AgentStatus × Platform Partners
Outside-in monitoring for the AI you build, and the customer deployments running on it.
CCaaS and UCaaS platforms are shipping agentic AI fast. Platform monitoring tells you the system is up. Your customers' CISOs want different evidence — is the agent doing the right thing this week, from where users are, across capabilities? AgentStatus is the outside-in layer that answers both.
What we do
What we do, in one paragraph.
We send realistic customer-style questions to your AI agents from real consumer devices in 30 countries. We log what comes back. We compare the responses to what a working agent should say — not just "did it respond?" but "did it respond correctly for the business it's serving?" We retain the evidence so it stands up in front of an auditor.
The result is something internal monitoring architecturally can't produce: an independent record of how your AI actually behaves in real users' hands, week after week.
Two ways in
Two ways AgentStatus shows up for your platform.
Direct
The AI products you ship are your competitive position. Outside-in evidence tells your team how they behave in customer hands — across regions, channels, and capabilities. Catch regressions, prove reliability, back the enterprise sales motion.
ISV partnership
Your customers deploy agents on your platform. Their CISOs and procurement teams want independent evidence those agents work. AgentStatus surfaces in your marketplace as the reliability layer — co-sell, listing, revenue share. The differentiator your enterprise pitch leans on when buyers ask the hard questions.
Most platforms running both their own AI and customer deployments eventually want both.
What we find
What outside-in monitoring typically surfaces.
The same patterns show up across the platforms we've monitored:
Agents that are technically up but behaviorally adrift.
They respond. The transport is fine. But the answers are generic, ungrounded, or off-topic for the business they're meant to serve. Internal "is it responding?" checks pass. The customer experience tells a different story.
Per-tenant variance hidden inside aggregate platform health.
Two of your customers running the same crew template can be 20 points apart on reliability, week after week. Platform-level monitoring averages it out. Outside-in surfaces it tenant by tenant.
Region-specific failures invisible to centralized telemetry.
Agents pass internal evals and cloud-region uptime checks but consistently fail from specific residential ISPs in regions where your customers' users actually live.
Concentrated failure modes hidden in broad pass rates.
An 82% pass rate looks fine until you discover 80% of the failures are concentrated on one specific user intent — turning a "broad reliability problem" into a one-line fix.
Why this fits
Why this matters for platforms specifically.
Your customers' CISOs and procurement teams are getting harder. Self-reported platform metrics don't pass enterprise scrutiny in regulated verticals anymore. Independent third-party outside-in evidence is what gets agents into production — and keeps them there.
Bringing AgentStatus into your alliance program means your enterprise pitch includes an answer to "how do we verify the agents are actually working?" Most platforms don't have one. That's the differentiator.
The ask
What we're proposing.
A two-week structured pilot. Direct on your AI infrastructure, ISV-shaped on three to five customer deployments, or both. We align scope with your team upfront. Weekly reports. Honest finding at the end: did outside-in surface things your internal monitoring didn't?
If yes, the natural shape depends on which motion landed: direct contract, formal ISV partnership with revenue share and co-sell, or both.
Closing
Your platform runs AI. Your customers run AI on your platform. We're the outside-in layer for both. We'd love to hop on a call and figure out where AgentStatus fits — direct, ISV, or both.
AgentStatus is independent outside-in production monitoring for AI agents. Validations target publicly-reachable customer-visible agent surfaces with conservative rate limits and customer approval. Findings cited above are representative patterns from monitoring on multi-tenant agent fleets, anonymized.