Ai quorum for resource provisioning code generation

An apparatus for generation of cloud substrate provisioning code, comprising:

• one or more memories storing processor-executable code;
• and
• one or more processors coupled with the one or more memories and individually or collectively operable to execute the code to cause the apparatus to:
• receive user input indicating a plurality of provisioning parameters for provisioning resources on a cloud substrate;
• transmit, to a first artificial intelligence (AI) model, the plurality of provisioning parameters and a request to generate, based at least in part on the plurality of provisioning parameters, provisioning code associated with the cloud substrate;
• transmit, to one or more second AI models, the provisioning code generated by the first AI model, the plurality of provisioning parameters, and a request to analyze the provisioning code based at least in part on the plurality of provisioning parameters;
• and
• update respective reputation values associated with the first AI model and the one or more second AI models based at least in part on one or more analysis results associated with output of the one or more second AI models.

A non-transitory computer-readable medium storing code for generation of cloud substrate provisioning code, the code comprising instructions executable by one or more processors to:

• receive user input indicating a plurality of provisioning parameters for provisioning resources on a cloud substrate;
• transmit, to a first artificial intelligence (AI) model, the plurality of provisioning parameters and a request to generate, based at least in part on the plurality of provisioning parameters, provisioning code associated with the cloud substrate;
• transmit, to one or more second AI models, the provisioning code generated by the first AI model, the plurality of provisioning parameters, and a request to analyze the provisioning code based at least in part on the plurality of provisioning parameters;
• and
• update respective reputation values associated with the first AI model and the one or more second AI models based at least in part on one or more analysis results associated with output of the one or more second AI models.