Introducing Brizz: Product Analytics for AI Agents

Ship an AI agent and the first real question is blunt: is it any good? For most teams, the honest answer is that they cannot tell. Their dashboards fill with sessions, response times, and the occasional thumbs-up, yet none of it explains what the agent actually did, where it quietly went wrong, or whether any of it changed a number the business cares about. We built Brizz because we kept hitting that same wall.

The Problem with Traditional Analytics

Traditional analytics tools were built for a world of buttons, forms, and page views. You defined an event, fired it when a user clicked, and counted the results. That model works when the product behaves the same way every time. AI agents do not. Every conversation is a little different, the interface is open-ended language instead of a fixed set of buttons, and the path a user takes is generated on the fly. Counting page views in that world tells you almost nothing.

When a user talks to your agent, the surface metrics you inherit from web analytics miss the parts that decide whether the product succeeds:

1. What did the agent actually do? Session duration cannot tell you if the agent understood the request or took the right action. A long session can mean a user was engaged, or that they were stuck repeating themselves.
2. Where does it fail? The failures that hurt most are rarely clean errors. They are subtle: a confident answer that happens to be wrong, an intent the agent misread, a hand-off it should have made and did not. None of these throw an exception, so none of them show up in a traditional error rate.
3. What is the business impact? Even when you can see that a conversation went well, most tools leave you guessing how that connects to retention, conversion, or support cost. The link between a single agent interaction and an outcome your team reports on stays invisible.

There is a deeper reason these tools fall short. Classic analytics assume determinism: the same input produces the same output, so you can decide in advance what to measure. Agents are probabilistic and open-ended, which means the interesting behavior is emergent. You cannot write an event for a failure mode you have not seen yet. You need something that reads the conversations and tells you what is happening, rather than waiting for you to instrument it ahead of time.

How Brizz Is Different

Brizz is purpose-built for AI agent analytics. Instead of asking you to define events up front, we analyze every conversation and surface the patterns, so product teams (not just engineers) get the visibility they need. Three capabilities carry most of that weight.

Intent Classification

Brizz automatically identifies what users are trying to accomplish in each conversation. Rather than tagging transcripts by hand, you see intents cluster on their own: the common requests, the ones trending up this week, and the ones your agent handles badly or cannot handle at all. That last group is the valuable one. The gap between what users ask for and what your agent can do is a product roadmap written by your users, and it is almost impossible to see without reading every conversation. Brizz reads them for you.

Quality Scoring

Every session gets a quality assessment based on user satisfaction signals, task completion, and behavioral patterns. The point is not a single vanity score. It is triage. When you have thousands of conversations a day, you cannot review them all, so you need to know which handful deserve a look. A quality score that reflects whether the user actually got what they came for lets you jump straight to the sessions that went wrong, learn from them, and confirm that the ones you assumed went well really did.

Actionable Alerts

Insight that arrives a week late is a post-mortem, not a fix. When something changes, a spike in negative interactions, a new intent pattern, or a performance regression after a prompt change, Brizz alerts your team in Slack with the context and a suggested next step. The alert lands where your team already works, and it carries enough detail that the first response can be a decision instead of an investigation.

Put together, these turn a pile of transcripts into three questions you can finally answer: what are users asking for, how often are we getting it right, and what just changed.

Getting Started

Integrating Brizz takes minutes, not a migration. The SDK is OpenTelemetry-compatible, so it sits alongside the tracing and infrastructure you already run rather than replacing any of it:

from brizz import Brizz, start_session

Brizz.initialize(
    api_key='your-brizzai-api-key',
    app_name='my-app',
)

with start_session('session-123') as session:
    session.update_properties(user_id='user-123', model='gpt-4')
    response = openai.chat.completions.create(
        model='gpt-4',
        messages=[{'role': 'user', 'content': 'Hello'}],
    )

You wrap a session, attach the properties you care about (a user id, the model in use), and let your agent run as usual. Within minutes of the first session, insights start showing up in your dashboard: the intents behind the conversations, the quality of each one, and the first patterns worth acting on. Because the SDK follows the OpenTelemetry standard, your data is not locked in a black box. It fits the observability stack your engineers already trust.

The Bottom Line

The teams shipping the best AI agents are not the ones with the most dashboards. They are the ones who can see, in plain terms, what their agents are doing and where they break. Traditional analytics were never built to answer that, and bolting more page-view metrics onto an agent will not get you there. Start by reading the conversations, because that is where the truth about your product already lives, and build from what they tell you.

Ready to see what your agents are really doing? Get in touch.