Oqoqo

Oqoqo is an evaluation platform that lets you run AI agents against real products and measure how well they perform on actual tasks. Instead of relying on synthetic benchmarks that may not reflect real world usage patterns, you can point any agent at your own CLI tool, MCP server, SDK, or API and see exactly where it succeeds, fails, or gets stuck. The output is a complete trajectory showing every tool call, command, retry, reasoning step, and discovery loop the agent went through before either hitting its result or giving up. If you're building tools that AI agents will use, this gives you empirical evidence of what works and what doesn't rather than guessing based on capability scores from academic benchmarks.

The problem it solves is straightforward. Benchmarks like MMLU or HumanEval tell you how a model scores on standardized tests, but they don't tell you whether that model can actually navigate your product, find the right endpoint, parse your error messages, and complete a task successfully. If you're building MCP servers, developer tools, APIs, or any interface that AI agents might interact with, you need to know whether Codex, Claude Code, Cursor, GitHub Copilot, or any other harness can use what you've built. Oqoqo gives you that answer by running the task in an isolated sandbox, capturing everything the agent does along the way, and evaluating the final result against your own success criteria.

The infrastructure spins up isolated sandboxes for each run so one experiment doesn't contaminate another. It executes the tasks you've defined, provides the agent with the project state and tools it needs, and catalogs every single step from start to finish. When the run completes, you see token consumption, cost breakdown, pass or fail verdicts, and the written reasoning behind each judgment call. Because each run includes consistent project state, context injection, and tool availability, comparisons across models and agent harnesses stay fair. You can run the same task against Claude Code and then against Cursor, and see which one passed, which one burned more tokens, and at what point each one got stuck or gave up.

The primary audience is teams building agent facing surfaces. If you're shipping an MCP server, a CLI with natural language capabilities, an SDK that agents will call, or an API that needs to be discoverable without human guidance, Oqoqo lets you test whether AI agents can actually use it before your users try and fail. It also fits teams running regression tests on agent interfaces after shipping changes. Instead of waiting for bug reports from frustrated users who tried to automate something and couldn't figure out why the agent kept calling the wrong function, you catch those friction points before release. The feedback loop is faster than manual QA and more realistic than unit tests that don't model actual agent behavior.

What sets Oqoqo apart from other eval frameworks is the emphasis on real products over toy benchmarks and puzzle solving tasks. You're not measuring whether a model can pass a coding challenge in a vacuum or answer trivia correctly. You're measuring whether it can find the right endpoint in your documentation, call the right function with the right parameters, handle edge cases gracefully, and actually complete a workflow that matters to your users. The rubrics you write in plain language stay versioned across experiments, so runs from last month remain comparable to runs today even as your product evolves. That versioning also makes it possible to track regressions over time when you update your tooling or switch to a different agent provider.

Custom rubrics are written in plain language rather than code, which lowers the bar for defining what success looks like. You describe the expected outcome, the platform runs the agent, and the evaluation system determines whether the result matches what you asked for. If the agent took a weird path but still got the right answer, that shows up in the trajectory. If it got stuck in a loop retrying the same failed call, that shows up too. The level of detail is granular enough that you can debug agent behavior without having to reproduce it manually.

Access is free to try. You can run experiments on the platform and start seeing results without payment upfront. Pricing for heavier usage, team collaboration features, and enterprise support isn't published directly on the site, so you'd need to reach out if you plan to scale beyond the free tier or need SLA backed availability. For solo developers and small teams testing a handful of agent integrations, the free tier is likely enough to validate whether your tooling works as expected.