AgentStatus × Raindrop, a quick map of how we fit
The user-side 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 2,500+ nodes across 70 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 measure whether users can reach the agent, then whether it still passes its checks.
Reachability. Controlled validations from 2,500+ residential devices across 70 countries measure whether users can open the agent the way they do — past CDN, WAF, and bot walls. Multi-geo is observer vantage for access and last-mile latency — not answer localization by probe IP, and not agent tool egress.
Outcome verification. Once reachable, we verify outcomes: Scenario (did it finish the job?), Compositional (do the pieces hold together?), Safety (must-not-say / policy / attacks), Stability (same ask, same story?). Consistency and Drift track what changed. Job anchors and side-effects where they exist — not prose matching. Optional sample review is corroboration only; stably wrong still needs a domain expert.
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.
User-side validation is two separate jobs
Reachability
Outcome
Can we talk to it?
Residential observers take the inbound path customers take — past CDN, WAF, and bot walls that treat datacenter synthetics differently. Monitoring asks: is it healthy right now? Reliability asks: does it keep working over time? “Up” means reachable from home networks, not from AWS.
Outcome verification
Outcome
Did it do the right thing?
Reachable and wrong is still broken. Scenario — did it finish the job? Compositional — do the pieces hold together? Safety — must-not-say, policy, attack probes. Stability — same ask, same story? Scores: Consistency and Drift. Not prose matching. Optional sample review is corroboration only.
Where we fit
We sit beside the platform. We do not replace it.
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 residential observer vantage — catching path-dependent access failures even when aggregate traces look fine. Multi-geo is inbound reachability, not a claim that probe IP changes agent tool egress or answer content.
User-side 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
2,500+ residential nodes across 70 countries prove we are not synthetic from a single cloud region. Buyers who distrust lab-only validation get inbound reachability evidence across geographies — access and last-mile path, not a claim that probe IP localizes answers or agent tool egress.
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
How the work divides
Your platform
- • SDK-backed production monitoring
- • Step-by-step traces & Deep Search
- • Automatic issue detection → Slack
- • Experiments on real traffic
- • PII Guard / SOC 2 Type II
Outcome
System of record
Dashboards, exports, lifecycle tools, and orchestration remain yours. We do not replace that surface.
AgentStatus
- • Continuous validate traffic
- • Expected-answer checks & drift detection
- • Multi-turn / multi-agent journeys
- • Real-network execution evidence
- • 2,500+ nodes across 70 countries
Outcome
User-side layer
Reachability (Monitoring / Reliability) from residential networks, then outcome verification once reached — Scenario, Compositional, Safety, Stability; Consistency and Drift scores. Not prose matching.
Proof of scale
Auditable scale metrics
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
We are an independent layer that runs alongside your stack.
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 user-side validate outcomes with inside-out production signals.
What we'd like from this conversation
These three asks would move a pilot forward.
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 user-side 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.
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.
Contact·dulra@carmel.so·roman@carmel.so
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.