While the developer community is in broad agreement about the potential of AI to take on an ever-growing roster of software development tasks – from code generation to testing and refactoring – there is less consensus on the speed and extent to which it might happen.
Making accurate predictions about the future is difficult, but from numerous conversations leading up to GITEX in Dubai, it’s clear that there is significant enthusiasm for AI agents in software development, although it is tempered by some doubts, especially when the conversation turns to finer details.
This is reflected by global research which shows that while 93 per cent of surveyed organisations are planning to trial agents for software development, only 27 per cent believe that these agents will become fully autonomous, down from 43 per cent in 2024.
This is of particular interest in the UAE, with its clear ambitions and a strategy to foster a development ecosystem through initiatives such as the National Program for Coders, which aims to train 100,000 coders within five years. In such a dynamic environment, implementing AI agents effectively will be essential.
So how can development teams prepare for successful human/AI collaboration? This depends partly on how quickly and deeply they commit to integrating AI agents. It’s useful to see two possible scenarios: The first where AI agents are integrated into the development process at a slower pace and function as a valuable assistant to human developers, and the second where AI will take a more dominant role in development at all stages of the process.
Scenario 1: Dev-to-Agent collaboration
AI’s support is seen expanding beyond simple code completion, with agents becoming active team members, capable of autonomously resolving issues, updating documentation, and tidying codebases. This enables human developers to shift their focus to complex, creative work. Furthermore, AI agents/assistants will extend to non-coding roles, like QA, DevOps, and product management.
To manage the work with the coding agents, the developer toolkit must evolve. Integrated Development Environments (IDEs) will transform to include intuitive interfaces that enable humans to assign tasks, track progress, and review output from AI agents. This necessitates creating tools that build trust, allowing developers to quickly verify the AI’s actions. Beyond coding, the next generation of AI will automate tasks like debugging, profiling, and environment configuration, further expanding human capacity.
“Even with the current LLM models, code can be generated in seconds via completion and edit prediction. In this first scenario, context switching will be minimised with ‘one window, one chat’ coding. Short prompt tasks will be fully taken over by the agents, which saves hours of development. At the same time, it won’t solve business problems completely because a business case usually requires more than one developer,” said Andrew Zakonov, VP of Business at JetBrains.
As machine-generated code proliferates, new control mechanisms are critical. This includes implementing Git-level tagging to mark code as AI-generated for traceability and developing specialised auditing and testing tools to ensure machine-written code meets quality standards.
Scenario 2: Dev as an Agent Lead
The shift toward a ‘full-blown’ AI future relies on creating collaboration models where AI-developer teams take the lead. This requires solving three core challenges: establishing seamless AI workspaces, developing next-generation collaboration tools, and creating an AI-native marketplace.
The biggest challenge is defining the interaction points between human insight and AI productivity, a field called Human-AI eXperience (HAX). Developers will need radically new IDEs designed for guiding and verifying a flood of AI-generated code. These tools must automatically understand, validate, and summarise AI output, allowing human developers to stay focused on high-level architecture and requirements.
For AI developers to operate effectively, they must be given a single, integrated environment that provides the tools they need without friction. This means moving beyond complex integrations to give AI agents direct, seamless access to all development tools (coding, testing, debugging, documentation) from one interface. The agent’s physical “home” must also be addressed, whether through centralised cloud platforms or secure, offline-first local inference environments.
Finally, scaling AI productivity requires a new hub for code reuse. A dedicated AI-first marketplace must feature built-in sandboxing for safe testing and automated verification pipelines to check quality and compatibility. This allows AI teams to move faster, share assets safely, and continuously improve codebases, creating a scalable ecosystem for autonomous development.
Regardless of what the future holds, one thing is clear: AI will evolve from a supportive assistant into a proactive player in coding, testing, and analysis. This shift will create new roles for developers as guides and reviewers, collaborating closely with AI agents.
Whether AI agents quickly take a lead in software development or remain as assistants to human developers for a prolonged period, their contribution will be key to the success of developer teams. It’s essential that organisations read the market and develop a blueprint that works for their organisation.
