What is in a High-Fidelity trace?

High-fidelity AI traces aim to reveal the full context and cost of agent interactions, including system prompts, security checks, injected instructions, tool calls, tool results, reasoning blocks, and hidden harness activity that conventional agent or OpenTelemetry traces may omit. Using an example codebase-analysis session, the passage argues that substantial token use and expense can be concealed from users, with a 404,000-token session containing more than a third of its usage in hidden context and a single recap feature adding over 60,000 tokens. Tapes is presented as an open-source, proxy-based system positioned between an agent harness and an inference provider to capture every exchange, enabling teams to inspect what influenced an agent’s actions, understand token and financial costs, and share operational knowledge more effectively.

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