Thoughtworks, a global technology consultancy that integrates design, engineering and AI to drive digital innovation, today released volume 35 of itsTechnology Radar, a biannual report informed by the organization's experiences with clients.
In developing this edition, the Technology Radar team observed that as agents are handed larger, longer-running tasks, the hardest engineering challenges increasingly lie beyond interactions with the model itself: securing the environment in which agents operate, deciding what they can access, establishing meaningful human oversight and controlling the cost of running them.
One indication of this focus is where the Radar makes its strongest recommendations. The 11 practices and technologies placed in its Adopt ring, which include techniques such as agent sandboxing and threat modeling, signal the growing importance of the safeguards needed to run AI reliably in production and underline the need to act now.
Earlier editions of the Radar traced the growing capabilities of AI in software engineering, and volume 34 examined the friction and risks of using it at scale. Volume 35 turns to managing those risks as adoption grows. Its themes span the systems that govern agent behavior, the role of human judgment and the infrastructure that determines cost and reliability.
“Not long ago, the question we heard from clients was how fast agents could write code. Now it’s whether anyone can trust, review and afford what they produce,” said Rachel Laycock, chief technology officer at Thoughtworks. “Models will keep improving, but lasting value will go to the organizations that invest in the engineering and oversight around them.”
This edition’s themes explore what it takes to build and manage the system around the model:
- Harness engineering at scale: The environment around an AI agent, including its prompts, tools, memory, guardrails and feedback loops, is becoming a product in its own right, with owners, versions and dependencies. As a growing ecosystem of plugins and supporting tools takes shape, organizations must treat the harness as an engineered system, with appropriate evaluation, security, access controls and ongoing maintenance.
Harness engineering at scale: The environment around an AI agent, including its prompts, tools, memory, guardrails and feedback loops, is becoming a product in its own right, with owners, versions and dependencies. As a growing ecosystem of plugins and supporting tools takes shape, organizations must treat the harness as an engineered system, with appropriate evaluation, security, access controls and ongoing maintenance.
- Putting more agents to work, without losing human judgment: Developers are increasingly running multiple agent sessions in parallel and delegating longer-running tasks, including unattended overnight builds. But as agents produce more work, human review and software delivery pipelines can become bottlenecks. The Radar cautions against “human in the loop theater,” where approval becomes a ritual rather than a meaningful check, and encourages teams to assess their review capacity before scaling agent use.
Putting more agents to work, without losing human judgment: Developers are increasingly running multiple agent sessions in parallel and delegating longer-running tasks, including unattended overnight builds. But as agents produce more work, human review and software delivery pipelines can become bottlenecks. The Radar cautions against “human in the loop theater,” where approval becomes a ritual rather than a meaningful check, and encourages teams to assess their review capacity before scaling agent use.
- Beyond frontier models: Proprietary frontier models are no longer the obvious default for every task. Open-weight models have narrowed the gap for many workloads and offer additional deployment options, including running within an organization's own infrastructure. The Radar cautions against defaulting to a frontier model for every task, encouraging teams to choose models deliberately based on task requirements, cost, privacy and operational considerations.
Beyond frontier models: Proprietary frontier models are no longer the obvious default for every task. Open-weight models have narrowed the gap for many workloads and offer additional deployment options, including running within an organization's own infrastructure. The Radar cautions against defaulting to a frontier model for every task, encouraging teams to choose models deliberately based on task requirements, cost, privacy and operational considerations.
- The economics of running AI at scale: The cost of AI extends well beyond model pricing. Routing, batching, storage, networking and scheduling all influence the economics of running AI workloads in production. Much of the innovation needed to make AI economically sustainable lies deeper in the technology stack, where models, GPUs, storage and networks are managed as programmable resources through a common platform layer.
The economics of running AI at scale: The cost of AI extends well beyond model pricing. Routing, batching, storage, networking and scheduling all influence the economics of running AI workloads in production. Much of the innovation needed to make AI economically sustainable lies deeper in the technology stack, where models, GPUs, storage and networks are managed as programmable resources through a common platform layer.
Together, these findings point to a broader shift in enterprise AI: realizing its potential requires more than access to capable models. Organizations must build the engineering practices, infrastructure and human oversight needed to operate agents responsibly at scale.
Visitwww.thoughtworks.com/radar to explore the interactive version of the Radar or download the PDF.
Supporting resources:
- Listen to a discussionabout the key themes in this volume on the Thoughtworks Technology Podcast.
Listen to a discussionabout the key themes in this volume on the Thoughtworks Technology Podcast.
- Keep up with Thoughtworks news by visiting the company'swebsite.
Keep up with Thoughtworks news by visiting the company'swebsite.
- Follow Thoughtworks onX,LinkedIn andYouTube.
Follow Thoughtworks onX,LinkedIn andYouTube.
About Thoughtworks
Thoughtworks is a global technology consultancy that integrates design, engineering and AI to drive digital innovation. For over 30 years, Thoughtworks has helped organisations solve complex business problems with technology as the differentiator.
Contact:
Marsh Abraham
Head of PR for Americas
marsh.abraham@thoughtworks.com
