AGENTS.md, Confluence, ADRs, READMEs, coding standards, and manual beliefs can all feed into memory. The docs stay where they are. The beliefs are what your AI uses.
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 what a model would need to know. It's just scattered across files, docs, and old decisions that no tool can pull from consistently.
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 instructions a person can read. Tenure adds the scope, source tracking, review step, and per-turn control that markdown alone can't give you.
If a rule is already known, your team shouldn't need repeated conversations to teach it to the model. Admins, team leads, and developers can just add it directly.
Trying to let the AI learn everything at once isn't the fastest path. Seeding from sources you already trust, watching what Tenure pulls out, then approving what's worth keeping gets you there faster.
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 what was used.
In practice, this means keeping the docs your team already trusts and using Tenure to make the durable parts of them inspectable, scoped, and available across every AI client.
Tenure lets your team decide what feeds into memory, keep track of where it came from, and control what actually reaches the model.