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TwoTail AI

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TwoTail is eval analytics for agentic products. Most eval setups grade a sampled test set and drift away from what users actually experience. TwoTail grades production traffic continuously, keeps LLM judges calibrated against your team's own labels, and correlates eval scores with business metrics so you know which failures cost you money and which are noise. How it works, in four steps: - Define: build evals from real traces, import the ones you already have, or start from TwoTail's proposals. - Annotate: rate smart-sampled slices of traffic against a scoring rubric. Bayesian sampling means you label the traces that move the estimate, not thousands at random. - Calibrate: judges are continuously tuned to match your labels, with agreement rates visible so you can see when a judge stops being trustworthy. - Correlate: validate every eval against outcome metrics (resolution rate, editor approval, conversion) to find the two or three that predict results. What you get on top of that: - Failure taxonomy: failures are clustered and named, with share of traffic per category, so the fix list is ranked rather than anecdotal. - Segmentation: quality broken down by user segment, intent, model or configuration, to find where an agent underperforms rather than whether it does on average. - Real-time alerts when a calibrated eval regresses. - MCP access, so your coding agent can query eval results directly. Integrations: TwoTail ingests OpenTelemetry (OTLP/JSON) from a single endpoint. Works with LangChain, LlamaIndex, CrewAI, OpenAI Agents SDK, Claude Agent SDK, Vercel AI SDK and any custom agent that can emit spans. Runs alongside Langfuse rather than replacing it. Who it is for: growth-stage teams running agents in production, where "is the agent getting better" has to be answered with numbers the business believes. Pricing starts at EUR 59/month with unlimited seats. Free 14-day trial on every plan, no credit card required.

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