AgentStatus × Roots Automation, a quick map of how we fit
Independent verification for Roots' insurance-trained agents.
We continuously test AI agents from outside your stack and check whether the answers are correct, across the channels each platform supports, from 800+ nodes across 30 countries. We sit alongside Roots' AI agent library and multi-system integrations. We don't replace them.
What we understand about Roots
Insurance-trained AI agents for claims and underwriting.
Roots is the AI agent platform built for insurance, purpose-built agents for claims and underwriting, with insurance brains embedded into the platform itself. The agent library covers the lifecycle: submissions intake and triage, loss history access for underwriters, premium audit at 98%+ accuracy, FNOL/FROI automation, and policy management.
Roots positions itself as a transparent, ethical alternative to BPOs and to general-purpose LLMs, with multi-system process automation that integrates into the tools insurance teams already use daily, and a Trust Center built around the data security expectations of carrier customers.
What AgentStatus is
We continuously test your AI agents and check if the answers are correct.
We send controlled test calls, messages, and emails to your production and staging agents from a global network. Then we compare each answer to a library of known-correct answers ("expected answers") for that scenario. When something drifts or breaks, we flag it with the evidence attached.
That includes multi-turn conversations and multi-agent journeys when customer paths span tools, escalations, and handoffs. It supports governance and risk conversations when stakeholders ask what was tested, from where, and what changed.
Where we fit
Complement, not overlap.
Insurance-trained agents vs production drift
Roots ships agents trained on insurance, that's the foundation. AgentStatus answers the next-layer question: what did the deployed agent actually do for a user-like validate today, given a specific submission, claim type, or geography, and did the answer drift from what the expected answer says it should be?
Premium audit accuracy vs ongoing accuracy
A 98%+ accuracy figure is a strong inside-out signal at evaluation time. Distributed validate traffic catches the cases where that accuracy starts slipping in production, before a renewal cycle is priced wrong or a claim is mis-triaged at FNOL.
Global execution footprint
800+ nodes across 30 countries is the proof we are not 'synthetic from a single cloud region.' It matters for carriers operating across multiple geographies and for insurance failure modes that only reproduce from specific networks, jurisdictions, or partner integrations.
Partner-friendly integration posture
We do not assume we can 'discover' Roots customers the way some web-widget vendors can be scraped. Credential-based surfaces (agent endpoints, sandbox environments, customer-approved monitoring) are the right model, aligned with the security posture Roots already maintains for its carrier customers.
The split
Two truths, one story.
Roots, Inside-out
- • Insurance-trained agents
- • Claims & underwriting library
- • Multi-system automation
- • Premium audit accuracy
- • Trust Center & data security
AgentStatus, Outside-in
- • Continuous validate traffic
- • Expected-answer checks & drift detection
- • Multi-turn / multi-agent journeys
- • Real-network execution evidence
- • 800+ nodes across 30 countries
Proof of scale
Plain definitions, no inflation.
In about two months, we have executed on the order of 18 million validate runs across the network. We also maintain on the order of 6,000 agent records in our system, meaning rows/configurations we track, including evaluation and pipeline agents, not "6,000 paying customers."
If helpful, we can share stricter production-only definitions under NDA.
What we are not claiming
An independent layer that coexists.
We are not a replacement for Roots' agent library, insurance training, or multi-system integrations. We are an independent layer that can coexist with them, and, where useful, help carriers correlate outside-in validate outcomes with inside-out agent performance, so claims and underwriting leaders have continuous evidence the deployed agent is still behaving the way regulators and customers expect.
What we'd like from this conversation
Asks.
A 2-week sandbox pilot
A sandbox agent (FNOL triage, premium audit, or underwriting submission), a set of agreed scenarios with expected answers, and a 2-week evaluation window. No production traffic, no policyholder data. At the end you get a written report of what we tested, what passed, and what drifted.
Security and procurement posture
How AgentStatus should connect in a way that satisfies carrier security reviews. Data handling, least privilege, audit evidence, and clear test-traffic boundaries aligned with Roots' Trust Center posture.
Where independent proof is most useful
Whether the right starting point is Roots-internal QA, a joint carrier scenario where the buyer operates under regulatory and audit requirements (primary, P&C, life), or both.
Closing
Roots helps carriers build and operate AI agents trained on insurance from day one. AgentStatus helps those same carriers prove, continuously, that those agents behave the way regulators, auditors, and policyholders require, globally, with evidence that holds up under scrutiny.
Metrics are stated with explicit definitions: validate runs are scheduled executions over ~two months; agent records are database rows, not revenue customers. Public Roots Automation references above reflect Roots' public product pages, agent library, and Trust Center documentation as of the date of this note.