Software developers are increasingly integrating AI into their work processes, with a significant shift expected in the coming years.

According to Gartner’s forecast, by 2028, approximately three in four enterprise software engineers will be utilizing AI code assistants, marking a substantial transformation in development practices.

This transition is already underway, as developers adapt to tools like GitHub Copilot, Codeium, and Tabnine. These AI assistants are changing not only how code is written but also expanding beyond coding to tasks such as writing user stories, according to Thoughtworks’ Technology Radar.

AI Development Platforms: The Next Evolution

The software development landscape is expected to undergo significant changes by 2025, moving beyond simple code generation toward comprehensive intelligent development platforms. This shift represents a maturation in how AI tools are integrated into the development lifecycle, with five key developments anticipated to transform AI use in software development, as highlighted by TechRadar.

Despite the technological advancements, the human element remains crucial. A December 2024 study by Future (TechRadar’s owner) revealed that 71% of users are polite when interacting with AI assistants, indicating that human-AI relationships maintain social norms even as the technology becomes more sophisticated.

The Rise of Autonomous AI Agents

McKinsey’s insights suggest that generative AI agents are beginning to deliver breakthrough value, particularly when companies successfully build and orchestrate hundreds of them. This represents the next horizon in development acceleration, where specialized AI agents operate independently with human oversight.

These autonomous agents don’t just assist with coding—they collaborate on complex tasks including data analysis, orchestrating sophisticated integrations, designing and running test cases, and refining outcomes based on real-time human feedback. The true value emerges when these agents work together to complete entire software development processes rather than isolated tasks.

Orchestration and Specialization: The Key to AI Value

The orchestration of multiple specialized agents creates powerful workflows. For example, data mapping and storage agents can perform data analysis, compliance checks, quality assurance, and documentation creation. These agents collaborate with security design agents focusing on threat analysis, information security policy, and security design to develop safe, secure, and effective code.

To ensure these AI systems deliver appropriate outcomes, organizations are implementing various controls. Constructive feedback loops allow agents to review and refine each other’s work, while some agents are programmed to teach themselves problem-solving or escalate issues to human managers when necessary.

Developer Experience and Productivity Measurement

As AI reshapes development processes, measuring productivity becomes increasingly important yet challenging. Thoughtworks notes that this isn’t a new concern—their Chief Scientist Martin Fowler wrote about it 20 years ago—but it’s taking on new dimensions with AI integration. Tools such as DX DevEX 360 aim to improve the developer experience and support productivity in this evolving landscape.

The industry is also adapting to permanent changes in work environments. Remote and hybrid work models are now established features of development teams, with various tools and techniques emerging to support these distributed workflows. Remote event storming and similar approaches indicate growing maturity as teams adjust to these new working arrangements.

The Diversifying AI Landscape

While major players like OpenAI’s ChatGPT, Google’s Bard, and Meta’s LLaMA define much of the AI space, interesting trends are emerging around self-hosted large language models (LLMs) and LLM-powered autonomous agents. These developments point toward greater customization and control over AI applications, potentially allowing development teams to tailor AI capabilities to their specific needs and workflows.

This diversification suggests that as AI becomes more integral to software development, organizations will have more options to implement these technologies in ways that align with their specific requirements, team structures, and development philosophies.