Partner management runtime enforcement

1 . A computer-implemented method for dynamically generating a set of application programming interfaces (APIs) usable to implement a set of conditions applicable to an exchange to be performed among a plurality of entities, the computer-implemented method comprising:

receiving an indication to generate the set of APIs;

receiving, in response to the indication, metadata corresponding to the set of conditions;

generating the set of APIs based, at least in part, on the metadata;

analyzing executable application code to determine one or more insertion points for the set of APIs to be called, the executable application code to be executed during performance of the exchange; and

inserting the set of APIs into the executable application code using the one or more insertion points to cause the set of conditions to be implemented in real time as a result of calling the set of APIs.

2 . The computer-implemented method of claim 1 , further comprising:

obtaining natural language text modified by at least using one or more trained artificial intelligence models; and

identifying portions of natural language text that expresses the set of conditions.

3 . The computer-implemented method of claim 1 , further comprising:

generating a machine-readable data structure that corresponds to the set of conditions, wherein the machine-readable data structure is usable to generate the set of APIs.

4 . The computer-implemented method of claim 1 , wherein at least one of the executable application code is generated using one or more trained artificial intelligence models based, at least in part, on one or more matrices that correspond to the set of conditions.

5 . A system, comprising:

one or more processors; and

one or more non-transitory, computer-readable media comprising executable instructions recorded thereon that, as a result of execution by the one or more processors, cause the system to at least:

obtain metadata corresponding to a set of conditions applicable to an exchange to be performed among a plurality of entities, the set of conditions determined using one or more trained artificial intelligence models;

generate a set of application programming interfaces (APIs) based, at least in part, on the set of conditions;

determine one or more points to insert the set of APIs to an executable application code to be executed for the exchange; and

cause the set of APIs to be called during runtime of the executable application code to implement the set of conditions.

6 . The system of claim 5 , wherein the executable instructions further include instructions that further cause the system to:

generate, using one or more additional trained artificial intelligence models, a portion of the executable application code based, at least in part, on the set of conditions.

7 . The system of claim 5 , wherein the executable instructions further include instructions that further cause the system to:

update an API specification corresponding to the set of APIs as a result of obtaining an indication that the set of conditions has been changed.

8 . The system of claim 5 , wherein the executable instructions further include instructions that further cause the system to:

obtain context information corresponding to at least one of the plurality of entities; and

generate machine-readable data structure based, at least in part, on the set of conditions and the context information, wherein the machine-readable data structure is usable to generate the set of APIs.

9 . The system of claim 8 , wherein the machine-readable data structure is generated by at least converting the set of conditions and the context information into one or more matrices.

10 . The system of claim 8 , wherein the one or more points to insert the set of APIs and one or more arguments for the set of APIs are determined based, at least in part, on the machine-readable data structure.

11 . The system of claim 5 , wherein the executable instructions further include instructions that further cause the system to:

update the one or more trained artificial intelligence models based, at least in part, on natural language text.

12 . The system of claim 5 , wherein the executable instructions further include instructions that further cause the system to:

determine a format for the set of APIs based, at least in part, on a tech stack that corresponds to the executable application code.

13 . The computer-implemented method of claim 1 , wherein the one or more insertion points is determined based, at least in part, on the metadata.

14 . One or more non-transitory computer-readable storage media having stored thereon computer-executable instructions that, as a result of being executed by one or more processors of a computer system, cause the computer system to at least:

obtain metadata corresponding to a set of conditions applicable to an exchange to be performed among a plurality of entities, the set of conditions determined using one or more trained artificial intelligence models;

generate a set of application programming interfaces (APIs) based, at least in part, on the set of conditions;

analyze executable application code to be executed during performance of the exchange to determine one or more locations for the set of APIs to be embedded into the executable application code; and

cause the set of APIs to implement the set of conditions for the exchange during execution of the executable application code.

15 . The one or more non-transitory computer-readable storage media of claim 14 , wherein calling the set of APIs to implement the set of conditions causes an additional set of conditions to control the exchange, the additional set of conditions corresponding to an additional set of APIs.

16 . The one or more non-transitory computer-readable storage media of claim 14 , wherein the computer-executable instructions further include executable instructions that further cause the computer system to:

generate, using one or more additional trained artificial intelligence models, a portion of the executable application code based, at least in part, on the set of conditions.

17 . The one or more non-transitory computer-readable storage media of claim 14 , wherein the computer-executable instructions further include executable instructions that further cause the computer system to:

generate machine-readable data structure based, at least in part, on the set of conditions and context information that corresponds to at least one of the plurality of entities.

18 . The one or more non-transitory computer-readable storage media of claim 17 , wherein the computer-executable instructions further include executable instructions that further cause the computer system to:

determine a set of functions to be performed as a result of calling the set of APIs based, at least in part, on the machine-readable data structure.

19 . The one or more non-transitory computer-readable storage media of claim 17 , wherein the one or more locations for the set of APIs to be embedded is determined based, at least in part, on the machine-readable data structure.

20 . The one or more non-transitory computer-readable storage media of claim 14 , wherein the computer-executable instructions further include executable instructions that further cause the computer system to:

determine dependencies between a first API and a second API of the set of APIs to cause a second API to be called as a result of calling the first API.