You can't design the agent platform first

Internal AI adoption often progresses from a single narrowly focused agent for a repetitive task, to several independently hand-built agents, and eventually to an operational challenge involving discovery, ownership, credentials, permissions, duplication, and accountability. Using Wallaby, a GTM research agent, and Atlas, a broader agent-building platform, the account argues that platforms should emerge from lessons learned while creating specific agents rather than being designed before real use cases exist, although shared infrastructure should begin developing by the second similar agent rather than after widespread sprawl. Atlas was built to address this later-stage complexity through shared connections, distinct agent identities, scoped permissions, isolated memory, server-side secret management, audit logs, reusable skills, and integrations. The central prediction is that internal AI work will increasingly focus less on improving agent capabilities and more on consolidating, governing, and assigning ownership to growing collections of agents.

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