# US 12,111,859 B2 · Enterprise generative artificial intelligence architecture > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,111,859 B2 - Document ID: US12111859B2 - Status: Granted - Kind: B2 - Application number: 18/542,536 - Filed: 2023-12-15 - Published: 2024-06-20 - Granted: 2024-10-08 - Earliest claimed: 2022-12-16 - Assignees: C3.ai, Inc. - Applicants: C3.ai, Inc. - Inventors: Thomas M. Siebel; Nikhil Krishnan; Louis Poirier; Romain Juban; Michael Haines; Yushi Homma; Riyad Muradov - Web page: https://patentgrep.com/patent/US12111859B2 ## CPC - G06F 16/345: {Summarisation for human users} - G06F 16/3326: {using relevance feedback from the user, e.g. relevance feedback on documents, documents sets, document terms or passages} - G06F 16/334: Query execution (filtering based on additional data G06F16/335) - G06F 16/3347: {using vector based model} - G06F 16/335: Filtering based on additional data, e.g. user or group profiles (filtering in web context G06F16/9535, G06F16/9536) - G06F 16/338: Presentation of query results - G06F 40/20: Natural language analysis (semantic analysis of natural language G06F40/30) - G06F 40/40: Processing or translation of natural language (natural language analysis G06F40/20; semantic analysis G06F40/30) - G06N 3/045: Combinations of networks - G06N 3/0475: Generative networks - G06N 3/092: Reinforcement learning - G06N 5/04: Inference or reasoning models - G06N 20/00: Machine learning - G06N 3/063: using electronic means - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## Abstract Systems and methods managing, by an orchestrator, a plurality of agents to generate a response to an input. The orchestrator employs one or more multimodal models such as a large language models to process or deconstruct the prompt into a series of instructions for different agents. Each agent employs one or more machine-learning models to process disparate inputs or different portions of an input associated with the prompt. The system generates, by the orchestrator, a natural language summary of the structured and unstructured data records. The system formulates output and transmits the natural language summary of the data records. ## Claims ### Claim 1 A method comprising: managing, by an orchestrator, a plurality of agents to generate a response to an input, wherein the orchestrator employs one or more multimodal models to process or deconstruct a prompt into a series of instructions for different agents, wherein each agent employs one or more machine-learning models to process disparate inputs or different portions of an input associated with the prompt; instructing, by the orchestrator, retrieval requests related to the input to the one or more agents of the plurality of agents; receiving, from the one or more agents of the plurality of agents, data from multiple data domains based on instructions from the orchestrator; analyzing, by the orchestrator, the received data to formulate one or more responses to the prompt, wherein the orchestrator provides additional retrieval requests to the one or more agents to retrieve additional data to satisfy a context validation criteria associated with the input; wherein the orchestrator generates intermediate instructions associated with the additional retrieval requests to the plurality of agents, wherein the intermediate instructions comprise portions of the input, questions about the input generated by the one or more multimodal models, and follow-up questions about answers generated by the one or more multimodal models; and wherein at least one agent of the one or more agents instantiates a tool to perform one or more operations on the instruction, the retrieved data, and the intermediate instructions; and outputting, by the orchestrator based on the intermediate instructions and the one or more operations performed by the tool, a validated response of the one or more responses to the input that satisfies context validation criteria and a portion of data retrieved by the one or more agents related to the input. ### Claim 2 (depends on 1) The method of claim 1, wherein outputting the portion of data retrieved by the one or more agents related to the input includes a source citation for the at least a portion of the validated response. ### Claim 3 (depends on 1) The method of claim 1, wherein the context validation criteria includes a threshold for identifying source material from an enterprise data system that corroborate the response. ### Claim 4 (depends on 1) The method of claim 1, wherein managing the plurality of agents comprises iterative processing or multiple instructions from the orchestrator. ### Claim 5 (depends on 1) The method of claim 1, wherein the retrieving the data from multiple data domains includes time series data, structured data, and unstructured data. ### Claim 6 (depends on 1) The method of claim 1, wherein the operation includes at least one of calculation, translation, formatting, visualization. ### Claim 7 (depends on 1) The method of claim 1, wherein the one or more agents are trained on different domain specific machine-learning models. ### Claim 8 (depends on 1) The method of claim 1, wherein at least one agent employs a type system to unify incompatible data from disparate data sources. ### Claim 9 A system comprising: one or more processors; and memory storing instructions that, when executed by the one or more processors, cause the system to perform: managing, by an orchestrator, a plurality of agents to generate a response to an input, wherein the orchestrator employs one or more multimodal models to process or deconstruct a prompt into a series of instructions for different agents, wherein each agent employs one or more machine-learning models to process disparate inputs or different portions of an input associated with the prompt; instructing, by the orchestrator, retrieval requests related to the input to the one or more agents of the plurality of agents; receiving, from the one or more agents of the plurality of agents, data from multiple data domains based on instructions from the orchestrator; analyzing, by the orchestrator, the received data to formulate one or more responses to the prompt, wherein the orchestrator provides additional retrieval requests to the one or more agents to retrieve additional data to satisfy a context validation criteria associated with the input; wherein the orchestrator generates intermediate instructions associated with the additional retrieval requests to the plurality of agents, wherein the intermediate instructions comprise portions of the input, questions about the input generated by the one or more multimodal models, and follow-up questions about answers generated by the one or more multimodal models; and wherein at least one agent of the one or more agents instantiates a tool to perform one or more operations on the instruction, the retrieved data, and the intermediate instructions; and outputting, by the orchestrator based on the intermediate instructions and the one or more operations performed by the tool, a validated response of the one or more responses to the input that satisfies context validation criteria and a portion of data retrieved by the one or more agents related to the input. ### Claim 10 (depends on 9) The system of claim 9, wherein outputting the portion of data retrieved by the one or more agents related to the input includes a source citation for the at least a portion of the validated response. ### Claim 11 (depends on 9) The system of claim 9, wherein the context validation criteria includes a threshold for identifying source material from an enterprise data system that corroborate the response. ### Claim 12 (depends on 9) The system of claim 9, wherein managing the plurality of agents comprises iterative processing or multiple instructions from the orchestrator. ### Claim 13 (depends on 9) The system of claim 9, wherein the retrieving the data from multiple data domains includes time series data, structured data, and unstructured data. ### Claim 14 (depends on 9) The system of claim 9, wherein the operation includes at least one of calculation, translation, formatting, visualization. ### Claim 15 (depends on 9) The system of claim 9, wherein the one or more agents are trained on different domain specific machine-learning models. ### Claim 16 (depends on 9) The system of claim 9, wherein at least one agent employs a type system to unify incompatible data from disparate data sources. ### Claim 17 A non-transitory computer readable medium comprising instructions that, when executed, cause one or more processors to perform: managing, by an orchestrator, a plurality of agents to generate a response to an input, wherein the orchestrator employs one or more multimodal models to process or deconstruct a prompt into a series of instructions for different agents, wherein each agent employs one or more machine-learning models to process disparate inputs or different portions of an input associated with the prompt; instructing, by the orchestrator, retrieval requests related to the input to the one or more agents of the plurality of agents; receiving, from the one or more agents of the plurality of agents, data from multiple data domains based on instructions from the orchestrator; analyzing, by the orchestrator, the received data to formulate one or more responses to the prompt, wherein the orchestrator provides additional retrieval requests to the one or more agents to retrieve additional data to satisfy a context validation criteria associated with the input; wherein the orchestrator generates intermediate instructions associated with the additional retrieval requests to the plurality of agents, wherein the intermediate instructions comprise portions of the input, questions about the input generated by the one or more multimodal models, and follow-up questions about answers generated by the one or more multimodal models; and wherein at least one agent of the one or more agents instantiates a tool to perform one or more operations on the instruction, the retrieved data, and the intermediate instructions; and outputting, by the orchestrator based on the intermediate instructions and the one or more operations performed by the tool, a validated response of the one or more responses to the input that satisfies context validation criteria and a portion of data retrieved by the one or more agents related to the input. ### Claim 18 (depends on 17) The non-transitory computer readable medium of claim 17, wherein outputting the portion of data retrieved by the one or more agents related to the input includes a source citation for the at least a portion of the validated response. ### Claim 19 (depends on 17) The non-transitory computer readable medium of claim 17, wherein the context validation criteria includes a threshold for identifying source material from an enterprise data system that corroborate the response. ### Claim 20 (depends on 17) The non-transitory computer readable medium of claim 17, wherein managing the plurality of agents comprises iterative processing or multiple instructions from the orchestrator.