Context Engineering Emerges as Key to AI's Software Development Role in 2025
Published on November 5, 2025 at 10:31 AM
A real-time experiment has unfolded across the technology industry in 2025, testing AI's software engineering capabilities against human technologists. While the year began with AI showing promise, the transition from 'vibe coding' to 'context engineering' underscores the continuing critical role of human developers, albeit in an evolving capacity.
The latest "Thoughtworks Technology Radar" captures this shift, revealing the emergence of techniques and tools designed to help teams manage context when working with LLMs and AI agents. This signals a broader direction in software engineering and AI, suggesting that the ability to handle context effectively is paramount, moving past the industry's initial focus on scale and speed.
Early experiments with generative AI have highlighted the importance of providing necessary context, or 'knowledge priming,' to ensure consistent and reliable outputs. This is particularly useful in understanding legacy codebases, even without full access to source code. However, effective context isn't just about more data; it's about the appropriate level of abstraction.
The rise of agents and agentic systems has further emphasized the need to move beyond a purely vibes-based approach, demanding significant human intervention to ensure these agents can respond to complex and dynamic situations. Technologies like agents.md, Context7, and Mem0 are aimed at tackling this challenge.
Protocols like the Model Context Protocol and agent2agent (A2A) protocol are emerging to standardize how LLMs/AI agents connect to context sources and interact with each other. Despite the excitement around AI, the importance of fundamental software engineering practices, such as curated shared instructions for teams, remains vital for effective collaboration. The industry is now considering how to balance adaptability and flexibility with the need for robust contextual foundations for AI systems. The conversation has shifted from speed and scale to context, placing software engineers at the heart of AI's future.