AI Agents in Action: Generative Intelligence for Everyday Business Use

Course Duration

2 Days

Audience

Employees of federal, state and local governments; and businesses working with the government.

Prerequisites

No prerequisites required.

Course Description

This two-day, hands-on course provides practical experience with cutting-edge AI systems to help professionals transform the way they work. This course focuses on AutoGPT and similar AI agent frameworks that build upon large language models such as GPT-4 and beyond, enabling autonomous task execution, workflow automation, and business process optimization. While ChatGPT focuses primarily on interactive dialogue, AutoGPT and related systems can perform multi-step operations, integrate with business tools, and make intelligent decisions with minimal human supervision. Participants will explore these technologies through guided examples, hands-on labs, and use cases covering text generation, data analysis, process automation, and decision support.

Learning Objectives

• Create high-quality text and content for emails, reports, marketing materials, and other business communications using AI agents, copilots, and automation frameworks such as AutoGPT.
• Analyze and visualize data using AI-powered tools to extract insights, identify trends, and support informed, data-driven decisions.
• Automate and streamline workflows by integrating AI systems with existing business tools and platforms to boost productivity and reduce repetitive tasks.
• Improve collaboration and communication by applying AI-assisted tools for document review, project management, and team knowledge sharing.
• Adapt and customize AI models or agents to meet specific organizational needs, ensuring responsible use and effective deployment across business functions.
• Apply best practices for ethical, secure, and optimized AI use, including troubleshooting, performance tuning, and maintaining high-quality outcomes in everyday workflows.

Course Outline

1.) Introduction to AI Agents and AutoGPT

• Overview of Artificial General Intelligence (AGI) and AI Agents such as AutoGPT
• Common business use cases for AI Agents and Copilots
• Architecture and core components of AI automation systems
• Data privacy and security considerations
• Lab: Exploring AI Agents

2.) AI Agents for Text Generation

• Generating text for emails, reports, and marketing materials
• Text summarization and information extraction
• Optimizing text generation quality and controlling output
• Ethics and responsible use of text generation
• Formulating prompts and instructions
• Configuring model parameters (temperature, max tokens, etc.)
• Handling long documents and generating coherent text
• Fine-tuning and customizing models for specific tasks
• Lab: Text Generation and Summarization

3.) AI Agents for Data Analysis

• Analyzing datasets and generating insights with AI automation tools
• Data visualization and chart creation
• Time series forecasting
• Integrating AI systems with Excel, Google Sheets, and other platforms
• Lab: Data Analysis and Visualization

4.) AI Agents for Automating Business Processes

• Streamlining repetitive tasks with AI automation tools
• Implementing basic chatbots and customer support systems
• Workflow automation and integration with third-party tools
• Monitoring and managing automation performance
• Lab: Process Automation, creating a workflow automation

5.) AI Agents for Decision Support

• Natural language processing for decision support
• Sentiment analysis and customer feedback
• Risk assessment and fraud detection
• Market research and trend analysis
• Lab: Use AI tools to analyze customer feedback and make data-driven decisions

6.) Customizing AI Agents

• Fine-tuning AI models for specific business needs
• Training new models on your data
• Evaluating and improving model performance
• Deploying custom AI agents within your organization
• Lab: Customizing and Training Models

7.) AI Agents for Collaboration and Teamwork

• Collaborative editing and document review with AI tools
• Enhancing communication and productivity in remote teams
• AI-assisted project management and planning
• Managing and organizing shared knowledge using AI systems
• Lab: Collaboration and Teamwork

8.) Best Practices and Troubleshooting

• Ensuring high-quality output and avoiding common pitfalls
• Monitoring usage and managing resources effectively
• Scaling AI automation systems for larger organizations
• Staying up to date with AI agent and AutoGPT community developments