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AI Agent OS

Retry Strategy for Onchain Agent Tasks

Handle RPC errors, replacement transactions and temporary execution failures without accidentally duplicating value-moving actions.

In this guide

Practical outcomes

  • Use idempotent task IDs
  • Set retry limits
  • Escalate uncertain outcomes

How this works in practice

Handle RPC errors, replacement transactions and temporary execution failures without accidentally duplicating value-moving actions.

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. Use idempotent task IDs. 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 retry limits. Define the expected result, capture the relevant onchain or operational evidence, and stop for review if the result differs from the plan.
  3. 03. Escalate uncertain outcomes. 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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