Writing from the work itself.

AI agents need the same product discipline as data products when they become reusable, governed business capabilities.

Anthropic Dreaming points toward a durable data product portfolio that preserves organizational context independently of any AI model.

Maysano needs a data product graph because catalogs manage inventory while agents need operating context, relationships, review, and approved shared memory.

Maysano turns the data product portfolio into shared memory that helps AI agents follow governed, replayable runs.

Context, loop, and graph engineering show why data products need governed meaning, explicit relationships, and controlled operating workflows for AI agents.

Why data and AI product work needs a visible operating layer where humans and AI agents can share workflows, approvals, evidence, and governance.

Palantir Ontology and the Maysano Product Graph both help agents use enterprise context, but they structure that context at different layers.

Data product value depends on the workflow that keeps business context, decisions, delivery, and outcomes connected.

A Golden Data Product Portfolio helps leaders turn many separate data and AI ideas into one stronger implementation case.

ODPS, ODPC, ODPG, and ODPV are becoming a shared language for people, platforms, and AI agents that need data products to work in practice.

Why data product standards need to become agent-native so AI agents can read, validate, connect, and operate them.

How the Open Data Product Vocabulary was reshaped so people, tools, and AI agents can use the same product language.