AI Agent

with engini’s ai agent, you can add powerful ai capabilities directly into your workflows generate text, analyze data, transform content, and automate decisions using natural language to get started with the ai agent, make sure you have an active account and connection to a large language model (llm) engini currently supports connections such as claude and openai which must be enabled in you account before using the agent getting started with ai agent prerequisites a connection to one of those llm anthropic claude https //app archbee com/public/preview ixqqblwfxopjg0nave78y/preview o5qm90hhwykusy9lvkxoa#j1o2y or openai https //app archbee com/public/preview ixqqblwfxopjg0nave78y/preview g z8jtgw1cz jkub mp3n#5a8zg or gemini https //app archbee com/docs/ixqqblwfxopjg0nave78y/rfq2jvwr qhsopichanna or azure openai https //app archbee com/docs/ixqqblwfxopjg0nave78y/kszmw3brk97z6mt fvwti ai agent activity to get started, the first step is to create a new workflow once your workflow is ready, follow the steps click on '+' sign choose "ai agents" once you’ve opened the ai agents menu, you’ll find a suite of powerful tools designed to integrate and specialized logic into your workflows anthropic https //shared archbee space/public/preview ixqqblwfxopjg0nave78y/preview o5qm90hhwykusy9lvkxoa#getting started with anthropic connect to claude models to handle sophisticated reasoning, creative writing, or complex coding tasks engini ai tools specialized internal tools optimized for data processing and platform specific automations gemini https //shared archbee space/public/preview ixqqblwfxopjg0nave78y/preview rfq2jvwr qhsopichanna#getting started with gemini leverage google’s multimodal models for high speed processing and integration with google ecosystem data open ai https //shared archbee space/public/preview ixqqblwfxopjg0nave78y/preview g z8jtgw1cz jkub mp3n#getting started with openai access gpt models for industry standard conversational ai, translation, and general purpose intelligence ai agent this activity allows you to deploy an intelligent ai agent into your workflow capable of executing complex reasoning, interacting with specialized tools, and processing data dynamically based on your instructions prompt (required) – enter the core instruction that defines how the ai agent should behave inside your workflow here you specify what the model should do with the data coming from your connected systems (such as crms, erps, or databases) the prompt can dynamically reference values from any previous workflow step using the tooltip note there is a character limit of 128 characters for this field add field choose the add field option to control your data max iterations this defines the maximum number of reasoning steps the ai agent is allowed to take when performing tool calls or multi step logic a higher number allows the agent to break the task into more steps, while a lower number limits how long the agent can run return intermediate steps when enabled, it displays more details system message here you define high level instructions such as tone, formatting rules, or domain focus (e g , “you are a crm assistant” or “always answer in json”) note there is a character limit of 128 characters for this field json structured output when json structured output is enabled, you can define the response format by clicking “load json sample to generate structure ” this action lets you create the expected json schema in two ways you can paste or enter a custom json example directly the system will generate the schema based on the sample you provided add ai model (required) this is where you choose the ai model that will power your agent select one of the llms you’ve already connected to engini (e g , claude, openai, gemini or azure openai) the model you choose is the one the agent will use every time this step runs add memory add storage click on the add storage option and choose the agent storage , for example engini storage then the engini storage activity will be displayed context window length defines how far back the agent can “remember” when retrieving data from engini storage a higher value keeps more history, while a lower value limits the agent to recent interactions session id keeps all messages and memory tied to the same conversation each session needs a unique identifier so the agent knows which stored context belongs together add tools this is where you choose the activities the ai agent is allowed to use from the connections in your account you can select multiple activities from different systems such as priority, engini tables, okta, and more the agent will decide when and how to use these tools based on your prompt and system message, and there is no predefined flow between the tools for example we choose the anthropic connector and the list files tool tool description this field acts as the "instruction label" that the ai agent reads to understand what the tool does the agent uses this description during its reasoning loop to decide whether it needs to run this specific tool to answer a prompt set automatically – the platform automatically applies a predefined, optimized description detailing the tool’s core functionality set manually – opens a text field allowing you to write your own custom rules, context, or constraints for when the agent should use this tool top n click this field to specify the maximum total number of file entries the tool should retrieve and return to the ai agent at one time if left blank, it defaults to returning all found entries let the ai decide the sparkle icons indicate fields where you can hand control over to the agent's logic note the agent may use some activities, all, or none of tools, depending on the instructions you give in the prompt and the decisions it makes during execution