AgentStatus × Kore.ai, a quick map of how we fit
Independent verification for Kore.ai's enterprise 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 Kore.ai's built-in observability, analytics, evaluation, and governance. We don't replace them.
What we understand about Kore.ai
An enterprise agent platform with strong inside-out visibility.
Kore.ai positions an enterprise AI agent platform for work, service, and process: multi-agent orchestration, Search / RAG and connectors, Model Hub, Prompt Studio, and Evaluation Studio for model and agent quality work.
Public materials emphasize built-in observability (tracing, analytics, monitoring events, audit-oriented visibility), AI safety and guardrails, and enterprise-grade security and compliance, plus no-code, low-code, and pro-code paths and broad integrations across channels and business systems. On the XO / contact-center side, Kore.ai also markets deep operational analytics, conversation dashboards, quality and coaching workflows, exports and diagnostics for real-world troubleshooting.
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
Kore.ai gives enterprises strong inside-the-platform visibility: traces, analytics, conversation intelligence, evaluation workflows, and guardrails. AgentStatus answers a complementary question: what did the real channel actually do for a controlled validate from a specific geography, network path, and latency profile, including failures that only show up outside the vendor's own telemetry.
Production truth beyond aggregate health
Dashboards and platform-native signals can still miss path-dependent regressions (WAFs, regional routing, third-party dependencies, consent flows). A distributed execution mesh is purpose-built to surface those early.
Global execution footprint
800+ nodes across 30 countries is the proof we are not 'synthetic from a single cloud region.' That matters for global deployments, regulated industries, and buyers who distrust single-region checks.
Partner-friendly posture
We assume consenting, credential-based access to customer endpoints (or official integration patterns Kore.ai prefers). We do not pitch 'covert scanning of every Kore deployment on the public internet.'
The split
Two truths, one story.
Kore.ai, Inside-out
- • Multi-agent orchestration
- • Model Hub & Prompt Studio
- • Evaluation Studio
- • Built-in tracing & analytics
- • Guardrails, security & compliance
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 Kore.ai's Evaluation Studio, XO analytics and quality workflows, or platform-native observability. We are an independent layer that can coexist, and, where useful, help teams reconcile outside-in validate outcomes with inside-out traces, transcripts, and operational reports.
What we'd like from this conversation
Asks.
A 2-week sandbox pilot
A sandbox bot on a specific channel, 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 Kore.ai-internal QA, a joint enterprise scenario where the buyer already mandates independent controls alongside Evaluation Studio and XO analytics, or both.
Closing
Kore.ai helps enterprises build, deploy, and operate AI agents at scale with strong governance and observability inside the platform. AgentStatus helps those same enterprises prove, continuously, that customer-facing behaviour still matches policy and expectations in the real world, 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. Kore.ai references above reflect public marketing and documentation as of the date of this note, not an endorsement by Kore.ai.