AgentStatus × Raindrop, a quick map of how we fit
The outside-in layer for Raindrop customers.
AgentStatus is how teams prove behaviour in the wild: independent, distributed production assurance for AI agents, continuous checks, gold-based expectations, and alerting, run from 800+ nodes across 30 countries. We sit alongside Raindrop's SDK-backed monitoring, traces, issue detection, and experiments. We don't replace them.
What we understand about Raindrop (public)
"Sentry for AI agents", catch silent failures evals miss.
Raindrop positions as "Sentry for AI agents": catch silent failures in production that evals miss, surface issues automatically, route teams through Slack, and make failures actionable with step-by-step traces across conversations, tool calls, and decisions.
Public messaging emphasizes detect → trace → track → understand → fix, including plain-language monitoring ("describe it, then track it") and experiments to validate changes against real production behaviour.
Raindrop also highlights enterprise security positioning such as PII Guard and SOC 2 Type II compliance on raindrop.ai.
What AgentStatus is
We continuously test your AI agents and check if the answers are correct.
AgentStatus continuously tests production and staging agent surfaces against known-correct answers, watches for drift, and alerts your team when behaviour diverges or breaks, so you have clear, repeatable evidence when something changes.
That includes multi-turn flows and multi-agent journeys when customer paths span tools, escalations, and handoffs, and it supports governance and risk conversations when stakeholders ask what was exercised, from where, and what changed.
Where we fit
Complement, not overlap.
Instrumented production vs independent validations
Raindrop shines when your product is instrumented and you can observe what actually happened for real users. AgentStatus answers a complementary question: what happens when we exercise the same surface on purpose from a specific geography, network path, and latency profile, including failures that are path-dependent even when 'everything looks fine' in aggregate traces.
Outside-in truth
Bot protection, regional routing, and third-party dependencies can create green dashboards and bad reality. Distributed execution is built to reduce that blind spot.
Global execution footprint
800+ nodes across 30 countries is the proof we are not 'synthetic from a single cloud region.' That matters when your buyers care about global behaviour, not lab-only validation.
Partnership-friendly framing
The strongest joint story is often: Raindrop triages what users did; AgentStatus proves what controlled validations saw from many places, then you correlate. We are not pitching 'replace the SDK.'
The split
Two truths, one story.
Raindrop, Inside-out
- • SDK-backed production monitoring
- • Step-by-step traces & Deep Search
- • Automatic issue detection → Slack
- • Experiments on real traffic
- • PII Guard / SOC 2 Type II
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 Raindrop's automatic issue detection, trace UX, Deep Search, or experimentation platform. We are an independent layer that can coexist, and, where useful, help teams reconcile outside-in validate outcomes with inside-out production signals.
What we'd like from this conversation
Asks.
A 2-week joint pilot
One customer archetype, one set of expected answers, and a 2-week evaluation period. We run the validations, you see the outside-in evidence next to your inside-out signals, and we share a short joint summary at the end.
Integration posture
What a clean "Raindrop + AgentStatus" story would look like for buyers (even if integration is initially manual via timestamps and incident IDs).
Validate the complement
Where you see independent distributed validating as additive versus redundant for your customers, so we can sharpen the joint narrative.
Closing
Raindrop helps teams see and fix what their agents did in production. AgentStatus helps teams prove, continuously, what their agents will do when exercised like real global traffic , 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. Raindrop references above reflect public marketing on raindrop.ai as of the date of this note, not an endorsement by Raindrop.