We validate your agents from real home networks. Then we check whether the answers stay right: consistency, policy, and graded quality when you have a reference.
WE VERIFY AND OBSERVE AI AGENTS BUILT ON:
Agents continuously monitored across the global network.
User-side validations run from real residential devices.
Countries covered on real home ISPs.
Outside-in validation is two separate jobs
Outcome
Your monitor hits the VIP lane. Users hit the WAF. Datacenter checks get blocked, throttled, or allowlisted. Residential observers take the inbound path customers take — so “up” means reachable from home networks, not from AWS.
Outcome
Reachable and self-contradicting is still broken. Rephrase flips, drift, and policy breaks need no ground truth. Gold and dual judges cover the rest when truth exists. Uptime grades none of that.
Nine checks from real home networks — starting with reachability and answer quality. On a schedule, with plain verdicts. No instrumentation. Just your URL.
Is it up right now? We validate from home networks on a schedule and give you a plain verdict - UP, degraded, or down - plus a run ledger and per-region view. Not a ping from your office.
Does it keep working over time? Pass rates, latency, time-to-first-byte, and week-over-week trends - so one green check does not fool you.
Slow shifts over time. We snapshot what normal looks like for your agent, then flag when behavior drifts away from it - day by day, with the rough runs worth a second look.
Reachable is table stakes. We grade the actual answer - spot checks, dual reviewers, format contracts, and domain-specific questions. A confident wrong answer is not up.
Does it finish the job? We give a simulated user a real goal and let them pursue it over several messages - then judge whether they got what they came for.
Same situation, same story. Rephrased questions, rising stakes, and follow-ups should not flip the answer for no reason.
Tools, streams, safety rules, and people trying to break it. Catch broken integrations and policy failures before customers do.
Why we flagged it. Every verdict comes with a probe trace - what we asked, what came back, which check failed, and a one-click reproduce so your team can fix it.
When it breaks, you know. Slack, webhooks, PagerDuty, email digests - with enough context to fix it, not just a red dot.
Test an agent live. Get results in 30 seconds.
Choose how to test:
GPU kernels reduce in nondeterministic order. The same logits, summed twice, do not produce the same logits.
The deterministic path is a marketing term.
Your prompt is served in a batch with other people's prompts.
Your answer depends on who else is querying the model right now.
MoE gating networks are themselves trained, and small differences in activation values route the same token to different experts.
The "model" you are calling is, at the level of computation, a different model on every call.
A small draft model proposes tokens; a large verifier accepts or rejects them. The accept boundary is stochastic.
The final text is shorter, faster, and not the same.
The model identifier did not change. The model did.
You will learn about it from your customers.
Non-determinism is a failure mode that, by construction, cannot be detected by inside-out tools.
Each era answered a question the previous one could not.
Note. Years are approximate; eras overlap and never fully retire. The claim is not that Era V is obsolete, it is that no era prior to VI was even attempting the right measurement.
OpenAI
Claude
Anthropic
Google
Azure
AWS Bedrock
LangChainLangGraphLangServeLangbase
Fetch.ai
Forethought
ElevenLabsElevenLabs Voice
Retell
Perplexity
Poe
DevinSwarmsVoiceflowBotpressCrewAIHuggingFaceGradioGoogle ADK / A2AA2A JSON-RPCNanda A2AAgent AIAgorAgenticAutoGenBlandBoostDecagonDifyMavenMCPn8nOpenAI AssistantsOpenAI CUATalkdeskuAgentVapi
