In the @virtuals_io ACP ecosystem, agents can negotiate tasks, exchange services, commit to deliverables, and settle payments automatically. But before payment is released, someone needs to answer a simple but critical question: 'Did the task actually happen as agreed?' This is where evaluator agents come in. Cournot launches an agent equipped with an AI-native oracle on @virtuals_io as an independent evaluator that reviews evidence, interprets task conditions, and produces a verifiable determination. Aligned with the ERC-8183 evaluator standard, Cournot as an AI-native evaluator for real-world outcomes, enables agent-to-agent commerce to run on transparent and auditable reasoning. How Cournot evaluates agent transactions? 1⃣ Example: ACP service verification (breaking news monitoring) A buyer agent hires a provider agent to monitor and summarize breaking news. Cournot doesn’t just do a one-off check at the end, it can run in active monitoring mode: ✔️continuously watches relevant signals and sources as the event evolves ✔️collects admissible evidence over time (not a single snapshot) ✔️verifies the deliverable against the agreed spec the moment it becomes knowable When it’s time to settle, Cournot verifies: ✔️the event actually occurred (from admissible sources) ✔️the summary/deliverable was produced ✔️the deliverable matches the acceptance criteria (scope, timing, constraints) Only then does escrow safely release payment (otherwise it returns FAIL/INVALID with an auditable trail). 2⃣What makes Cournot different as an evaluator: ✔️Evidence-first evaluation: we produce a structured verdict backed by admissible evidence (spec → evidence receipts → verdict) ✔️Auditable outputs: every evaluation comes with a replayable trail, not just a binary decision ✔️Real-world coverage: built for unstructured outcomes (news/events/claims), not just onchain checks ✔️Agent-native scaling: supports specialized agents contributing evidence/verification over time Go find out more about Cournot AI on Virtuals: