In a recent LinkedIn post, Teresa Torres discusses a critical challenge in managing AI systems: debugging complex pipelines. Torres emphasizes that evaluating the entire AI chain without granular inspection can obscure the root cause of issues, making problem-solving inefficient.
According to Teresa Torres, a common pitfall is the tendency to look at the overall performance without dissecting individual components. This holistic view, she argues, leaves developers in the dark about where precisely a problem might lie within a multi-stage AI process.
“When you evaluate only the whole chain, you have no idea where things went wrong. It could be anywhere in the pipeline.”
The Importance of Granular Agent Evaluation
Teresa Torres highlights the approach taken by Tendos AI as a key example of effective debugging. She points out that Tendos AI recognized early on the necessity of evaluating each AI agent independently. This agent-level assessment, as Torres explains, provides clear visibility into specific changes and pinpoints exactly where to direct investigative efforts.
“Tendos AI figured out early that evaluating each agent independently was vital. It shows exactly what changed and where to look,” Torres shared in her post.
Scaling Debugging with Advanced Tools
Beyond independent evaluation, Teresa Torres also touches upon the role of advanced tools in making large-scale debugging manageable. She notes that the ability to query the evaluations of individual agents, coupled with the capacity to classify errors into distinct groups and compare them against historical data, transforms a daunting task into a tractable one.
As Teresa Torres observes, this systematic approach is crucial for maintaining and improving AI systems over time. Without such granular insights, identifying and rectifying errors in complex AI pipelines can quickly become an insurmountable challenge.
“Combined with the ability to ask questions on their evals—classifying errors into groups and comparing against historical data—debugging at scale became manageable instead of impossible.”
Teresa Torres’s insights underscore the value of detailed, component-specific analysis in the realm of AI development and maintenance. By breaking down complex systems and meticulously examining each part, organizations can more effectively identify, address, and resolve issues, ensuring the robust performance of their AI technologies.
📝 About This Content
This article is based on insights shared by Teresa Torres on LinkedIn.
📅 Originally posted on January 17, 2026 | View original post on LinkedIn →