What is shared context in enterprise AI?
Shared context is the company information, rules, decision history, and measured outcomes available to an AI team within its permissions. It connects relevant evidence across systems so a team can understand the work without requiring the user to repeat the whole business background.
Shared does not mean unrestricted
An AI team uses only the records relevant to its job that it is permitted to access. A finance workflow and a talent workflow may draw on the same company foundation without exposing every record to both teams.
In Nodes, the context graph preserves relationships between source evidence, decisions, human input, approved actions, and later outcomes. Decision Traces make that history inspectable.
Why it matters for the next workflow
A new AI team can draw on relevant knowledge already in place. The company does not lose its accumulated record when a job ends or a model changes.
Retaining information does not make every generated output an accepted fact. Evidence quality and permissions still govern its use. A validated result in one decision program does not establish that a different program will work.