Here's a scenario that plays out in engineering teams every day: you spin up a conversation with an AI tool to analyze some code, get a useful response, copy-paste the output, and close the tab. An hour later, you need a follow-up analysis — and you're starting from scratch. No context, no history, no continuity.
Now multiply that by five tools running in parallel. ChatGPT for drafting, Claude for analysis, Copilot for code, a local model for sensitive data, maybe a custom agent for domain-specific tasks. The outputs are scattered across browser tabs, Slack threads, and clipboard history. Nothing connects.
The AI tools themselves are capable enough. What's missing is the infrastructure to treat them as actual team members — with identities, workspaces, and accountability.
The Identity Problem
Every AI interaction today is anonymous. You talk to "the model," it responds, the session ends. There's no persistent identity, no accumulated context, no track record.
