Find where your AI agent fails before users do.

I evaluate one AI agent or workflow against your goals, expected behavior, and real-world edge cases. This sprint is designed for tool-use agents, coding assistants, voice agents, browser workflows, and LLM-powered automations.

You receive:

a scoped test plan and evaluation rubric

up to 25 functional, adversarial, and edge-case checks

a failure taxonomy with reproducible examples

a scorecard and prioritized recommendations

one review round and a clear handoff

Best for teams that need an independent QA pass, regression baseline, prompt-injection review, or evidence before a launch or iteration.

To start, share the test environment or exported traces, intended user journey, success criteria, and known problem cases. No confidential data is required; sanitized samples are welcome.