AGENTS.md, Confluence, ADRs, READMEs, coding standards, and manual beliefs can all seed governed memory. Docs remain sources. Beliefs become the runtime state your AI can use.
Tenure does not replace AGENTS.md, ADRs, or Confluence. It turns stable parts of them into scoped beliefs with provenance.
Teams can add explicit policies, conventions, decisions, preferences, and constraints without waiting for the system to infer them.
Start in document-driven, observation, or curated modes so teams can review memory before models act on it.
Most teams already have useful AI context. The problem is that it lives in scattered files, docs, and decisions that models cannot use consistently across tools.
Use existing agent instructions as trusted source material, then convert stable guidance into scoped runtime beliefs.
Extract conventions, architecture rules, and team knowledge from documentation your organization already trusts.
Turn architectural decisions into model-facing state so future sessions inherit the why, not just the current code.
Seed project-specific facts, setup rules, service boundaries, and codebase conventions.
Make lint preferences, testing rules, review expectations, and generated-code conventions available per turn.
Create explicit beliefs for policies, preferences, decisions, and constraints that should not depend on inference.
Markdown files are great for human-readable instructions. Tenure gives those instructions governance, scope, provenance, review, and per-turn control.
Teams should not have to wait for repeated conversations to establish known rules. Manual belief creation lets admins, team leads, and developers seed memory intentionally.
The fastest path is not “let AI learn everything.” It is to seed from sources you trust, observe what Tenure extracts, then approve the beliefs that should become runtime memory.
Start with AGENTS.md, ADRs, coding standards, and Confluence pages that represent stable team knowledge.
Tenure proposes structured beliefs with type, scope, confidence, source, and why-it-matters fields.
Pin, edit, reject, or approve before those beliefs become model-facing memory.
Only relevant approved beliefs are injected into the model, with audit trails showing exactly what was used.
The practical adoption story: Keep the docs your team already trusts. Use Tenure to make the durable parts inspectable, scoped, and available across every AI client.
Tenure lets teams seed memory intentionally, preserve source provenance, and decide exactly what becomes active AI context.