TwoTail sits on top of your agent observability. It reads your traces, finds what's wrong, and proves the change before you ship it across three stages.
01 / Observability
Point your OpenTelemetry exporter at TwoTail and every agent run becomes a structured, queryable trace. No SDK, no code changes.
Every span, in order, with timing, tokens, cost, inputs, and outputs. Expand any step to see the prompt that ran and the response that came back, all the way down to the leaf LLM call.
Point your existing OpenTelemetry exporter at TwoTail and you're done. If your framework already emits OTel spans, there's nothing to install and no agent code to change.
02 / Analytics
Ask questions in plain English, teach it your product, and let it investigate on its own. The analysis comes back as a diagnosis, not another dashboard.
TwoTail investigates on its own, working through research-backed, battle-tested playbooks the best data teams use: failure clustering, latency decomposition, cost attribution. It queues the work, runs it, and sends you a diagnosis when something's worth your attention.
Tell TwoTail your strategy, your terminology, and what success means. Every answer comes back in your terms, not generic LLM-speak, and the data structure is auto-generated from your own traces.
power_user become reusable across every analysis.Type a question. TwoTail writes the SQL, runs it against your traces, picks the right chart, and hands back a headline you can act on. No query language, no dashboard building.
Answer
Refund requests resolve 38% of the time, far below the 82% average.
Bar, line, box plot, radar, clustering, single-metric, A/B. TwoTail auto-detects the right visualization for the question, and you can pin any chart to a dashboard to watch it over time.
03 / Optimization
A finding is only half the job. TwoTail curates the test set, replays your real traces against variants offline, then measures the winner on live runs, so every change you ship is grounded in evidence.
Group traces into datasets you can label by hand, use to calibrate your LLM judges against real human verdicts, and pin as the test set for sandbox experiments. One curated set, trusted everywhere.
Pick a task from production, define your variants, choose which evals to score, and run it against a sample of real inputs. Nothing touches production, no risk, no waiting for new data.
Define your variants
Roll a change out to a fraction of your live runs and let TwoTail measure it against the control on your real metrics, with statistical significance, so you keep what genuinely moves the needle and roll back what doesn't.
Setup in 10 minutes. First insights within a week.