Thoughtworks’ latest Radar report says these are the AI solutions you should adopt right now

The latest Thoughtworks Technology Radar report reveals significant shifts in the technology landscape, with artificial intelligence and machine learning taking center stage while emphasizing the continued importance of foundational software engineering practices.

Key developments in AI and systems: The technology landscape is experiencing a dramatic transformation driven by generative AI, Large Language Models (LLMs), and emerging programming languages.

• Generative AI and LLMs are reshaping software development practices, with a growing emphasis on responsible implementation
• AI-powered coding tools continue to evolve, requiring careful balance between automated assistance and human oversight
• Rust programming language is gaining significant traction in systems programming applications
• WebAssembly 1.0’s widespread browser support is creating new opportunities for cross-platform development

Technical recommendations: Thoughtworks identifies several key technologies and practices ready for mainstream adoption.

• The report recommends adopting 1% canary releases, component testing, and continuous deployment practices
• Retrieval-augmented generation (RAG) is highlighted as a mature technology ready for implementation
• Bruno, K9s, and visual regression testing tools like BackstopJS are recommended for immediate adoption
• Database technologies dbt and Testcontainers are endorsed for production use

Emerging platforms and tools: The AI ecosystem is rapidly expanding with new supporting technologies and infrastructure.

• Databricks Unity Catalog, FastChat, and GCP Vertex AI Agent Builder are identified as promising platforms worth trialing
• Vector databases and evaluation frameworks are growing in importance for AI applications
• Cloud management tools like AWS Control Tower and data processing solutions like ClickHouse are recommended for trial
• Small Language Models (SLMs) are emerging as viable alternatives to LLMs for specific use cases, particularly in edge computing

Infrastructure and implementation: The report emphasizes the importance of maintaining robust software engineering practices while embracing AI innovation.

• Traditional software development techniques like unit testing and architectural fitness functions remain crucial
• The ecosystem of tools supporting language models has expanded to include guardrails and evaluation frameworks
• Azure AI Search, V7, Nvidia Deepstream SDK, and Roboflow are highlighted as emerging platforms for AI implementation
• Languages and frameworks like CAP, CARLA, and LlamaIndex are gaining attention in the machine learning space

Historical perspective and future implications: The current AI technology surge mirrors previous technological transformations while presenting unique challenges and opportunities.

• The rapid growth in AI technologies parallels the explosive expansion of the JavaScript ecosystem around 2015
• The increasing focus on smaller, more specialized language models suggests a trend toward more efficient and targeted AI solutions
• This shift could lead to more diverse and practical applications of AI technology, particularly in resource-constrained environments
• The emphasis on responsible AI implementation indicates a maturing approach to artificial intelligence in enterprise settings