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, Engine builds a company-owned context graph, a knowledge graph that connects source evidence, historical decisions, human judgment, actions and later outcomes, alongside relevant external developments. Decision Traces preserve the inspectable business record. The company agrees source coverage, checking frequency and access for each person and assigned agent. Ownership and continued use follow the agreement; Nodes Engine supplies the context, intelligence and action layers, with Connector for integrations and AI FDE for configuration and testing.
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. Preserve the source, when it was true, when it became available, and where a lesson applies. Human approval is a judgment; later results may support, contradict, or leave it unresolved. A proposed lesson needs evaluation before reuse. A result in one decision program does not validate another.