Dynamic execution of artificial intelligence agents through device management
Systems and methods are described for dynamic execution of artificial intelligence (“AI”) agents. A server can receive, from a client device, an input associated with an AI agent. Based on a manifest file or user profile, the server can identify a management policy that applies to the AI agent. The server then dynamically configures access to the agent objects based on applying the management policy. The management policy is applied to a device status of the client device, a user profile of a user of the client device, and/or a network configuration of the client device. The server then executes a modified workflow based on the dynamically configured access, wherein the modified workflow bypasses or changes operation of at least one of the agent objects. Based on the modified workflow, the server transmits an output to the client device.
1 . A method for dynamically managing execution of an artificial intelligence (“AI”) agent, comprising:
receiving, at a client device, a request to execute a first AI agent, wherein the first AI comprises a client application, wherein a manifest file specifies relationships between agent objects of the first AI agent;
identifying, by a second AI agent, a management profile that applies to the first AI agent, the second AI agent being configured to evaluate compliance of the management profile based on at least one of:
a device state of the client device; and
a user profile of a user of the client device;
dynamically configuring execution of the first AI agent based on the compliance evaluation of the second AI agent;
executing a workflow for the first AI agent based on the dynamic configuration, wherein the workflow changes operation of at least one of the agent objects; and
logging the execution of the first AI agent, wherein the logging includes the compliance evaluation and the executed workflow.
2 . The method of claim 1 , wherein the management profile includes an agent object policy that is identified based on an identifier in the manifest file, and operation of a first agent object is based on the device state or user profile.
3 . The method of claim 1 , wherein the management profile includes a user management policy that is compared against a tenant and group identified in the user profile.
4 . The method of claim 1 , wherein the device state is noncompliant with respect to at least one of device security, operating system, software, and performance.
5 . The method of claim 4 , wherein the workflow of the first AI agent bypasses sending an input to an AI model.
6 . The method of claim 4 , wherein the workflow of the first AI agent includes local execution of a first AI model at the client device instead of accessing a second AI model over a network.
7 . The method of claim 1 , wherein the management profile specifies a security requirement, and wherein the security requirement is compared against a network configuration, wherein the network configuration is part of the device state.
8 . The method of claim 1 , wherein the management profile includes a list of allowed or disallowed applications, and wherein the list is compared against applications installed or executing on the client device.
9 . The method of claim 1 , wherein the management profile specifies use of different AI models for different groups, and wherein the dynamic execution configuration is based on a group identified in the user profile.
10 . The method of claim 1 , wherein the device state includes a location of the client device, and wherein compliance is determined based on comparing the location to a geofence.
11 . The method of claim 1 , wherein the workflow includes executing a conditional code block that selects between alternative AI models based on application of the management profile.
12 . The method of claim 1 , further comprising assigning tools to an agent object based on the evaluated compliance.
13 . The method of claim 1 , wherein an input is redacted or filtered as part of dynamically configuring the execution.
14 . The method of claim 1 , wherein access to a dataset is bypassed based on the evaluated compliance.
15 . The method of claim 1 , further comprising identifying an agent object policy that applies to a first agent object, wherein the agent object policy specifies build parameters for the first agent object.
16 . The method of claim 1 , wherein the management profile includes an AI model policy, a dataset policy, a tools policy, and a prompts policy, all of which are applied in configuring inputs to an AI model that is part of the workflow of the first AI agent.
17 . The method of claim 1 , wherein the management profile is applied based on a tenant and a group identified in the user profile.
18 . The method of claim 1 , wherein the management profile is applied in real-time to the agent objects of the workflow.
19 . A non-transitory, computer-readable medium including instructions are executed by a processor and cause the processor to perform stages for dynamically managing execution of an artificial intelligence (“AI”) agent, the stages comprising:
receiving, at a client device, a request to execute a first AI agent, wherein the first AI comprises a client application, wherein a manifest file specifies relationships between agent objects of the first AI agent;
identifying, by a second AI agent, a management profile that applies to the first AI agent, the second AI agent being configured to evaluate compliance of the management profile based on at least one of:
a device state of the client device; and
a user profile of a user of the client device;
dynamically configuring execution of the first AI agent based on the compliance evaluation of the second AI agent;
executing a workflow for the first AI agent based on the dynamic configuration, wherein the workflow changes operation of at least one of the agent objects; and
logging the execution of the first AI agent, wherein the logging includes the compliance evaluation and the executed workflow.
20 . A system for dynamically managing execution of an artificial intelligence (“AI”) agent, comprising:
a memory storage including a non-transitory, computer-readable medium comprising instructions; and
at least one hardware-based processor that executes the instructions to carry out stages comprising:
receiving, at a client device, a request to execute a first AI agent, wherein the first AI comprises a client application, wherein a manifest file specifies relationships between agent objects of the first AI agent;
identifying, by a second AI agent, a management profile that applies to the first AI agent, the second AI agent being configured to evaluate compliance of the management profile based on at least one of:
a device state of the client device; and
a user profile of a user of the client device;
dynamically configuring execution of the first AI agent based on the compliance evaluation of the second AI agent;
executing a workflow for the first AI agent based on the dynamic configuration, wherein the workflow changes operation of at least one of the agent objects; and
logging the execution of the first AI agent, wherein the logging includes the compliance evaluation and the executed workflow.