Trace Grading in the context of AI refers to the process of evaluating how an AI agent performs a task by examining its entire chain of actions—not just the final result. It’s a method designed to assess the trace of an AI system’s decision-making: every step, tool call, or reasoning segment that happens between input and output. The term was popularized by OpenAI in connection with AgentKit, their framework for building and testing “agentic” AI systems that act autonomously across multiple steps or tools.
Imagine an AI agent as a detective solving a mystery. Instead of grading the detective only on whether they caught the culprit, trace grading reviews their whole investigation—what clues they followed, which suspects they questioned, and whether each step made sense. This makes it possible to pinpoint where the detective (or in this case, the AI) took a wrong turn or wasted effort.
In practical terms, trace grading lets developers run end-to-end evaluations of an agent’s workflow and automatically score its internal trace to find weaknesses. For example, a grader might highlight when an AI made an unnecessary API call, misunderstood an instruction, or skipped a crucial reasoning step. This approach produces much richer feedback than simple accuracy scores, enabling teams to refine prompts, fix tool-use patterns, and optimize performance at each stage.
Trace grading is particularly valuable in modern AI agent systems, where reasoning chains, decision trees, and tool interactions can be complex. By making these traces visible and measurable, trace grading turns what was once a black-box process into something observable, improvable, and testable—bringing a layer of accountability and transparency to how AI agents actually think and act.
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