Agentes Zai: Automatizando Tarefas no Zeev

Introduction to the Webinar

Welcome and Overview

• Davi introduces himself as the presenter for the webinar focused on intelligent automation processes to enhance operational efficiency.

• He expresses excitement about the event and encourages audience interaction through comments on YouTube.

• Attendees are reminded to log in to YouTube for commenting and asking questions, which will be addressed towards the end of the session.

Structure of the Webinar

• The webinar is planned for one hour, with a commitment to finish on time at 3 PM. The presentation will be divided into two main parts: a presentation followed by a Q&A session.

• Davi shares his background as co-founder of Z by Stock, emphasizing his role in understanding client challenges and proposing suitable solutions.

The Journey of ZAI

Introduction to ZAI

• Davi discusses ZAI (Z's Artificial Intelligence), highlighting its development journey that began last year with features aimed at answering user queries about their platform.

Key Features of ZAI

• Query Response: Users can ask ZAI questions regarding platform functionalities, saving time compared to traditional manual searches.

• Process Analytics: ZAI provides analytics capabilities, allowing users to analyze automated processes and obtain performance insights through reports or direct inquiries.

Advanced Functionalities

• Process Modeling: Users can model processes using AI by either constructing flow diagrams or describing them in natural language; this democratizes process automation without needing extensive expertise.

• Form Creation: Users can request form creation from ZAI by specifying required fields, streamlining data collection within automated processes.

Future Developments

• Davi mentions ongoing beta features that allow users to initiate processes using natural language commands, enhancing usability further for non-experts in automation tools.

Introduction to ZAI and AI Agents

Overview of ZAI's Functionality

• Users can access the shopping application through ZIV, initiating a process by providing necessary information or engaging in a conversation with the AI, Zai.

• The AI prompts users for specific details about their purchase requests, including item quantity and desired delivery dates.

• If an invalid date is entered, Zai identifies the error and suggests corrections, showcasing its natural language processing capabilities.

Significance of AI Agents

• The primary focus is on how AI agents automate tasks, enhancing operational efficiency within companies.

• These agents are software systems that utilize artificial intelligence to achieve objectives and perform tasks on behalf of users.

• By delegating work to AI agents, organizations can significantly increase process efficiency and reduce reliance on human intervention.

Understanding Conversational vs. Process Agents

Differences Between Agent Types

• Conversational agents like ChatGPT interact directly with users through chat interfaces, responding to inputs in real-time.

• These agents focus on customer service and personal productivity by facilitating user inquiries based on manual input.

Role of Process Agents

• In contrast, process agents operate behind the scenes; they are triggered by events (e.g., API calls), executing pre-defined tasks without direct user interaction.

• They serve as intelligent automation components essential for orchestrating automated processes within BPMS platforms.

Automation Processes with BPMS

Introduction to BPMS Platforms

• BPMS (Business Process Management Systems) allow users to define process steps and assign responsibilities for each stage.

• Users can monitor progress transparently, identifying bottlenecks or delays in execution.

Traditional vs. Automated Processes

• Historically, processes required significant human involvement at every step; automation aims to streamline these workflows.

• For example, a purchase request involves multiple approvals and actions from various stakeholders before completion.

Understanding the Role of AI Agents in Business Processes

Integration of AI in Company Systems

• The value paid for equipment, such as a notebook, must be recorded in the company's RP system. This integration is managed by Ziv, who orchestrates tasks performed by humans and systems.

Transformative Impact of AI on Workflows

• AI can either enhance human work or replace it entirely, leading to faster, cheaper, and higher-quality outcomes for businesses.

• Utilizing AI 24/7 allows companies to scale processes effectively without needing additional personnel during peak times.

Dynamics of Using an AI Agent

• An AI agent can manage tasks traditionally handled by humans, allowing for greater efficiency and better results within existing workforce capacities.

• Information about requests (e.g., purchase orders) can be fed into the AI agent to streamline processing.

Data Handling Capabilities of AI Agents

• The agent can process various data types including personal information from requesters and details about items being purchased.

• It can also handle attachments like quotes from suppliers through Optical Character Recognition (OCR), extracting relevant information automatically.

Decision-Making Support Through Analysis

• After extracting data from documents like supplier quotes, the AI analyzes which option offers the best terms based on predefined criteria.

• The outcome includes recommendations for decision-making regarding supplier selection based on analysis conducted by the AI.

Practical Applications Across Different Domains

• Beyond procurement, an AI agent could assist legal departments by analyzing contracts and filling out necessary forms with extracted data.

• In HR contexts, an AI could filter resumes against job requirements efficiently, presenting only qualified candidates to recruiters.

This structured overview captures key insights from the transcript while providing timestamps for easy reference.

Automated Claims Processing and Credit Analysis

Introduction to Automated Processes

• The speaker discusses the capability of automated systems to process notes, extract information, and apply rules in contexts like vehicle insurance claims.

• An example is provided where an agent analyzes credit requests by evaluating documents submitted by applicants, assessing risk for loan approvals.

Live Demonstration of Credit Analysis

• A live demonstration is promised to showcase how a credit analysis agent operates within the system.

• The speaker contrasts using ZI agents with building custom solutions, highlighting differences in complexity and ease of use.

Challenges of Building Custom Solutions

• Building a custom solution requires multiple accounts and integrations with various services (e.g., OpenAI for generative AI).

• The speaker emphasizes that while anyone can build such systems, it involves significant time investment and overcoming numerous challenges.

Benefits of Using ZI Agents

• With ZI agents, users do not need to manage maintenance or costs associated with running AI processes; the system handles these aspects automatically.

• Users can submit requests without worrying about waiting times for responses from AI systems, as the process runs asynchronously.

Implementing Tasks in ZIV

• In ZIV's modeling phase, users can easily configure tasks by dragging elements into their workflow and setting up simple prompts.

• The simplicity of this setup allows non-experts to utilize AI effectively without needing extensive technical knowledge.

Example Scenario: Credit Application Process

• A hypothetical scenario illustrates the credit application process where applicants must provide personal identification and income verification documents.

• The speaker describes how traditional methods require manual document checks against submitted information during credit evaluations.

Process Automation with AI Agents

Introduction to AI Agents in Process Modeling

• The speaker introduces the concept of using AI agents for more advanced and simplified process modeling, demonstrating this live.

• An example is provided where an AI agent analyzes personal documents, aiming to automate data entry by extracting information directly from uploaded files.

Configuring the Personal Document Analysis Agent

• The speaker creates a new agent named "webinar specialist in personal documents," specifying its role as an expert in analyzing Brazilian personal documents.

• ZIV automatically generates a suitable prompt for the agent without requiring prior knowledge of prompt engineering, streamlining the setup process.

Setting Up Input and Output Parameters

• The agent's instructions include processing files and returning structured information while ensuring that missing data is represented as empty values.

• The speaker customizes input types to include various personal documents (e.g., RG, CPF, CNH), emphasizing flexibility in document handling.

Defining Desired Outputs

• The agent suggests 18 potential output fields based on typical Brazilian personal documents; however, the speaker narrows it down to essential details like name and birth date.

• After configuring the outputs, the agent is ready for testing with actual document uploads to verify its functionality.

Testing and Integration of Agents

• A successful test demonstrates that the agent can accurately extract information from a high-quality RG document.

• The importance of linking tasks within processes is highlighted; existing agents can be reused efficiently across different tasks by integrating them into workflows.

Additional Agent Creation for Income Verification

• A second agent designed to analyze income verification documents (like pay stubs or tax returns) is mentioned. This agent will determine both document type and net income.

• A third agent will be created to evaluate credit requests against extracted income data, showcasing a comprehensive approach to automating financial assessments.

Credit Analysis Process Using AI

Introduction of New Agents

• The speaker introduces a new stage in the credit analysis process, referring to it as an "analyst" agent that will utilize AI for credit evaluation.

• A new agent called "webinar specialist" is created, specifically designed for analyzing personal credit requests.

Configuration and Customization

• The speaker discusses configuring the agent with Zai, aiming to customize prompts to gather more information relevant to credit analysis.

• Key inputs for the agent include monthly income from income proof, loan amount requested, and calculated installment amounts based on loan terms.

Credit Granting Rules

• The criteria for granting credit are established: the installment must not exceed 30% of the applicant's monthly income.

• The agent is instructed to provide a final report summarizing the financial situation and recommending a percentage (0-100%) for credit approval along with a conclusive statement.

Output Specifications

• Three outputs are defined for the agent:

• Final report summarizing financial status.

• Percentage recommendation for credit approval.

• Conclusion regarding whether to recommend further analysis or deny approval.

Automation and Integration

• The integration with Zai automates data entry by linking form fields directly with input requirements identified by the AI.

• The system identifies specific fields in forms such as income and loan amount, ensuring accurate data mapping between inputs and outputs.

Execution of Credit Request Process

• After setting up three agents for processing credit requests, the speaker prepares to execute a live simulation of submitting a credit request.

• During this execution phase, any inconsistencies will be addressed in real-time as they arise during testing.

Document Submission and Task Management

• The speaker submits identification documents along with proof of income while noting that manual entry of personal details is no longer necessary due to automation.

• A hypothetical loan request is made for R$50,000 over 60 months; upon submission, tasks are generated within the system reflecting ongoing processes related to this request.

Task Monitoring Post Submission

• As tasks are processed by AI agents—recognizing documents and performing analyses—the results will return into a task queue once completed.

Understanding AI-Driven Credit Analysis

Overview of AI Integration in Document Processing

• The speaker contrasts two document processing methods: one without AI and one with AI, highlighting the efficiency gained by using AI to analyze data and assist in decision-making.

Data Recognition and Formatting

• The system recognizes various personal information from documents, such as RG and CPF numbers, ensuring that dates are formatted consistently as instructed (day/month/year).

Income Analysis from Documents

• A detailed income analysis is performed on a payslip showing a net income of R$ 2,852.50 after deductions for NSS and income tax.

Compliance with Credit Policies

• The analysis checks compliance with credit policies, confirming that the proposed monthly payment of R$ 833 is within the acceptable limit of 30% of the applicant's monthly income.

Recommendation Process

• An 85% recommendation for credit approval is given due to proximity to risk limits; this allows an analyst to review or approve based on their discretion.

Adjusting Credit Requests

Configuring Approval Processes

• If desired, configurations can allow automatic approvals for requests above a certain recommendation threshold (e.g., over 90%).

Revision Requests and Adjustments

• Users can request revisions on credit amounts or terms; adjustments can be made directly during live demonstrations.

Real-Time Testing and Feedback

• The speaker emphasizes the importance of testing processes live to demonstrate ease of making adjustments within the system.

Finalizing Credit Applications

Re-evaluation After Adjustments

• After submitting revised figures (R$ 40,000 over 120 months), the system re-evaluates the application leading to an updated recommendation percentage.

Simplifying Complex Processes

• The demonstration illustrates how quickly agents can be configured for tasks like document recognition or automated analyses across various applications beyond just credit assessments.

Q&A Session Insights

Accessibility of AI Agents

• Questions arise regarding technical requirements for implementing AI agents; it’s noted that while some knowledge is necessary, configuration remains accessible even for business areas without advanced technical skills.

How to Effectively Use AI Agents in Business Processes

Training and Implementation of AI Agents

• The training provided includes creating an agent and giving it specific instructions on tasks, which can be tested before publication.

• No advanced technical knowledge is required; the process is designed to be accessible for business areas.

Security, Privacy, and Compliance Concerns

• Companies are increasingly focused on information security, especially with the introduction of AI technologies.

• Data shared with AI will not be stored or used for training purposes, ensuring privacy and compliance.

• Users should be cautious when using free services like ChatGPT, as they may inadvertently share data that could be used for training models.

• The company has implemented strict security controls and policies regarding information handling in AI applications.

Handling Unforeseen Scenarios with AI Agents

• When configuring an agent, it's crucial to anticipate exceptions or unforeseen scenarios within processes.

• For example, if a document's quality is below a set threshold (e.g., 80%), the agent can abort the task and return it for manual input.

• This proactive approach ensures that users are prompted to provide better-quality documents or enter information manually when necessary.

Final Remarks and Opportunities

• The session concludes with an invitation for further questions from participants.

• A QR code is presented for attendees to request a personalized demonstration of the platform's capabilities beyond just agents.

• Participants who engage through this channel may receive their first month free when signing up for services related to automation processes.

Final Remarks and Contact Information Closing Thoughts

Conclusion of the Session

• The speaker expresses gratitude for the opportunity to engage with the audience, indicating a positive experience during the session.

• An invitation is extended for attendees to reach out directly if they wish to connect further, emphasizing openness and accessibility.

• The speaker encourages participants to take advantage of an ongoing campaign, hinting at valuable offerings that may enhance their experience.

• A mention of a complete demonstration suggests that there are additional resources available for those interested in learning more about the subject matter discussed.

• The session concludes with well wishes from the speaker, reinforcing a friendly and supportive atmosphere.