A semi-autonomous loop to find the best code changes for your LLM workflows and agents using real customer scenarios.
We wrapped all the functionality of our MCP, CLI, and SDKs into two well-tested coding agent skills so you don’t need to build infra around our infra.
The agent annotates the spans that matter and collapses noisy traces. Override whatever’s wrong. The dataset reflects you and your customer's judgment, not the agent’s.
trace · chatbot/order_status · 2.4s · 5 spans · 3 annotated
search_orders is most of the wall clock. A bounded query or cache knocks this under a second.
Called with order_id=4821. Customer wrote #4821-A — wrong order returned.
Trace shows a 3-day shipping delay; the reply never mentions it. Override for the dataset.
Rerun experiments locally or in the cloud on production trace data with custom sandboxes.
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