# Test AI Assistants in Real-World Scenarios with Oqoqo

Create custom test environments to measure how AI agents interact with your product.

- **Source:** [Product Hunt — The best new products, every day](https://brifing.si/source/a459d489-2e9f-45f3-a76f-4f40ae5ec434)
- **Published:** 2026-07-17
- **Topic:** [Data & AI](https://brifing.si/c/data-ai)
- **Original article:** <https://www.producthunt.com/products/oqoqo>
- **On Brifing:** https://brifing.si/s/69862ebc-7172-4066-869a-bbf1cbe259db

## The Realistic Testing Gap

Current AI benchmarks fail to measure product performance under real-world conditions. Oqoqo enables developers to define custom task sets in fully isolated sandbox environments. The platform simplifies creating test scenarios based on actual user commands (e.g., 'integrate Supabase to my webapp for user signups'), providing authentic evaluation metrics.

## Comprehensive Performance Metrics

The system meticulously records every step AI assistants take, including tool calls, retries, and discovery loops. It automatically measures token consumption, cost analysis, and success rates against defined criteria. This allows objective comparison of different models like Codex, Claude Code, and OpenClaw performing identical tasks.

## Testing Product Surfaces

Oqoqo is optimized for testing AI-friendly interfaces like SDKs, CLIs, and APIs. The platform actively dogfoods itself to test its own MCP/CLI products. Developers can observe how different models interpret and use product documentation to make interface improvements.

## Statistical Significance

To account for AI agents' non-deterministic nature, Oqoqo offers multiple trial runs. Each test scenario automatically repeats to produce statistically significant results. This measures consistent performance rather than one-off successes.

## Negative Scenario Management

The platform measures not just successful scenarios but also error conditions like permission denials, invalid credentials, and partial side effects. Users can define negative rubric criteria to evaluate agents' cleanup and recovery behaviors post-failure, reducing product state risks.

## Model Comparison

Oqoqo enables comparison of how the same task set performs across different models, reasoning levels, and frameworks. Observations show Codex spends more time researching before implementing solutions, while Claude Code implements and iterates more frequently to reach answers.

## Custom Benchmarks

Users can create and share domain-specific benchmarks to understand which models perform better in particular fields. The platform automatically tests different combinations to help select the optimal model/harness for specific use cases.

## Product-Model Fit

Oqoqo is becoming a critical tool for teams building AI-powered products. By understanding how their products interact with different assistants, developers can design better AI-friendly interfaces, ultimately improving end-user efficiency.

## Sources covering this story

- https://www.producthunt.com/products/oqoqo — 

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_Summary written by Brifing from the original reporting. Copyright in the original article belongs to its publisher; read the full piece at the link above._