Agent Action Runtime
Reliable execution control for AI agent actions
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Agent Action Runtime - Reliable execution control for AI agent actions

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Your AI agent called the tool successfully. But did the action actually happen? Agent Action Runtime is an open-source execution layer for agents that take real-world actions. It adds policy, human approval, retries, post-action verification, recovery, and execution receipts between the agent and the side effect. Agent → Policy → Approval → Execute → Verify → Recover → Receipt Because API success ≠ action success.

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Hey Product Hunt 👋 I built Agent Action Runtime around a problem I think becomes increasingly important as AI agents move from answering questions to actually doing things. Consider a simple CRM agent. The agent calls update_crm(). The API reports success. But the CRM record never changes. From the agent’s perspective, the tool call succeeded. From the business’s perspective, the job failed. That distinction led to the core idea behind this project: A successful tool call is not necessarily a successful action. Agent Action Runtime creates an execution boundary between the agent’s decision and the side effect: Policy → Approval → Execute → Verify → Recover → Receipt v0.1.0 is intentionally small and local-first. I’m particularly interested in feedback from developers building agents that send emails, modify records, call external APIs, or trigger other side effects. The next question I’m exploring is replay safety: what happens when an action succeeds remotely, the response times out, and the agent retries? Would love to hear how others are handling these execution problems today.