Thoughtworks, a global technology consultancy that integrates strategy, design and engineering to drive digital innovation, today presents the 32nd edition of the Technology Radar. The biannual report is built on Thoughtworks’ frontline experiences solving its clients’ most complex business challenges.

The latest edition of the Thoughtworks Technology Radar offers a comprehensive overview of the ever-evolving world of technology. While this edition takes a close look at the significant impact of generative AI, the Radar maintains its focus on the diverse technologies, tools and techniques shaping how we build and deliver software. Key themes include the practical application of AI, the modernisation of observability practices and the critical importance of data management in modern systems.

“The Thoughtworks Technology Radar has always been a guide to the technologies shaping our industry,” states Rachel Laycock, Chief Technology Officer of Thoughtworks. “This edition is particularly insightful, highlighting the need to balance the excitement around generative AI with a pragmatic approach to its adoption. We also emphasise the foundational elements of software delivery — like data product thinking and evolving observability — which are critical for building resilient and effective systems.”

Technology Radar Volume 32 again contains over 100 blips, spotlighting a diverse range of tools, techniques, platforms and languages and frameworks. Alongside them there are four themes that speak to key narratives that emerged during the process of putting the Radar together:

• Supervised agents in coding assistants: AI coding assistants are increasingly capable of driving implementation directly within the Integrated Development Environment (IDE), navigating and modifying code, and even fixing errors. While these tools show promise in streamlining development, caution is advised to avoid complacency with AI-generated code, emphasising the continued need for human oversight. The focus remains on a supervised approach, where developers guide and oversee the AI agent’s actions.
• Evolving observability: The observability space is evolving rapidly, driven by the increasing complexity of distributed architectures. There’s a growing focus on Large Language Models (LLM) observability tools to monitor AI performance and the increasing adoption of OpenTelemetry for standardised observability. This evolution signifies the industry’s growing recognition of observability’s importance.
• R in RAG: The retrieval aspect of Retrieval Augmented Generation (RAG) is a key area of development, focused on customising LLM prompts to generate relevant and useful responses. New techniques like corrective RAG, Fusion-RAG, and Self-RAG are emerging to enhance retrieval and optimise LLM output. This highlights the rapid evolution of effective retrieval methods within the generative AI ecosystem.
• Taming the data frontier: The focus in data management is shifting towards handling rich and complex data, rather than just volume. Ensuring data is effectively managed and packaged is crucial for leveraging it in AI applications and analytics. This theme also emphasises the importance of “data product thinking,” advocating for applying product management principles to data assets to improve usability and value.

 Visit www.thoughtworks.com/radar to explore the interactive version of the Radar or download the PDF version.

Supporting resources:

• Read more about the macro trends in the tech industry in this edition.
• The machines are rising — but developers still hold the keys
• We need to talk about vibe coding
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