AgentStatus × Parloa, a quick map of how we fit
Independent verification for Parloa's AI 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 Parloa's lifecycle platform and exports (Data Hub, Transcripts, AMP). We don't replace them.
What we understand about Parloa
A lifecycle platform with strong inside-out visibility.
Parloa's AI Agent Management Platform spans design, test, scale, optimize, and secure, with strong inside-out visibility: performance and compliance-oriented workflows, insights dashboards, Data Hub for event-level data into BI stacks, and Transcripts API for conversation-level depth.
For text integrations, TextchatV2 is a clear HTTP model: a dialoghook endpoint with a release id and Bearer token, per Parloa's public API specification.
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.
Outside-in vs inside-out
Parloa sees what happens inside the platform and what you export to your stack. AgentStatus answers a different question: what did the real channel actually do for a user-like validate from a specific geography, network path, and latency profile?
Production truth, not only dashboards
Aggregate metrics can miss regressions that only show up on certain paths. Distributed execution makes those failures easier to catch early, before they become executive-level incidents.
Global execution footprint
800+ nodes across 30 countries is the proof we are not 'synthetic from a single cloud region.' It matters for global CX and for issues that only reproduce from specific locations or networks.
Partner-friendly integration posture
We do not assume we can 'discover' Parloa customers the way some web-widget vendors can be scraped. Credential-based surfaces (for example TextchatV2) and customer-approved monitoring are the right model, aligned with enterprise trust.
The split
Two truths, one story.
Parloa, Inside-out
- • Design, test, scale, optimize, secure
- • Insights dashboards
- • Data Hub event exports
- • Transcripts API
- • AMP lifecycle workflows
AgentStatus, Outside-in
- • Continuous validate traffic
- • Expected-answer checks & drift detection
- • Multi-turn / multi-agent journeys
- • 800+ nodes / 30 countries
- • Alerting & evidence trail
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 Parloa's Data Hub or Transcripts API. We are an independent layer that can coexist with them, and, where useful, help teams correlate outside-in validate outcomes with inside-out conversation truth.
What we'd like from this conversation
Asks.
A 2-week sandbox pilot
A sandbox TextchatV2 release (release id and API token), a set of agreed scenarios with expected answers, and a 2-week evaluation window. No production traffic, no end-customer 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 enterprise security reviews. Data handling, least privilege, audit evidence, and clear test-traffic boundaries.
Where independent proof is most useful
Whether the right starting point is Parloa-internal QA, a joint customer scenario where the buyer wants third-party evidence alongside Data Hub and transcripts, or both.
Closing
Parloa helps enterprises build and operate serious AI agents. AgentStatus helps enterprises prove, continuously, that those agents behave the way policy and customers 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 Parloa references above reflect Parloa's public product pages and documentation as of the date of this note.