DeveloperTech News is part of the TechForge Publications series
TechForge
AI-Tools, Architecture & Methods, Build & Ship, Career, Training & Salaries, CI/CD & Release Engineering, Community & Culture, Editorial, Features, Infra-as-Code & Automation, Testing & QA, Version Control
Ryan Daws
5th June 2026
Share this story:
Tags:
Categories::
Endava is overhauling the traditional fullstack by deploying specialised AI agents to automate the entire software delivery process.
At the centre of this push is a unified platform integrating ChatGPT Enterprise alongside OpenAI’s Codex models. Endava isn’t just handing these tools to its developers as standalone assistants, they are building a network of separate agents and giving each one total ownership over a specific piece of the development journey.
Picture a scenario where one agent’s only job is grabbing raw business requirements and turning them into clear user stories and functional specs. You might have a separate agent handle the grunt work of spinning up boilerplate logic, executing unit tests, and writing the documentation based directly on those earlier requirements. Meanwhile, another specialised model acts as a silent reviewer, scanning pull requests for vulnerabilities, careless errors, or formatting problems long before a human engineer is pinged to check the code.
The overarching strategy for Endava is to establish a vast, modular library of these agents, allowing teams to quickly string together bespoke workflows tailored to whatever project they are tackling.
On a standard web application, the workflow might link up agents that handle frontend components, API testing, and accessibility compliance. At the same time, a data team could easily stitch together a completely unique sequence of agents designed to construct pipelines, validate schema parameters, and squeeze out extra performance.
Breaking things down into modular blocks like this ensures the entire system remains agile and adaptable, leaving rigid, generic coding assistants of yore in the dust.
Early enterprise generative AI use cases focused mostly on auto-completing single lines of code or generating basic functions. Endava’s approach stretches this automation across the full pipeline. An engineer might kick off a task, but an AI agent steps up to manage the sequence of events required to cross the finish line, pulling in other specialised agents whenever necessary.
This alters the day-to-day workload of human developers. Their main responsibility shifts toward defining the problem, choosing the best agent-driven workflow, and verifying the final results. Writing, testing, and documenting basic code is increasingly left to the AI platform. To survive and thrive here, engineers have to completely change gears, diving deep into big-picture systems thinking and learning exactly how to steer these autonomous processes.
Getting a platform like this off the ground is about changing how people think and work just as much as it is about writing new code. Endava is actively building an AI-native mentality across its entire organisation.
This requires deep training programs that teach teams how to think about software engineering within an agent-assisted framework, rather than just teaching them how to click buttons. Engineers are encouraged to constantly spot automation opportunities and build new specialised agents for the company library.
With automated agents actively drafting and auditing code meant for production, establishing watertight guardrails around intellectual property, stability, and security is a top priority. Every single line of machine-generated code faces rigorous automated scanning, while critical core components still require a final sign-off from human developers.
Simultaneously, clear data policies ensure that proprietary information stays safe and never inadvertently trains the underlying public AI models. Building these guardrails is non-negotiable for securing trust among internal teams and external clients.
Prioritising full workflow automation over basic code generation highlights the shift in how enterprises use AI. It transforms the technology from a simple developer tool into the foundation of the operational framework and shows that the true value of AI in software development isn’t just about cranking out lines of code faster, but completely reimagining the processes that bring that software to life.
See also: Google’s Gemma 4 12B brings local multimodal AI to laptops
Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information.
Developer is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here.
Ryan Daws
Senior Editor
4th June 2026
3rd June 2026
2nd June 2026
1st June 2026
Subscribe now to get all our premium content and latest tech news delivered straight to your inbox
Community & Culture, Developer, Languages, Mobile, Open-Source
Big Tech, Developer, Industry Insights
AI-Tools, Build & Ship, Cybersecurity & Development, Developer, Features, IDEs & SDKs
AI-Tools, Developer
Sponsored Content
2nd June 2026
Open-Source
1st June 2026
Open-Source
29th May 2026
All our premium content and latest tech news delivered straight to your inbox
DeveloperTech News is part of TechForge
All our premium content and latest tech news delivered straight to your inbox
Leave a Reply