The easiest way to build evals and custom benchmarks for real-world agentic tasks

2 min read Original article ↗

Run eval experiments at scale in realistic environments on fully managed cloud infrastructureDefine custom task sets to build your private benchmarks, measure how well agents can use any product, and find the best agent-model combinations for your use cases. Generate insights on the fly, such as product interface friction, token inefficiencies, performance differences, and so much more.

A video-style demonstration of building and launching an agent experiment in Oqoqo: describing a task, selecting tasks, configuring agents, treatments, runs and evals, then launching runs on sandboxed machines and reviewing pass and fail results with full step-by-step trajectories.

Configure an experiment: tasks, agents, treatments, rubrics, and headless interfaces.

Configure an experiment: tasks, agents, treatments, rubrics, and headless interfaces.

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This is a simplified, representative recreation. The full Oqoqo product is richer and more nuanced. Explore Oqoqo.

Evaluate any agent, any model, all at once

  • Claude Code
  • Codex
  • Cursor
  • GitHub Copilot
  • OpenCode
  • Grok Build
  • OpenClaw
  • Pi
  • Hermes
  • Managed infrastructure

    Large experiments run quickly and reliably in the cloud on durable workflows and orchestration we manage.

  • Bring your own models

    Connect the model providers and subscriptions you already use, then choose the model for each agent.

  • Compare treatments

    A treatment is the package of tools associated with a product, such as skills, MCP servers, CLIs, and SDKs, so you can compare one package against another.

  • Rich customizable environments

    Each task runs on its own sandboxed machine. Load optional repos and files, and customize the dependencies and tooling as needed.

What you can do with Oqoqo

Agents are becoming software's primary user.Build agent-first products any agent can use.

Point any agent at any product on any real-world agentic task. See every step it took, where it got stuck, and what fixing that is worth.

Build evals once and keep iterating to improve agents and agent-facing products

Frequently asked questions

Run your first experiment