Memory that goes into the task,
and comes back for review.
Decision Memory is the approved decision layer for human–AI teams. It preserves the decisions, rules, skills, and authorizations that define how your organization works — and keeps a human in the loop for everything that becomes trusted.
Not notes. Structured memory.
What was chosen and why: rationale, evidence, rejected alternatives, scope, status, lineage.
What must / must not / requires approval. Versioned, linked to the decision behind it.
How a class of work should be done: patterns, playbooks, dependencies, write-back contracts.
Which actor may do what, on what, under which conditions. Runtime enforcement stays separate.
Decision → use JSON for machine artifacts
Rule → never store them in Excel
Skill → define schema · validate · store example
Auth → agent may write repo;
external upload needs approval
AI proposes. Humans approve.
Candidate
Agents retrieve permitted memory and submit proposals — never approving organizational memory.
Review
A person reviews each candidate with its evidence and reasoning in context.
Approve · edit · reject
Imported or generated rules stay candidates until reviewed.
Decision Memory is a memory & governance product. Your runtime still enforces actions.
Four tools your agents call.
Search approved decisions.
Retrieve permitted decision context for a task.
Check possible conflicts across objects.
Submit a decision to human review.