Scouts

Scouts is best understood as the managed execution layer on top of Yutori's computer-use models, not a dashboard product. The models are the reason to look: Navigator n1.5 grounds its reasoning in both screenshots and page DOM, executes JavaScript to extract from dynamic pages, and returns JSON conforming to a caller-defined schema — which is the difference between an agent that survives a site redesign and one that breaks on a renamed CSS class. Yutori published 97.3% on Online-Mind2Web (human evaluation, independently verified by the OSU NLP Group and Careerflow) and 89.7% averaged across Navi-Bench v2 and Westworld, at roughly 2.93 seconds per step and about $0.81 per task. Navigator n2 takes the same idea to full desktops on Linux, macOS, and Windows, combining mouse and keyboard actions with shell commands, and it is now the default model on the API at $0.50/M input, $0.05/M cached input and $4.00/M output. Strengths: the three-mode API (Browsing per step, Research per task, Scouting per scheduled run) maps cleanly onto how automation work actually arrives. Yutori Local solves the hardest practical problem in this category — agents logging into authenticated sites without raw credentials leaving the device. Connectors (Gmail, Slack, Notion, Linear, GitHub, Granola) and Live Artifacts that refresh as new data lands turn a monitoring script into something that produces a maintained output. The 5-minute no-signup browser playground is a genuinely low-friction way to test n1.5 on your own task before you write a line of code. Weaknesses and where it doesn't fit: cost scales with volume, not seats, so continuous monitoring across dozens of targets multiplies. If all you need is a fixed-fee page-change alert, a dedicated change detector costs less per month. It is API-first — Yutori Delegate exists for the no-code hand-off case, but the API itself expects a developer in the loop. Sessions in the playground cap at 5 minutes, and Enterprise terms (dedicated capacity, SLAs, on-prem) only come through custom pricing. Where it fits: developers building computer-use agents who want per-step metering and an OpenAI-compatible endpoint; ops and sales teams running continuous competitor or account monitoring; analysts and VCs who want multi-agent research billed per task instead of per seat; recruiters automating sourcing and outreach research. Where it doesn't: real-time low-latency applications that can't tolerate roughly 2.93 seconds per step, or anyone who needs a predictable flat monthly bill.