Solution Architect

Thoughtworks appoints John Elliott to Global Management Team as Technology Advisory expands globally. July 14, 2026 - Chicago Thoughtworks, a global technology consultancy that integrates design, engineering and artificial intelligence (AI) to drive digital innovation, today announced the appointment of John Elliott to its Global Management Team (GMT) as Global Managing Director, Technology Advisory. Elliott's appointment comes as Thoughtworks expands the Global Technology Advisory practice in response to client requests for strategic guidance in navigating the adoption of AI technologies, agentic architectures and agent-ready data products, rapidly changing SaaS capabilities, agentic development practices and the economics of tokens as enablers to deliver new sources of value. The new global organization strengthens Thoughtworks' ability to help multinational enterprises navigate complex business and technology decisions with consistent expertise across markets. Since joining Thoughtworks in March 2026, Elliott has established and led the company's Technology Advisory practice, helping boards, CEOs and executive leadership teams make the governance, operating and investment decisions required to become AI-enabled enterprises. He also leads Thoughtworks' strategic partnership with Teneo, a premier global CEO advisory firm that sits at the intersection of strategic communications, financial advisory and management consulting. The firm is built specifically to help organizations navigate the world's most complex moments. John is coordinating the combination of Teneo's executive advisory expertise with Thoughtworks' strengths in AI, technology strategy and software engineering to help organizations align business strategy with technology execution and create lasting business value. "Organizations are looking for partners who can help them connect business strategy with technology execution as AI reshapes every industry," said Mike Sutcliff, Chief Executive Officer of Thoughtworks. "Technology Advisory has quickly become a strategic growth capability for Thoughtworks and John's leadership has been instrumental in building that business, establishing its partnership with Teneo and positioning ThoughtWorks, Inc. to better serve clients around the world. Before joining Thoughtworks, Elliott was a partner at McKinsey & Company, where he advised global organizations on technology strategy, enterprise platforms and AI. Earlier in his career, he served as a managing director at Accenture, where he led the firm's Applied Intelligence platform and helped build its mobility and digital platform businesses. He also held senior product leadership roles at Qualcomm, Openwave and Verizon, leading the development and commercialization of mobile media, messaging, IoT and payments platforms. Elliott holds a Master of Science in electrical engineering from the University of Southern California, is a named inventor on multiple technology patents and is the author of an upcoming book on AI leadership and business transformation. "AI presents an extraordinary opportunity for organizations but success won't be determined by the technology alone," said John Elliott, Global Managing Director, Technology Advisory at Thoughtworks. "The organizations that create lasting value from technology and AI will be those that make the right governance, operating and investment decisions, then execute them with discipline. That's what we're building through Technology Advisory and our partnership with Teneo and I'm honored to join the Global Management Team as we continue helping clients turn AI ambition into measurable business outcomes." Supporting resources: * Keep up with Thoughtworks news by visiting the company's website. * Follow Thoughtworks on X, LinkedIn, and YouTube. About Thoughtworks Thoughtworks is a global technology consultancy that integrates design, engineering and AI to drive digital innovation. ThoughtWorks, Inc. is over 10,000 people strong across 47 offices in 18 countries. For 30+ years, ThoughtWorks, Inc. has delivered extraordinary impact together with its clients by helping them solve complex business problems with technology and culture as the differentiator. Media contacts: Kathrin Jansing Head of Public Relations for Europe Michelle Surendran Head of Public Relations for APAC and India Email: [email protected]

Building reliable agentic AI systems. A Case Study in building production-ready agentic AI systems This paper presents the Preclinical Information Center (PRINCE), a cloud-hosted platform developed by Bayer AG with Thoughtworks to address pharmaceutical industry challenges in drug development. PRINCE leverages Agentic Retrieval-Augmented Generation and Text-to-SQL to integrate decades of safety study reports. Nowletus describe PRINCE's evolution from keyword-based search to an intelligent research assistant capable of answering complex questions and drafting regulatory documents. Nowletus reflect on key engineering decisions through the lens of context engineering - how information was shaped and routed between specialized agents - and harness engineering - how orchestration, recovery, and observability were built around the models to maintain control and reliability. The system prioritizes trust through transparency, explainability, and human-in-the-loop integration. PRINCE demonstrates AI's transformative potential in pharmaceuticals, significantly improving data accessibility and research efficiency while ensuring governance and compliance. * 16 June 2026 Contents. * The Challenge: Navigating the Preclinical Data Maze * The Solution: PRINCE - An Evolutionary Platform * System Architecture: Engineering a Reliable Agentic RAG System * The Agentic RAG System * Building Trust in a Production LLM System * Engineering for Resilience: Error Handling and Recovery * Enhancing Data Quality: Named Entity Recognition and Annotation * The Journey Continues: Iterative Development * Conclusion Preclinical drug discovery is inherently complex and data-intensive. Researchers face the significant challenge of efficiently accessing and analyzing vast volumes of information generated during this critical phase. Traditional keyword-based search methods, often reliant on rigid Boolean logic, frequently fall short when confronted with the nuanced and intricate nature of preclinical research questions. The advent of Large Language Models (LLMs) has presented a transformative opportunity. By combining the generative power of LLMs with the precision of information retrieval systems, Retrieval-Augmented Generation (RAG) has emerged as a promising technique. This approach holds the potential to revolutionize preclinical data access, enabling researchers to pose complex questions in natural language and receive accurate, context-rich answers grounded in proprietary data. Recognizing this potential early, Bayer committed to exploring how these technologies could address longstanding challenges in preclinical research. In this post, Nowletus share that journey - how Bayer's early investment in generative AI has resulted in PRINCE, an agentic AI system built on Agentic RAG. This case study explores the technical architecture, engineering decisions, and lessons learned in transforming preclinical data retrieval from a challenging maze into an intuitive conversational experience. Many of the engineering decisions behind PRINCE can now be understood through the lens of context engineering and harness engineering, although when the system was first designed Nowletus did not use these terms. Context engineering shaped what information each model received, what it did not receive, and how context moved between specialized steps such as research, reflection, and writing. Harness engineering shaped the scaffolding around the models: orchestration, tool boundaries, state persistence, retries, fallbacks, validation, reflection loops, observability, and human review. While this post focuses on the technical architecture and engineering challenges, its paper published in Frontiers in Artificial Intelligence covers the product evolution and business impact in more detail. The Solution: PRINCE - An Evolutionary Platform. To address these challenges, Bayer developed the Preclinical Information Center (PRINCE) platform. PRINCE was conceived as a unified gateway to preclinical data, initially focusing on consolidating previously siloed structured study metadata and exposing them in a âSearchableâ manner. This initial phase allowed users to apply advanced filters and retrieve information primarily from structured study metadata. However, a significant portion of Bayer's valuable preclinical knowledge resides within unstructured PDF study reports accumulated over decades. Due to numerous system migrations over the years, the structured metadata associated with these reports could be incomplete, missing, or even contain incorrect annotations. Crucially, the authoritative âgold standardâ information was consistently present within the approved PDF study reports. The emergence of Generative AI, particularly RAG, provided the key to unlocking this wealth of unstructured data. By integrating RAG capabilities, PRINCE began to shift the paradigm from a filter-based 'search' tool to a natural language 'ask' system, enabling researchers to query the content of these study reports directly. This evolution reflects PRINCE's progression through three distinct phases: * Search: the initial phase focused on creating a unified gateway to thousands of nonclinical study reports, consolidating multiple in-house data silos from various preclinical domains into a searchable format, primarily leveraging structured metadata. * Ask: this phase introduced an AI-powered question-answering system utilizing Retrieval Augmented Generation (RAG). This enabled researchers to derive insights directly from unstructured data, including scanned PDFs from historical reports, by posing questions in natural language. * Do: the current phase positions PRINCE as an active research assistant capable of executing complex tasks. This is achieved through the integration of multi-agent systems, allowing the platform to handle intricate queries, orchestrate workflows, and support activities like drafting regulatory documents. This deliberate evolution from Search to Ask to Do represents a strategic response to the industry's need for greater efficiency and innovation in preclinical development. By providing researchers with increasingly powerful tools to access, analyze, and act upon preclinical data, PRINCE aims to enable faster data-driven decision-making, reduce the need for unnecessary experiments, and ultimately accelerate the development of safer, more effective therapies. System Architecture: Engineering a Reliable Agentic RAG System. The system functions as an interactive conversational UI, powered by a robust backend infrastructure. Its architecture, designed for handling complex queries and delivering accurate, context-rich answers, is orchestrated using LangGraph and served via a * FastAPI* application. Figure 1 provides the system context - UI, backend, data stores, LLM fallbacks, and observability - while Figure 2 zooms into how the system coordinates its specialized agents. Figure 1: System context and supporting platforms. * User Request: the process begins when a user submits a request through the Conversational UI which is built with React. * Orchestration: the user's request is routed to a LangGraph-based orchestration layer in the backend. This workflow engine coordinates a multi-stage process that progresses through clarifying user intent, thinking and planning, conducting research (using RAG and Text-to-SQL), validating data completion, and finally generating a response through the Writer agent. The workflow includes deliberate pause points and feedback loops to ensure data completeness before proceeding. (Nowletus explore the details of this agentic workflow in a dedicated section later.) * Data Retrieval and State Management: the Researcher agents interact with a comprehensive and distributed data ecosystem: * Vector representations of all study reports are stored in OpenSearch, forming the coreknowledge basefor information retrieval. - Curated structured data, resulting from various ETL and harmonization processes, is

Thrad announces partnership with Thoughtworks. Thrad, the advertising infrastructure provider for the AI ecosystem, has announced a new partnership with Thoughtworks, a global technology consultancy renowned for building intelligent systems at scale. The partnership brings together Thoughtworks' deep technical expertise in AI-enabled transformation with Thrad's advertising infrastructure, enabling Thoughtworks to reach audiences within large language models (LLMs), where users are already actively engaging with AI, in a way that is ethical, scalable, and aligned with user experience. Since the collaboration began, Thoughtworks has launched four campaigns through Thrad's platform, spanning topics including the transformation of AI into monetisation capabilities, agentic development, and the successful adoption of AI within enterprise workflows. Two further campaigns are currently active, exploring the evolution from AI assistants to autonomous agents and productivity through AI. "For years, we've believed that advertising in AI doesn't have to be a compromise. It doesn't have to be intrusive, extractive, or at odds with user experience. Thoughtworks shares that vision and is building that future with us," said Andrea F. Tortella, CEO and co-founder of Thrad. That sentiment is reflected in why Thoughtworks chose the platform. Thoughtworks' world-class expertise in building intelligent systems at scale, combined with Thrad's focus on responsible AI advertising, creates better outcomes for platforms, advertisers, and end users alike. "We're thrilled to be working with a partner that understands how to reach audiences within AI environments without compromising on trust or user experience," said Sailesh JV of Thoughtworks. "Thrad gives us the infrastructure to communicate our solutions where our audiences are already engaging, and to do it with intention." For Thrad, the partnership underscores the growing appetite among enterprise technology leaders to invest in AI-native advertising channels that reflect their own values around responsible innovation. As both organisations continue to deepen their collaboration, Thrad and Thoughtworks aim to expand their active campaigns and explore new ways to demonstrate what becomes possible when technology is built with purpose. Thrad. Paid ads in LLMs - doing millions of ads daily, used by Fortune 500 brands... Powered by PressBox