Topic
Privacy & trust
Data boundaries, permissions, and security when agents can read, remember, and act across your work.
Why Privacy Is the Hardest Problem in AI Workspaces, and How Kylon Solves It
When AI agents operate with real agency inside a workspace, privacy becomes an architecture problem. This post walks through how Kylon keeps agents confined to authorized data, even under adversarial conditions.
A User Message Is More Than a User Message
When a user types a message in an AI-native workspace, the system sees far more than text. We unpack Kylon's activation architecture and show how a single message becomes a structured execution contract.
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All posts →AI workspace architecture
What makes an AI-native workspace different from a chat tool or a productivity app: activation, execution, and how work actually gets done.
Comparisons & alternatives
How Kylon differs from other AI tools and agents — architecture, not feature checklists.
Agent memory & context
How agents remember: context folding, continuity, and state that survives beyond a single conversation.
Integrations & connections
How Kylon works with the tools you already use — enrichment, scraping, voice, image, video — through real connections, not screenshots.