Andrej Karpathy emphasizes caution in the development of fully autonomous AI agents, advocating for systems that ensure reliability by constraining AI behavior through logical oversight and guardrails. The challenge lies not in making AI act but in ensuring it operates reliably, especially in complex enterprise settings where errors can compound through multi-step processes. To address this, a shift from monolithic language models to hybrid architectures combining neural reasoning with symbolic control is proposed. This approach involves neuro-symbolic systems that plan tasks, validate outputs continuously, and provide transparency and accountability. Such systems are designed to handle complex tasks like financial due diligence by breaking them into manageable steps, ensuring accuracy and fulfilling specific requirements with detailed audit trails. Implemented in platforms like AI21 Maestro, this model of constrained autonomy offers a reliable and transparent solution for business-critical AI applications, aligning with Karpathy's vision of AI that acts within defined bounds.