BenchRun Labs provides a benchmarking and QA platform for agentic workflows built on OpenClaw/MCP, addressing teams’ frustration that “agents never behave the same twice” and A/B testing is ad hoc. On GitHub and HN, devs are cobbling together scripts to replay tasks and compare agents, but there’s no standardized test harness for real-world computer-use sequences. BenchRun offers a scenario-based evaluation suite where you define tasks, environments, and success criteria, then continuously score agents. - Replayable task scenarios: record real desktop/browser flows once and replay with different models/prompts/skills. - Structured KPIs: success rate, time-to-complete, error classification, and human-review cost per task. - Model & tool comparisons: quantify ROI of changing model vendors or swapping MCP servers/skills. - CI/CD integration: gate deployments of new prompts/skills on benchmark regression thresholds. Revenue is team-based SaaS targeting AI infra teams, model providers, and consultancies that need to prove reliability to customers.