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Tenure

An AI proxy giving agents governed, structured memory plus runtime policy enforcement and audit trails

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freemiumFour tiers, with the paid ones currently in private beta. Community is free and self-run: project scopes, local tokens, structured beliefs and the core context engine, with unlimited client and agent tokens and no account required. Pro adds agent observability, automatic policy drafts from observed behaviour, policy enforcement, full audit history and exports, and backup/restore tooling, with community support. Team is $25 per user per month with a $99/month minimum that includes 4 users, then $25/user/month beyond that — it adds Team Mode, OAuth/OIDC, per-user identity, project and team scopes, shared team context, centralised audit history and ticket support. Enterprise is custom on an annual contract with SAML SSO, SCIM provisioning and deprovisioning, group-to-scope mapping, org-wide policy controls, retention and export controls, and faster support.View full pricing →

Visit Tenure

https://tenureai.dev

About Tenure

Tenure is an AI proxy that sits between your chat clients, IDEs and agents and the model, doing two jobs at once: supplying institutional memory and enforcing policy on what the agent is allowed to do. Its framing is that asking a model to obey a fragile system prompt is a losing battle, so the enforcement should live outside the model — the model does the thinking, Tenure does the policing. Memory is structured rather than a vector blob: conversations, code and docs are distilled into 'beliefs' that get reinforced, deduplicated or superseded over time, and contradictions are flagged for review instead of being silently overwritten. Knowledge sources include Confluence, AGENTS.md files, architecture decision records, ordinary docs, and manually authored beliefs. Memory modes let a team pick its trust level — observe-only, curated, adaptive or document-driven — and scoped tokens limit what each individual client or agent may read, write, extract and inject. Project Resume lets you pick up where you left off in any repository. GroundEval turns observed agent behaviour into reviewable policy, so what an agent actually did becomes what it is permitted to do. It runs in VS Code and VSCodium natively and works with mobile clients, Open WebUI and self-hosted agent gateways. The company markets it for AI governance and EU AI Act traceability, with audit trails and SCIM deprovisioning, and publishes a benchmark paper.

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Key Features

Belief extraction, merging and explicit contradiction handling
Scoped tokens limiting read, write, extract and inject per client
Four memory modes from observe-only to document-driven
GroundEval turns observed agent behaviour into reviewable policy
Project Resume across any repository
SCIM deprovisioning and audit exports for EU AI Act traceability

Tenure Pros & Cons

Pros

  • +Out-of-model enforcement is more durable than prompt-based guardrails
  • +Contradiction flagging beats silent overwrite for team knowledge
  • +Community tier runs entirely locally with no account

⚠️ Cons

  • Pro, Team and Enterprise are all still private beta
  • The $99/month Team minimum is steep for a three-person team
  • Belief-based memory is a heavier concept to adopt than a plain vector store

Tags

memorygovernancepolicyauditvscodecompliance
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