Oqoqo is an evaluation platform for running repeatable agent experiments in realistic, isolated environments. Teams use it to build private benchmarks, compare agents and models, and investigate why agent workflows succeed or fail.
Oqoqo is a platform for designing, running, and analyzing evaluations of AI agents on real-world tasks. An experiment combines task definitions and rubrics with agents, models, effort settings, product treatments, and repeated trials, allowing teams to compare behavior under controlled conditions.
Each trial runs in a fresh sandbox containing the project files, credentials, dependencies, and tools required by the task. Oqoqo records the full trajectory and reports both outcome measures, such as pass rate and lift, and operational measures, including steps, tool calls, tokens, and duration.
The platform is intended for teams building private benchmarks, evaluating agent-facing products, or investigating workflow failures. Findings include evidence-backed frictions and can inform a fix-and-rerun cycle, while results remain limited to the tasks, agents, models, and environments selected for the experiment.
Define tasks, rubric requirements, agents, treatments, and repeated runs in one experiment. Treatments can represent packages of skills, MCP servers, CLIs, or SDKs, with one setup used as the baseline for comparison.
Run each trial in an isolated, non-reused environment with project files, credentials, dependencies, and installed tools. Separate setup, agent work, and teardown timings help distinguish infrastructure time from agent time.
Review the steps, tool calls, commands, outputs, and files changed during a run. The recorded trajectory shows where an agent stopped or took an unproductive path, rather than reducing the result to a single score.
Grade every rubric requirement with a pass or fail result, a reason, and a citation into the run. Recorded work can be re-evaluated after a rubric change without running the agent again.
Compare pass rates and lift for treatments and agents alongside steps, tokens, tool calls, and duration. Grouped friction findings describe the obstacle, severity, recovery, likely owner, and supporting trajectory evidence.
People can launch experiments from the web app, while agents can launch them through CLI or MCP access. Tasks and experiment configurations are versioned, and launching snapshots the treatments, skills, and environment used.
Turn real-world workflows into versioned task sets with explicit rubrics. Repeated trials provide a team-owned benchmark for the workflows represented by those tasks.
Run the same task, treatment, and trial grid across different agents, models, and effort settings. Use pass rates, lift, tokens, and duration to compare outcome and operating cost together.
Test whether agents can use a product setup that includes skills, MCP servers, CLIs, or SDKs. Compare a baseline treatment with an improved setup to measure the effect of the change.
Inspect the trajectory and requirement citations to locate the failure, then use friction evidence to distinguish product, documentation, environment, harness, agent, and task issues before rerunning.
Use the CLI or MCP access to launch experiments when a change may affect agent workflows. Versioned tasks and snapshotted treatments help keep the measured configuration identifiable.
A run is one agent attempt on one task under one treatment. Errored or cancelled runs do not count against the organization’s run pool. Model inference is separate from Oqoqo’s platform and infrastructure fees.
An evaluator reviews the recorded trajectory and changed files requirement by requirement. Each requirement receives a pass or fail result with a reason and a citation to the run; the task passes only when all requirements pass.
Yes. Recorded work remains fixed, so a revised rubric can be applied through a re-evaluation of the same run. If the task itself changes, Oqoqo’s methodology says to launch a new experiment.
Reports include lift against a baseline, steps, tool calls, tokens, and duration. Friction analysis identifies specific obstacles and records their family, severity, recovery, likely owner, and supporting trajectory evidence.
Results describe only the selected tasks and do not establish performance across every workflow. They also depend on the agents, models, and effort settings used, which can change over time. Evaluator judgments may be incorrect, so verdict reasons and citations should be checked.
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