Eval Analytics forAgentic Products
TwoTail makes it easy to reliably measure the quality of your agent, by keeping your evals aligned to your expertise and business outcomes.
Your agent is live. Its heart is beating.
But is it always acting intelligently?
You ran manual tests when you shipped it. Maybe you even wired up a Claude job to keep an eye on things.
But it is getting away from you. New cases keep appearing, the Claude job sends false positives, and you cannot tell what it missed.
Customers complain, and the root cause is guesswork.
And you are only checking outputs. What about outcomes? An agent that replies politely and solves nothing still passes.
TwoTail is like having an evals data analyst on your team. Always reliably measuring what is happening and why, keeping your agent quality on track.
Your Forward Deployed Analyst
I’ve worked for some of Europe’s highest growth scaleups, and founded 3 AI startups. When you use TwoTail, you get me too. A powerful tool combined with an experienced operator figuring out the perfect way to measure and optimize your agent.
Timothy Daniell, founder Read the full storyFour steps to measure your agent.
From the first eval you write to an always-running live suite, TwoTail has everything you need to measure your agent in production.
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01
Define
Bring the evals you have, build them in TwoTail from your traces, or let TwoTail propose them for you.
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02
Annotate
Rate a smart-sampled slice of traces against your scoring rubric.
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03
Calibrate
Judges are continuously tuned to agree with your labels, grading every trace.
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04
Correlate
Every eval is tested against your business metrics, so you know what moves the needle.
We used to rely on a Claude routine to find issues with our agent, but it was unreliable and I started ignoring it. Now we have evals to measure when agent quality drops - and we are confident that when it pings us on Slack it’s not a false positive.
HaroldFounder, Sliq
⅓
of a founder's time reading summaries, replaced by Slack alerts he acts on
Read the case
The missing piece of Dark Matter. Super analytical to complement our creative team.
XavierFounder, DarkMatter
Timothy is an expert in data modeling. With his help, we now have a clear understanding of how users behave. We highly recommend working with him.
RandyFounder, Recharge360
Because they have so much experience in the field, they proactively find the edge cases and implement systems so those are handled correctly.
MarlonFounder, Assembly
Where are you on the ladder?
Answer up to 6 questions to figure out where you're up to.
From your labels to root cause.
Everything that makes TwoTail a complete eval analytics platform, organised to put the loop into practice.
Define
Annotate
Calibrate
Correlate
Act
Under it all
Ten minutes, and no code changes.
TwoTail reads the traces you are already producing. Point OpenTelemetry at it, or turn on the Langfuse integration and leave everything where it is.
Direct, via OpenTelemetry
Standard OTLP/JSON from any framework, straight to the TwoTail API. As easy as pasting the setup prompt into your coding agent.
Through Langfuse
Already tracing with Langfuse? Turn the integration on and TwoTail reads the same feed. No code changes, nothing moves.
Real problems, solved end to end.
Figuring out which evals actually matter.
12 → 2 evals that actually predict editor approval
Read the case Case 02 · Customer-support chat agentSegment intent, measure success, find the root cause.
38% vs 82% resolution gap on one intent
Read the case Case 03 · Research-assistant startupCutting cost without losing quality.
−42% cost on the per-step call
Read the case Case 04 · SliqKnowing when a user is frustrated.
⅓ of a founder's time reading summaries
Read the caseClaude Code can write you an eval.
It cannot tell you it is right.
64%
How often frontier judges agree with you in specialised fields, straight out of the box. You would not ship a metric that was wrong a third of the time. Most eval setups do, and never find out.
vs. doing it yourself
A coding agent gives you a session, not a system. New failure taxonomy every rerun, a sample rather than your corpus, and no ground truth to check itself against.
vs. Langfuse
Langfuse gives you somewhere to put scores. It has no opinion on which scores you should have, or whether they mean anything. Most of our customers run TwoTail on top of it.
vs. Braintrust
Braintrust runs the evals you already wrote. TwoTail works out which evals are worth running, with you in the loop. No retention cliff, priced for growth-stage.
Reading on agent evals.
How to Write a Good LLM-as-a-Judge Eval
LLM-as-a-judge is a powerful technique for measuring AI quality. 10 characteristics of a good judge eval, from binary verdicts to recalibration.
Read the articleCommon questions.
You can, and you should. Claude Code reads traces, proposes failure taxonomies, and writes eval criteria well. What it cannot do is tell you whether its judgement is right: no ground truth from you, no agreement rate, a fresh taxonomy on every rerun, and a sample rather than your whole corpus. TwoTail is what it calls to get those. Your agent does the analysis; TwoTail makes it defensible.
Online. TwoTail grades your live production traffic continuously, every trace rather than a sampled test set. Offline suites tell you whether a change broke something you already thought of. Online evals tell you what your agent is actually doing to customers today. And when you do need offline checks, you can pull your latest calibrated evals and run them there too.
Langfuse gives you somewhere to put scores. It has no opinion about which scores you should have, or whether they mean anything. That is the gap TwoTail fills. Most of our customers run TwoTail on top of Langfuse: same OTel feed, different job.
Not out of the box. In specialised fields, frontier judges agree with you as little as 64% of the time. Calibrated against a few hundred well-chosen labels, they hold up, and the agreement rate is visible before you trust a single score.
Your data is stored in isolated Postgres databases with row-level security. Each account's data is fully segregated, and all connections are encrypted in transit.
There is a free trial, and plans are priced for growth-stage teams. Full details on the pricing page.
Measure your agent properly.
Setup in 10 minutes. First calibrated judge within a week.