A team running an AI agent is responsible for a product they cannot really see. Users do not click buttons. They type, in their own words, for the thing they actually want. Locus was built to give the team that picture. Here is what it is, why we built it, and how it works.
Every company building an AI agent has the same question. What are our users actually using this for? It sounds simple. It is not. The people who pay for the product do not click a button called writing or a button called code. They type in plain words. They ask for help. They describe a goal. The product figures out the rest. That leaves the team with a pile of conversations and no clear way to read them.
Locus reads the pile. It groups every conversation by what the user was trying to do. It groups every user by what they spend their time on. It rolls the whole user base up into a handful of behaviours the team can talk about. You open it in the morning. You see the shape of your product in under a minute.
Locus is product analytics for AI agents.
Product analytics for a normal web app is built around events. A user clicks a button. An event fires. The event goes into a database. A dashboard counts it. You end up with charts of how many people clicked what, when, and in what order.
AI agents do not work that way. There are no buttons to click. The user types a sentence. The agent reads it, thinks, sometimes runs a tool, and writes back. The whole thing is a conversation. None of the older analytics tools know what to do with a conversation. They can count that it happened. They cannot tell you what it was about.
So teams end up guessing. The product manager reads twenty conversations a week and calls that research. The VP asks how the product is going and gets a vague answer. The roadmap gets built on a feeling, not on a real read of what users are doing. Locus was built to fix that.
The problem, in simple terms.
A team running an AI agent has to make calls about a product they cannot really see. Here is a concrete example. You ship a coding agent. Half your users are doing frontend work. The other half are writing backend services. A smaller third group is using it to learn a new framework. Each of those three groups wants something different from the product.
Today, there is no tool that shows you those three groups. Observability tools show you one conversation at a time. Dashboards roll every conversation into a single number called active users. Nothing sits in the middle, which is the place where product decisions actually get made. Locus fills that gap.
Three things teams need to know and cannot find today.
- What percent of your users mostly do each kind of thing in your product.
- How one specific user actually spends their time when they use you.
- Which group of users matches a specific profile, for a beta, a research call, or an outreach list.
Any team running an AI product has been asked at least one of these questions this week. Most of them had to make something up to answer it.
How Locus works.
Imagine one user of your AI agent. Over the last thirty days, they had eight conversations. Four were about frontend work. Two were about deployment scripts. One was about a bug. One was about writing an email. That is this user's mix. Half frontend, a quarter deployment, an eighth bug, an eighth writing.
Every user of your product has a mix like this. Some users look alike. Those users form a group. A group is a set of users who spend their time on the same kinds of things. It is not their plan tier. It is not the country they signed up from. It is what they actually do.
Roll up every group and you get the shape of the whole product. Forty percent of your users are frontend first. Thirty percent are backend first. Twenty percent are writing first. That is the top view. That is what you open Locus to every morning.
The three ways you look at your users.
Locus has three zoom levels. They all use the same picture. Only the thing being measured changes. Once you read one view, you can read every view. That is the whole design.
The whole user base.
A single horizontal bar across the top of the page. It shows how your whole user base breaks down by group. You read it in five seconds. This is the view you start every session with. It tells you the shape of your product right now.
The users inside one group.
Click any colour on that bar and you zoom into that group. You see every user in it. Each user has their own small bar showing how they personally spend their time. You can sort them by how typical they are, how unusual they are, or how active they have been. You find the person you want to talk to in under a minute.
The conversations one user had.
Click a user and you see every conversation they had with your agent. Each conversation has its own breakdown. A conversation is almost never about one thing. It might be seventy percent frontend and twenty percent deployment. Open any conversation and you read it the way the user did. Nothing is hidden behind a trace format only engineers understand.
What your team does with all of this.
Locus is not a thing to stare at. It is a thing to act on. Here are the six actions a team takes with it most often.
- Find the most typical user in a group. Useful when you want to set up a research call and need the person who best shows what that group does.
- Find the users at the edge. These are the ones whose behaviour does not cleanly fit any group. They are often the first sign of a use case your product does not have a name for yet.
- Build a cohort by behaviour. Users whose frontend share is above sixty percent, for example. You export the list and send it to your feature flag tool.
- Watch a single user drift. A user whose mix changes week over week is usually a week or two ahead of a churn event or a big expansion. Locus flags the change.
- Compare two groups side by side. Your writing team wants to know how writing users differ from research users. Locus puts the two shapes next to each other.
- Spot something new starting. A pattern growing inside an existing group often deserves to become its own group. Locus notices before you do.
Why Locus is different from everything else in the stack.
Every other tool in the AI stack does a specific job. Locus fills the gap between those jobs.
- Observability tools like Datadog and OpenTelemetry show you one conversation at a time. They tell you the system stayed up. They do not tell you what the user was trying to do.
- Trace stores like Langfuse, Braintrust, and LangSmith let you debug one response. They do not show you what your whole user base has been doing for the last thirty days.
- Eval tools score model output against a fixed test set. They tell you the model got the right answer on a prepared question. They do not tell you what your real users are asking for.
- Dashboards roll everything into one number. Active users. Sessions. Nothing about what those users actually did.
Locus reads from all of these tools. It does not replace them. It adds the one thing none of them do, which is show you what your whole user base is actually doing, broken down in a way that is clear enough to act on.
“You can keep your observability tool, your trace store, and your eval runner. You just need one more thing that sits between them and turns the data into something your product team can read.”
What Locus does not do.
We built Locus to be one thing and to be clear about what it is not. It is not an oracle. It does not tell you what to ship. It shows you the picture and steps out of the way.
- Locus does not tell your team what to build next. That is your job.
- Locus does not host your traces. It reads from where they already live.
- Locus does not run your evals. You keep Braintrust, Langfuse, or LangSmith for that.
- Locus does not replace your observability tool. Datadog keeps doing its job.
- Locus does not write your prompts, your specs, or your memos.
- Locus does not act on users automatically. Feature flags and outreach are one click exports. They are never done for you.
Who Locus is for.
Locus works for teams shipping an AI agent that is in production or close to it. You need enough conversation volume for the patterns to be real. That is around two thousand conversations a month, give or take. Below that, the groups are not stable and you are better off reading conversations by hand.
If your team is a handful of people, one product manager and a small engineering group, Locus pays off fast. You stop reading twenty conversations a week and guessing at the other ten thousand. You start every Monday knowing what actually moved.
If your company is larger and runs a few AI products, Locus is how the leadership team gets a real read on each one. The same view works for a VP, a product manager, a designer, and an engineer. They all see the same shape of the same users, at different zoom levels.
How Locus handles your data.
Locus reads from the trace store you already use. OpenTelemetry, Langfuse, Braintrust, LangSmith, Datadog, OpenAI, or Anthropic. There is no new SDK to install and no change to your application code. Your engineers do not have to ship anything.
We are SOC 2 Type II. We are GDPR compliant. Your data stays in your region. No content is used to train any model, ours or anyone else's. A DPA is available on request.
Where to start.
The fastest way to see what Locus does is to open the playground. It is a live version of the product with sample data. You can click through the three zoom levels in about ninety seconds.
If you want to see your own users, book a short call. We will pull a sample from whatever trace store you already use and show you your first memo while you watch. It takes thirty minutes. You keep the memo either way.