MOTA
AI agents that follow your rules, not their imagination.
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MOTA - AI agents that follow your rules, not their imagination.

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MOTA is a governed execution runtime for high-consequence workflows. Instead of letting LLMs improvise, MOTA separates AI planning from deterministic execution. It translates user intent into a strict execution graph to enforce business policies, retrieve live data, and route tasks conditionally. With human-in-the-loop approval gates, durable execution states, and transparent audit records, MOTA guarantees that sensitive customer support actions are safe, predictable, and fully verifiable.

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Hi Product Hunt! We built MOTA after running into a practical problem with AI agents: they’re impressive at understanding requests, but much harder to trust with sensitive business actions. A support agent can draft a response. But should it be allowed to issue a $7,200 refund, modify an account, or apply an SLA exception based only on an LLM’s judgment? We didn’t think so. MOTA separates AI planning from governed execution. It turns a request into an explicit workflow graph, retrieves live system data, evaluates business rules, follows conditional paths, and pauses for human approval when policy requires it. For example: when a refund exceeds $5,000, MOTA does not rely on the model to “remember” the threshold. The runtime evaluates policy directly and stops at an approval gate before the refund action can proceed. In the demo, try: - A refund below the approval threshold - A refund above $5,000 - A request outside the 90-day refund window - Approving vs rejecting an escalated request We’d especially value feedback on: - What sensitive workflows have you avoided automating because AI execution felt too unpredictable? - How are you handling approvals and policy enforcement around AI-driven actions today? Thanks for checking out MOTA.