Software Development Shifts from "Vibe Coding" to Context Engineering

Thoughtworks' latest Technology Radar report highlights a significant shift in software development, from "vibe coding" to "context engineering." This transition reflects the growing importance of human developers in managing context when working with large language models (LLMs) and AI agents. According to the report, the term "vibe coding" was coined by Andrej Karpathy in February 2025, sparking a wave of interest in the industry. However, as the year progressed, it became clear that while AI's software engineering capabilities had improved, human developers remained essential. The report notes that teams are now focusing on techniques and tooling designed to handle context effectively, rather than solely relying on scale and speed. "We're seeing a shift in the industry's understanding of AI's capabilities," said a Thoughtworks spokesperson. "While AI can process vast amounts of data, it's the human developer's ability to understand and manage context that's becoming increasingly critical." The spokesperson added that this shift is reflected in the emergence of new innovations, such as context engineering, which prioritize human input and oversight. Context engineering involves using techniques like data curation, human-in-the-loop validation, and explainability to ensure that AI models are working effectively within specific contexts. This approach acknowledges that AI is not a replacement for human developers, but rather a tool that requires careful management and oversight. The Thoughtworks Technology Radar report highlights several key trends and innovations in software development, including the growing importance of context engineering, the need for human-in-the-loop validation, and the emergence of new tooling and techniques for managing AI models. In addition to context engineering, the report notes that other techniques, such as data curation and explainability, are becoming increasingly important in software development. Data curation involves ensuring that the data used to train AI models is accurate, relevant, and representative of the context in which the model will be used. Explainability, on the other hand, involves providing insights into how AI models arrive at their decisions, allowing developers to understand and correct any biases or errors. The shift towards context engineering and human-in-the-loop validation reflects a growing recognition of the limitations of AI and the importance of human oversight in software development. As the industry continues to evolve, it's likely that we'll see even more emphasis on these techniques and innovations. The Thoughtworks Technology Radar report provides a comprehensive overview of the latest trends and innovations in software development, offering insights and guidance for developers, teams, and organizations looking to stay ahead of the curve.