No. ActionProof drives synthetic conversations against a development, staging or dedicated test environment. Production monitoring is a separate capability that is coming later.

Model-based judging only grades the text. We additionally verify tool calls, their arguments and the resulting business outcome deterministically, so an agent cannot pass by sounding confident.

Yes. Any agent reachable over HTTP works, LangChain, LangGraph, CrewAI, vendor platforms or a fully custom stack. You map the request and response fields once during setup.

Every workspace starts with twenty scenarios covering refunds, cancellations, shipping, escalation and privacy. You write your own alongside them, and any failure can be promoted into a permanent regression case. Generating scenarios from your own policies and knowledge base is planned, not built.

Scenarios use synthetic customers and synthetic records by default. Test runs point at sandboxed tools and APIs, so no real refunds, cancellations or account changes occur.

Yes. Trigger suites from GitHub Actions or any CI system, gate releases on critical regression tests and receive results via webhook or Slack.

Yes. A synthetic caller speaks the same scenarios you run over chat, and we transcribe what the agent says back. Latency is reported as three separate measures, endpointing delay, time to first audio and speech duration, because one number cannot tell you whether to tune the voice detection or the model. Agents reachable over our voice bridge work today; dialling a phone number needs a SIP trunk, which is in progress.