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@danshipper laid out two architectures for agents in an org:
1/ everyone has their own agent that mirrors their taste and skills
2/ one super-agent at the center of the org that does everything for everyone
I think for non-native AI companies, the answer is mostly 1, with a sprinkle of specialized task agents layered on top.
here's why:
→ it adapts to tools and workflows that already exist. no forced migration to new AI-native workflows
→ gives every employee a super-powered intern instead of threatening to replace them
→ human taste stays the driver of output. the agent inherits the instincts of a specific person rather than generating the generic middle
→ decisions still have a clean human owner. "whose agent did/approved this" has an answer. this is especially important in risk-averse orgs
→ specialized task agents can plug in when a rigid process needs to be followed exactly
→ distributes the skill of working with agents across the whole team, not just one agent-whisperer at the center
the super-agent model assumes you can build something that's good for everyone. but that's extremely hard in siloed workspaces where context and workflows are scattered and fragmented.
yes, it's harder for every employee to "teach" their agent what to do, but I think the ideal scenario is that the agent learns through observing and not prompting.
specialized agents generate the generic middle. employee agents carry the taste and judgement of the people behind them.
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