Why Building an Eval Platform Is Harder Than It Looks — Braintrust
Building robust agent quality platforms is a complex systems challenge, requiring more than just a UI or spreadsheet. The core goal is maintaining confidence in non-deterministic LLM agents by establishing a continuous improvement loop. This loop requires integrating pre-production evaluation (Evals) with post-production observability. The primary technical hurdle is managing massive, nested JSON agent traces, which necessitates specialized data querying tools like BTQL to support both real-time monitoring and long-running analysis.
Key takeaways
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Evals vs. Observability
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Agent quality relies on two pillars: Evals (testing behavior before production) and Observability (monitoring interactions with real-life users in production). Both are necessary to understand and improve agent performance due to the non-deterministic nature of LLMs.
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The Evolution of Evals Maturity
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Eval platforms progress through stages: 1) Spreadsheets (basic logging); 2) Custom UIs (allowing PMs to participate); 3) Side-by-side experiments (tweakable parameters); and 4) Production Flywheels (connecting live failure modes back into offline test cases).
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The Data Challenge of Agent Traces
Agent traces are semi-structured JSON, often containing hundreds of megabytes of data per interaction. This volume and structure overwhelm typical cloud data warehouses, requiring specialized query capabilities for both real-time and long-running analysis.
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Advanced Automation
The most advanced platforms allow coding agents to run evals themselves using natural language prompts (e.g., 'Find me all traces in the last 24 hours where the user had a poor experience. Run the evals for me.'), enabling the surfacing of 'unknown unknowns.'