We just opened the waitlist for Something, and the part that surprised me most while building it wasn't the multi-agent orchestration — it was how hard it is to make an AI actually disagree.
Every model we tested defaults to being helpful, which in practice means agreeable. Even when explicitly prompted to "find flaws," the outputs would soften into "here are some considerations" instead of a real critique. We had to engineer around this specifically:
Separate system prompts with opposing reward framing — one agent optimizes for identifying growth potential, the other is explicitly told its only success metric is surfacing a disqualifying flaw
Structured output forcing a verdict, not a summary — the skeptic agent (Nothing) has to commit to a specific weakness category (unit economics, timing, technical feasibility) rather than hedging across all of them
A reconciliation step where both outputs get merged into one conviction score, so the founder isn't just reading two contradictory paragraphs
If anyone's built adversarial agent setups and hit the same "it just wants to agree with me" problem, curious how you solved it. [Everyone who has a brain is a founder here]