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 2,500+ real residential devices across 70 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

We provide outside-in assurance for production AI agents.

• • Validations that fire from 2,500+ 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 scoring with a known ceiling — not a substitute for domain experts on stably wrong but consistent answers; and 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 observer-path / access variance (CDN/WAF) — not correctness-by-origin.

The split

How the work divides

How the work divides

Their platform

T-Mobile, Inside-out

• • Resolution rates
• • Deflection dashboards
• • Transcript quality
• • Escalation flows
• • Internal QA

Outcome

System of record

Dashboards, exports, lifecycle tools, and orchestration remain theirs. We do not replace that surface.

AgentStatus

AgentStatus, Outside-in

• • Residential validations from real carrier networks
• • Gold prompt profiles
• • Multi-turn conversation validation
• • LLM-as-Judge semantic checks
• • Failure attribution

Outcome

Outside-in layer

Residential inbound path past CDN/WAF, then gold, consistency, and scoped judges once the agent is reachable.

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 failures before the customer does. We are not selling you another infrastructure dashboard. We sit on the customer's side of the line and tell you, in real time, when residential/carrier observers can't reach the agent — or when gold, contract, and consistency checks fail once they can.

The goal isn't uptime alone. The goal is reachable agents that still pass their own checks from the networks customers actually use.

What we are not claiming

We are an independent layer that runs alongside your stack.

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

These three asks would move a pilot forward.