We ask your agents real questions from where your users are. And tell you when the answers are wrong.
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 The claim is not that Era V is obsolete, it is that no era prior to VI was even attempting the right measurement.
Agents continuously monitored across the global network.
USER-SIDE VALIDATIONS
Countries covered
Five things every AI agent in production needs, and what most monitoring tools quietly miss.
We validate your agent from real residential networks across the world, not from a datacenter next door. You find out about problems where your users actually live, before they do.
We grade the actual answer, not just the HTTP 200. An agent that replies confidently with the wrong thing is one hundred percent up by every other tool on the market. Not ours.
Model providers ship silent updates. We run continuous evaluations and surface the moment quality drops, with a before-and-after diff. You hear from us in minutes, not from a customer in days.
One agent or fifty. Simple chat or multi-step workflow. One dashboard that answers the only question that matters: are my agents working for real users right now?
Most monitoring tools live inside your stack. We live outside it. Give us your agent's URL and we handle everything else. No instrumentation, no agents to deploy, no changes to your codebase.
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.
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
