Building AI agents is easy; trusting them isn’t. How do you verify outputs?
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We ship an agent that turns one product photo into a full listing on marketplace. The hardest part wasn’t generation(and it was surprice for us) it was verification: “Is this correct, compliant, consistent?”
What’s your minimal verification loop for agents?
A) structured outputs (JSON schema / Pydantic)
B) retrieval + fact checks / tool calls
C) LLM-as-judge with rubrics
D) human-in-the-loop gates
E) regression tests on golden sets
Happy to grant an extra 100 credits (that’s the monthly quota on the free plan) so you can see the agent in action, and tell us if it works for you 😅. mujoai.com
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Mujo AI
Since nobody jumped in, here’s the minimal verification loop we ended up using for Mujo AI.
We don’t use HITL or regression (yet). Our minimal loop is:
A) Hard-typed JSON schema for all outputs (fail on missing/wrong types).
B) Deterministic checks via retrieval & tools: marketplace rules, image/file validators, banned claims/words.
C) LLM-as-judge with a small rubric (clarity, policy compliance, factuality from product data). If score < threshold, we auto-reject and regenerate.