, repetitive, or even complex code generation, freeing senior engineers to focus on architectural design, complex problem-solving, and providing high-level oversight. This allows junior engineers to learn by scrutinizing and refining AI-generated code, effectively accelerating their development and broadening their exposure to best practices. It’s a force multiplier for expertise, scaling human intelligence through artificial means.
The Underlying Shift: Infrastructure for Machines
Endava’s success with Codex is not an isolated phenomenon; it reflects a broader, foundational shift underway in the global technology infrastructure. For decades, cloud architectures have been meticulously optimized for human interaction: predictable searches, clicks, scrolls, and streaming patterns. However, AI agents behave very differently. They can unleash torrents of activity, spinning up multiple sub-agents to query hundreds of databases, search vast document repositories, and call APIs in rapid succession, only to then disappear just as quickly. This bursty, non-linear demand pattern requires a completely reimagined infrastructure.
Leading cloud providers are actively redesigning their core services to accommodate this future, one dominated by machine-generated internet traffic rather than human users.
, for instance, recently launched its next generation of OpenSearch Serverless, a fully managed search and vector database system. This new offering is engineered specifically for agentic workloads, boasting the ability to instantly scale up when agents trigger intensive tasks and then scale back down to zero when idle. This elastic, on-demand infrastructure is crucial for supporting the kind of dynamic, agent-driven operations that Endava is pioneering. Without such underlying technological advancements, the vision of pervasive agentic organizations would remain largely theoretical, constrained by the limitations of human-centric cloud design.
Beyond Code Generation: The Enterprise Agent Ecosystem
While Endava’s use of Codex for software delivery is a compelling example, the implications of agentic systems extend far beyond coding. Enterprises across various sectors are grappling with the challenge of integrating AI agents effectively and safely. The non-deterministic, multi-step nature of agents presents unique validation hurdles. A single erroneous tool call or misinterpretation early in an agent’s workflow can cascade into significant downstream errors, making robust evaluation and monitoring paramount.
To address this, frameworks like
, often integrated with platforms such as
and
, are becoming indispensable. These tools provide comprehensive evaluation frameworks to catch issues early, track agent behavior in production, and continuously improve reliability throughout an agent’s lifecycle. For example, a text-to-SQL deep agent, designed to translate natural language queries into database commands, can be rigorously tested and monitored using these systems, ensuring its accuracy and preventing costly data retrieval errors.
Even in specialized domains, such as the telecommunications sector, the agentic paradigm is taking hold. Azercell Telecom LLC, a leading provider in Azerbaijan, collaborated with the AWS Generative AI Innovation Center to build a custom Azerbaijani large language model (LLM) on
. This initiative, aimed at developing a customer-facing chatbot and other telecom-specific use cases, highlights the need to adapt foundation models to morphologically rich languages with limited existing data. The six-week collaboration established a production-ready framework, achieving a 23% higher training throughput and 58% lower peak GPU memory usage, demonstrating that even niche, resource-constrained applications can leverage advanced AI infrastructure for efficient agent development.
The Future is Agentic
Endava’s journey with Codex is a powerful indicator of the direction enterprise AI is heading. It’s a future where AI agents aren’t just intelligent assistants, but integral, autonomous participants in complex workflows, capable of executing multi-step tasks that traditionally required significant human effort. This transition to an “agentic organization” means not just automating tasks, but fundamentally restructuring how work gets done, enhancing the roles of human experts, and unlocking unprecedented levels of productivity and innovation.
The path is not without its challenges, particularly in ensuring the reliability, safety, and ethical deployment of these powerful systems. However, as infrastructure evolves to support agentic workloads, and as robust evaluation frameworks mature, the blueprint laid out by Endava suggests that the age of the truly agentic enterprise is not just coming, it is already here, rapidly transforming the landscape of software delivery and beyond. The shift from human-centric to machine-centric operations, underpinned by intelligent agents, is arguably the most significant evolution in enterprise technology since the advent of cloud computing itself. Companies that embrace this shift early, like Endava, are not just adopting new tools; they are fundamentally redefining their organizational DNA for a future where intelligent agents are partners in progress.
