Outside-in monitoring for T-Mobile's customer service AI, from real residential devices on real carrier networks.

What we understand about T-Mobile

Customer service AI at carrier scale.

T-Mobile runs AI-powered customer service agents at massive scale, handling billing questions, plan changes, device troubleshooting, and account support for over 100 million customers. Your AI engineering team is building toward a future where AI handles the majority of customer interactions end to end.

T-Mobile has invested heavily in being an "AI native" company at enterprise scale. That ambition means your customer service AI agents carry real weight. When they work, they delight customers and reduce cost. When they drift or degrade, the impact is measured in NPS points and churn, not just error logs.

The inside-out story, internal dashboards, resolution rates, deflection metrics, tells you what happened in your stack. It doesn't tell you what a real customer on a residential T-Mobile connection in rural Texas experienced 8 minutes ago, or whether the agent gave them the right answer about their bill.

Why this matters uniquely for T-Mobile

You are a carrier. Your customers are on real networks.

T-Mobile is a carrier. Your customers are literally on mobile and residential connections. AgentStatus validations from 800+ real residential devices across 30 countries, the same conditions your own customers are on. No other monitoring tool tests from real consumer networks.

This is uniquely relevant for T-Mobile. You can test your customer service AI from T-Mobile network connections, Verizon connections, AT&T connections, and see if behavior differs. If a customer on a T-Mobile home internet connection in Tulsa gets a different answer than a customer on Verizon in Dallas, you'll know before they call back angry.

What AgentStatus is

Outside-in assurance for production AI agents.

• • Validations that fire from 800+ real consumer devices on residential ISPs and carrier networks, not AWS.
• • Gold prompt profiles: you define what the right answer looks like, we test against it continuously.
• • Multi-turn validation: we test full conversations the way a real customer would have them.
• • LLM-as-Judge evaluation: semantic correctness, not just HTTP 200.
• • Carrier-aware validations: run the same scenarios from T-Mobile, Verizon, and AT&T connections side by side.
• • Failure attribution: network issue vs model regression vs geographic variance.

The split

Two truths, one story.

On the same side as your customers

Catch the wrong answer before the customer does.

Every wrong answer a T-Mobile customer service agent gives is a customer who gets off the phone more frustrated than when they called. They tell their family. They post about it. Some of them churn.

AgentStatus exists to catch those wrong answers before the customer does. We are not selling you another infrastructure dashboard. We are sitting on the customer's side of the line and telling you, in real time, when the answer they just got was wrong.

The goal isn't uptime. The goal is that every T-Mobile customer gets the right answer, every time, from wherever they are.

What we are not claiming

An independent layer that coexists.

AgentStatus doesn't replace your internal resolution dashboards, QA workflows, or escalation tooling. We don't have visibility into your knowledge base, your training data, or your CRM. We are an independent layer that validates what real customers experience in the wild, so you can prove your agents work to customers, compliance, and leadership without asking anyone to trust your own telemetry.

What we'd like from this conversation

Asks.