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AI Agent OS

Onchain Scheduling for Durable Agent Tasks

Learn how scheduled tasks can be represented as durable onchain intent with explicit execution windows and controls.

In this guide

Practical outcomes

  • Define due tasks
  • Set execution windows
  • Handle missed execution

How this works in practice

Learn how scheduled tasks can be represented as durable onchain intent with explicit execution windows and controls.

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. Define due tasks. Define the expected result, capture the relevant onchain or operational evidence, and stop for review if the result differs from the plan.
  2. 02. Set execution windows. Define the expected result, capture the relevant onchain or operational evidence, and stop for review if the result differs from the plan.
  3. 03. Handle missed execution. 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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