Agent.ai

agent ai discover how to connect engini with agent ai and use its activities to search, inspect, and run ai agents directly from engini workflows agent ai is a platform for discovering, building, and running ai agents with the agent ai connector in engini, you can search for available agents, retrieve agent metadata, run selected agents with dynamic input properties, and send custom authorized api requests when needed getting started with agent ai prerequisites before connecting agent ai to engini, make sure you have an agent ai account an agent ai api key access to your engini account retrieve your agent ai api key log in https //login agent ai/u/login/identifier?state=hkfo2sboehfqtjuwcvblm2dqavzvwhm4sfrzm3lcm0lfanh5wafur3vuaxzlcnnhbc1sb2dpbqn0awtziexfalzxbtd4nwttuhcytvlcedj1zmpsbektvjb1dxq0o2npznkgmuxdz2pmbgnjvzbhc1nxeeewelprd2hurzfbetvwrjc to your agent ai account open the connections https //agent ai/user/connections in your agent ai account inside connections navigate to api https //agent ai/user/connections#api page copy your api key keep this key secure it provides access to your agent ai account and usage, so it should be treated like a password connecting engini to agent ai enter your engini account at https //app engini io https //app engini io navigate to connections page by clicking on the connections on the left sidebar or by clicking here https //app engini io/connections click on the "new integration" option located at the topbar choose agent ai option from the available applications enter the following details in the new integration form and press save connection name enter a meaningful name that will help you identify the connection later api key paste your agent ai api key this key is used to authenticate requests made through the agent ai connector save click the save button to save the connection actions get agents searches for agent ai agents according to the selected search criteria status – select the agent status you want to search by for example, public, private, team, or all top n – you can set the maximum number of agents engini will return add filter click add filter to define conditions that narrow down the data list and return only the relevant items to learn more about using filters, click here https //help engini ai/workflow editor#add filters get agent retrieves detailed information about a specific agent ai agent status – select the agent status you want to filter by to find the agent id agent id – select or enter the id of the agent you want to retrieve this field becomes available after selecting a status once a status is selected, engini retrieves and displays the relevant list of agent ai agent ids based on that status you can then click the field to search for and select an agent run agent runs a selected agent ai agent from inside an engini workflow status – select the agent status you want to filter by agent name – select the agent name you want to run this field becomes available only after selecting a status once a status is selected, engini retrieves and displays the relevant list of agent ai agents based on that status click the field to search for and select an agent note you must select an agent name before entering the properties field properties – enter the full json object required by the selected agent this field should be filled only after selecting an agent name the required json structure depends on the selected agent ai agent each agent may require different fields according to its own input schema save to file save text content as a file in the selected format specify the file type, provide the content you want to save, and enter a file name the action generates the file, which can then be used in later workflow steps, such as sending it by email, uploading it to cloud storage, or sharing it with other applications file type – select or enter the file format you want to create for example pdf, docx, html, md, odt, tex, azw3, epub, or png body – enter the content that will be saved inside the file this can be plain text, markdown, html, or any supported content structure according to the selected file type file name – enter the output variable name that will store the generated file url for example saved file note after the action runs, agent ai returns the generated file url under this name send api request sends a custom api request to agent ai by specifying the endpoint, http method, headers, and query parameters base url the main endpoint of the api you want to call (e g , the server or service url) relative url the specific path or resource you want to access, appended to the base url method defines the http method used for the request (e g , get, post, put, delete) body type specifies the format of the request body (e g , json, form data) body the payload sent with the request, usually required for methods like post or put add headers allows you to include additional headers (e g , authorization, content type) required by the api add queries allows you to add query parameters to the request url (e g , filters, pagination)