Tenure
An AI proxy giving agents governed, structured memory plus runtime policy enforcement and audit trails
0About 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than a vector blob: conversations, code and docs are distilled into 'beliefs' that get reinforced, deduplicated or superseded over time, and contradictions are flagged for review instead of being silently overwritten.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
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
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