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How we made continuous trace intelligence possible at scale
Braintrust founder Ankur Goyal introduced Topics, an intelligence layer designed to analyze massive, unstructured production agent traces at scale.
What: Topics uses an LLM-driven pipeline to preprocess, embed, and classify trace data. By summarizing traces before embedding, the system bypasses the context window limitations that make traditional NLP tools fail on complex agent logs.
Why it matters: As AI agents generate increasingly long and non-uniform log traces, specialized analytical infrastructure is required to make sense of 'agent sprawl' in production environments.
Decoder
- Trace: A record of the sequence of operations or calls made by an agent, useful for debugging and monitoring execution paths.
Original article
Braintrust founder Ankur Goyal lays out Topics, the intelligence layer for analyzing production agent traces at scale where million-token traces with hundreds of spans break every standard NLP tool that expects uniform document shapes. Inspired by Anthropic's Clio paper, the pipeline runs preprocess to facet to embed to cluster to name to classify, with the LLM summary doing the one job that makes the rest tractable since the raw trace never has to fit in an embedding model's context window.
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