Agent

Get started with an UPTIQ AI agent. Your own AI Assistant.

While LLMs are powerful, their capabilities are bound by their pre-existing knowledge. With UPTIQ AI Agent, you can go beyond these limitations by integrating customized, relevant workflows to supercharge your AI agent. Whether it’s leveraging prebuilt workflows or designing your own, the Workbench empowers you and your team to create AI solutions tailored to your needs. Your AI assistant is now smarter, faster, and more aligned with your goals.

What does it do?

The agent is the main point of contact for users, handling their questions and getting things done. They're the decision-maker, figuring out what needs to be done to meet user requests and the best way to do it. This key role makes the agent the center of user interaction and task completion, highlighting their importance in ensuring smooth user experiences and efficient results.

Illustration of the UPTIQ AI agent showcasing the flow from Sub-Agents to Intents and Workflows, powered by the Reasoning Engine and connected to Data for seamless AI-driven processes

How Does It Work?

This diagram illustrates the end-to-end flow of how the UPTIQ AI Agent processes user queries and executes workflows effectively. Let’s break it down into key steps:

1. Receiving the User Query The journey starts with a user submitting a request to the AI Agent. Example: A user might ask, "I’d like to upload a balance sheet of a business for analysis and to have it structured into my spreadsheet template"

The AI Agent takes this request and begins analyzing it to understand the user's needs.

1. Intent Classification The core of understanding lies in the Reasoning Engine, which powers the AI Agent.

   • In this case, the intent is identified as “Analyze Balance Sheet and Structure Data”.

   • The Reasoning Engine understands that this involves analyzing the uploaded document, extracting key data, and organizing it in a specific template.

2. Delegation to Sub-Agent Based on the identified intent, the AI Agent delegates the task to the appropriate Sub-Agent.

   • The relevant Sub-Agent here specializes in document analysis and data structuring tasks.

3. Executing the Workflow

   The Sub-Agent activates the predefined workflow associated with this task. The workflow involves several steps, such as:

   1. Document Upload and OCR:

      • The user uploads the balance sheet.

      • The Sub-Agent uses OCR (Optical Character Recognition) to extract structured data from the document.

   2. Data Analysis:

      • The extracted data is analyzed for key metrics like assets, liabilities, and equity.

   3. Template Structuring:

      • The analyzed data is formatted into the user’s specified spreadsheet template.

      • Any required calculations (e.g., net income or ratios) are applied based on the template’s logic.

4. Final Decision & Response

   Once the workflow is completed, the AI Agent evaluates the result and determines the next steps:

   • If additional information is needed (e.g., missing data from the balance sheet), the Reasoning Engine proactively asks the user for clarification or additional documents.

   • If the task is completed, the AI Agent delivers the structured spreadsheet back to the user.

   • Example Output: A polished spreadsheet populated with the analyzed balance sheet data, fully formatted according to the user’s template.

How to create an Agent ?