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The author reviews three months of experience using multi-agent collaboration, summarizing five main pain points (such as conflicts between agents, ignoring boundary conditions, self-censorship failure, difficulty in merging decisions, and exposing harder problems after compressed execution) and two insights (the high value of read-only review agents, and that agent conflicts expose ambiguous requirements), emphasizing the core decision-making role of humans in AI collaboration.
Based on conversations with over 20 teams, the author identifies three recurring pain points when using LLMs in production: enterprise-only basics, lack of agent observability, and slow support for new models.
A founder reflects on the importance of validating customer pain points before building AI solutions, questioning if many AI startups are solving problems that aren't painful enough.
The author criticizes existing AI memory platforms for lacking multi-agent memory, poor long-term recall after many interactions, and no forgetting mechanism, and is building a new solution; asks the community for additional pain points.