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AI Agent OS

Multisig Approval Policies for AI Agents

Use multiple approvers for higher-value agent actions so no individual operator can silently expand an agent’s authority.

In this guide

Practical outcomes

  • Set signature thresholds
  • Separate operator roles
  • Audit approval history

How this works in practice

Use multiple approvers for higher-value agent actions so no individual operator can silently expand an agent’s authority.

A controlled agent separates the controller from the runtime. The controller owns the agent identity and defines policy, while a smart wallet, permission rules, quotas, relayer and scheduler constrain what the runtime can actually execute.

Implementation sequence

Turn the topic into a controlled implementation rather than a one-off transaction. Each step below should leave evidence a teammate, user or auditor can independently review.

  1. 01. Set signature thresholds. Define the expected result, capture the relevant onchain or operational evidence, and stop for review if the result differs from the plan.
  2. 02. Separate operator roles. Define the expected result, capture the relevant onchain or operational evidence, and stop for review if the result differs from the plan.
  3. 03. Audit approval history. Define the expected result, capture the relevant onchain or operational evidence, and stop for review if the result differs from the plan.

Evidence to retain

Record the .rama identity, controller, wallet, policy version, approval event, task identifier and resulting transaction hash. This makes a decision traceable without giving the agent unrestricted custody.

Control point

Start with small allocations, explicit recipient and contract allowlists, bounded session keys and a tested pause path. An automated workflow must never be the only place where authority or recovery knowledge exists.

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