# US 2026/0099518 A1 · Model Based API Mocking > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2026/0099518 A1 - Document ID: US20260099518A1 - Status: Published application - Kind: A1 - Application number: 18/908,285 - Filed: 2024-10-07 - Published: 2026-04-09 - Earliest claimed: 2024-10-07 - Assignees: Scaled Cognition, Inc. - Applicants: Scaled Cognition, Inc. - Inventors: Mathew Gardner; Adam Pauls; Jordan DeLoach - Web page: https://patentgrep.com/patent/US20260099518A1 ## CPC - G06F 16/33295: {in dialogue systems} - H04L 51/02: using automatic reactions or user delegation, e.g. automatic replies or chatbot-generated messages - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M) ## Abstract The present technology, roughly described, provides for mocking an application program interface (API) using a large language model (LLM). The present system generates a prompt with API signature information and API desired behavior information. The prompt can include instructions, library functions, examples, executed programs, and a current function invocation, as well as other content. The prompt can be generated, and submitted to an LLM to mock an API and generate a response. The response can be audited and the LLM can be fine tuned to provide improved performance in subsequent calls. ## Claims ### Claim 1 A method for mocking an application program interface, comprising: generating, by a first application on a first server, an application program interface (API) signature for the API to be mocked; generating, by the first application, a desired behavior for the API to be mocked; and generating a prompt that includes the API signature, the API desired behavior, and world state data, the desired behavior including instructions to a machine learning model, the world state data including zero or more values related to an operation that initiated creation of the prompt, the zero or more values maintained on a second server remote from the first server; submitting the prompt to a large language model LLM; and receiving a response from the LLM based on the prompt, the LLM response mocking a response that would be provided by the API based on a call to the API using data contained within the prompt. ### Claim 2 (depends on 1) The method of claim 1, wherein the API desired behavior includes examples of API inputs and outputs. ### Claim 3 (depends on 1) The method of claim 1, wherein the API desired behavior includes constraints on API output. ### Claim 4 (depends on 3) The method of claim 3, wherein the constraints include a selected format of the mocked API output. ### Claim 5 (depends on 3) The method of claim 3, wherein the constraints include a specified format for a structured mocked API output. ### Claim 6 (depends on 1) The method of claim 1, wherein the LLM is fine-tuned based on an audit of the LLM response. ### Claim 7 (depends on 1) The method of claim 1, wherein the mocked API call is related to an interaction between an automated agent and a simulated customer. ### Claim 8 A non-transitory computer readable storage medium having embodied thereon a program, the program being executable by a processor to mocking an application program interface, the method comprising: generating, by a first application on a first server, an application program interface (API) signature for the API to be mocked; generating, by the first application, a desired behavior for the API to be mocked; and generating a prompt that includes the API signature, the API desired behavior, and world state data, the desired behavior including instructions to a machine learning model, the world state data including zero or more values related to an operation that initiated creation of the prompt, the zero or more values maintained on a second server remote from the first server; submitting the prompt to a large language model LLM; and receiving a response from the LLM based on the prompt, the LLM response mocking a response that would be provided by the API based on a call to the API using data contained within the prompt. ### Claim 9 (depends on 8) The non-transitory computer readable storage medium of claim of claim 8, wherein the API desired behavior includes examples of API inputs and outputs. ### Claim 10 (depends on 8) The non-transitory computer readable storage medium of claim of claim 8, wherein the API desired behavior includes constraints on API output. ### Claim 11 (depends on 10) The non-transitory computer readable storage medium of claim of claim 10, wherein the constraints include a selected format of the mocked API output. ### Claim 12 (depends on 10) The non-transitory computer readable storage medium of claim of claim 10, wherein the constraints include a specified format for a structured mocked API output. ### Claim 13 (depends on 8) The non-transitory computer readable storage medium of claim of claim 8, wherein the LLM is fine-tuned based on an audit of the LLM response. ### Claim 14 (depends on 8) The non-transitory computer readable storage medium of claim of claim 8, wherein the mocked API call is related to an interaction between an automated agent and a simulated customer. ### Claim 15 A system for automatically rendering a prompt, comprising: one or more servers, wherein each server includes a memory and a processor; and one or more modules stored in the memory and executed by at least one of the one or more processors to generate, by a first application on a first server, an application program interface (API) signature for the API to be mocked, generate, by the first application, a desired behavior for the API to be mocked, generate a prompt that includes the API signature, the API desired behavior, and world state data, the desired behavior including instructions to a machine learning model, the world state data including one or more values related to an operation that initiated creation of the prompt, the one or more values maintained on a second server remote from the first server, submit the prompt to a large language model LLM, and receive a response from the LLM based on the prompt, the LLM response mocking a response that would be provided by the API based on a call to the API using data contained within the prompt. ### Claim 16 (depends on 15) The system of claim 15, wherein the API desired behavior includes examples of API inputs and outputs. ### Claim 17 (depends on 15) The system of claim 15, wherein the API desired behavior includes constraints on API output. ### Claim 18 (depends on 15) The system of claim 15, wherein the constraints include a selected format of the mocked API output. ### Claim 19 (depends on 15) The system of claim 15, wherein the constraints include a specified format for a structured mocked API output. ### Claim 20 (depends on 15) The system of claim 15, wherein the LLM is fine-tuned based on an audit of the LLM response.