AI agent validation · with Langfuse
Lineate gets agents validated and shipped. We record what your agent actually does, measure where it stands, turn that into tests every change must pass, and put a person's approval in front of each release. Langfuse is the tool behind it.
The approach in five steps, built on Langfuse
Why agents stall
An agent that works in a demo still has to earn its way into production. Most teams install tracing and stop there. Three gaps keep the agent stuck.
An agent takes many steps: it calls tools, reads data and makes decisions. Without a record of each input, output, tool call, latency and cost, every failure is a guess.
Without agreed test cases and scores, "it seems better" is the only evidence anyone has. That doesn't convince a product owner, a risk team or a regulator.
A new prompt, model or tool can make the agent worse in ways nobody notices until users do. Without a gate, every release is a gamble.
How we do it
Each step builds on the one before it. Langfuse is where the traces, test cases, scores and prompt versions live, so every decision has evidence behind it.
See what the agent actually does.
We add tracing to your agent through OpenTelemetry, so every run is recorded step by step: each input, output, tool call, latency and cost. Traces from the agent can sit next to the rest of your application and infrastructure telemetry.
A full trace of every agent run, with cost and latency for each step.
LANGFUSE · TRACINGKnow where the agent stands today.
We work with your team to pick representative cases from real traffic, including the hard ones, and agree what a good answer looks like. Then we score the current agent against them for quality, cost and latency.
A golden dataset of test cases and baseline scores that everyone agrees on.
LANGFUSE · DATASETS · EXPERIMENTSTurn "good" into checks that run on their own.
We write the evaluators that matter for your use case: LLM-as-judge prompts, rule-based checks, and human review queues for cases a model can't judge. We check the judges against your reviewers' scores, so you can trust them.
An eval suite that scores every experiment and keeps scoring live traffic after release.
LANGFUSE · EVALUATIONS · ANNOTATION QUEUESStop regressions before users see them.
Every change to a prompt, model or tool runs the eval suite in your CI pipeline. If quality drops below the baseline, or cost or latency goes past the limit you set, the build fails and the change doesn't ship.
A regression gate in CI, with thresholds for quality, cost and latency.
LANGFUSE · EXPERIMENTS · PROMPT MANAGEMENTA person signs off, with the evidence in front of them.
A named reviewer on your side approves each release after seeing the eval results. Where the agent takes high-impact actions in production, we design the step where it waits for a person before acting.
A release approval step and an audit trail: which version shipped, who approved it and on what evidence.
LANGFUSE · PROMPT VERSIONS · SCORESThen the loop keeps running. The same evaluators score live traffic after release, so a drop in quality shows up as a number on a dashboard, not as a complaint from a user.
What Lineate brings
Building AI products is one of our service lines, so we read a trace the way the agent's builders would. When an eval fails, we fix the retrieval, the tool call or the prompt, not only report it.
AI Product DevelopmentTraces, test datasets and eval scores are data, and they grow with every user. We've built data platforms on ClickHouse since 2018, so we model the trace data, join it with your business data and keep retention and cost under control.
Data EngineeringYour product owners, domain experts and risk team decide what a good answer is. We turn that into test cases and scoring rules, then check the automated judges against your reviewers before anyone relies on them.
Langfuse Cloud, or self-hosted in your AWS account with ClickHouse Cloud, BYOC or open-source ClickHouse. We set up masking, retention and access control from day one.
Hands-on sessions take your engineers and product managers through the whole loop on your own agent: tracing, prompt management, monitoring, datasets, experiments and evaluation.
The tool behind it
Langfuse is the open-source platform for tracing, evaluating and improving LLM applications, and it's now part of ClickHouse. Lineate is a ClickHouse Services Partner, so we set it up, tune it and run it alongside the rest of your data.
Traces, prompt versions, test datasets, evaluations, experiments and cost and latency metrics, all in the same tool.
Use Langfuse Cloud, or self-host it in your own AWS account so prompts, completions and user context never leave your VPC, which is usually what decides it for FinTech and HealthTech teams. Self-hosting means ClickHouse, Redis and S3, the kind of setup we already build and run.
Because the traces live in ClickHouse, we can join agent cost and quality with your business outcomes, such as resolution rate or revenue per session.
Framework-agnostic and open source under MIT: the same traces whether you use the OpenAI or Anthropic SDK, LangChain or your own agent loop. No framework to adopt.
Partnerships
The platforms your agents run on and run with.
Let’s talk!
