Insights from ProductNow

Perspectives on context engineering, AI agents, and how companies build a governed source of truth people and agents can act on.

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The Context Engine

Every company now has thousands of agents trying to get work done, and almost none of them know what the organization actually knows. Organizational context is a big data problem with its own 4Vs. What a context engine is, the five attributes that define one, and why ProductNow built it as a plain document editor.

AI-native data integration without an ontology

Every data integration effort historically required an ontology — slow to build, locking clients out of their own system, never truly self-service. AI changes that equation: continuous ingestion into living documents, agent-managed pruning, and a deep graph — integrations in 24 hours, not months.

Storing prototypes from external tools

An explanation of how we store external prototypes in the warehouse and what it took to get there.

CRDTs are not enough when your co-worker is an AI agent

CRDTs converge; they do not coordinate. That distinction becomes critical the moment an AI agent joins the editor. Why multiplayer collaboration needs a coordination layer above merge, and the section-level lease protocol we built to keep agents well-mannered.

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