Building Custom AI Agents

Building Custom AI Agents Course

Artificial intelligence is no longer limited to simple chat responses. Today, professionals can build custom AI assistants that support real business tasks, reduce repetitive work, and improve daily operations. This course introduces participants to the design and use of custom AI agents without requiring coding experience.

Participants will learn how to create task-specific AI assistants using tools such as OpenAI GPTs, ChatGPT Custom Actions, Zapier, and Make. The course focuses on practical workplace applications, including content workflows, customer support, internal communication, reporting, scheduling, and knowledge management.

Through live instruction, guided practice, applied exercises, and a final project, participants will design an AI assistant that responds to a real organizational need. The course emphasizes not only automation, but also quality control, privacy, security, responsible use, and human oversight.

This is a structured, multi-week course designed for professionals who want to extend team capacity, improve productivity, and apply AI in a practical and responsible way.

Learning Outcomes

This program is structured around progressive development, integrating graphic design principles with AI-driven workflows. By the end of the program, students will be able to:

Describe the purpose, structure, and limitations of custom AI agents and no-code automation tools in professional workflows.
Explain how prompts, instructions, knowledge sources, actions, and automation triggers work together to guide an AI agent’s behavior.
Design a task-specific AI agent plan that defines the goal, audience, workflow, tools, inputs, outputs, and human review points.
Build a functional no-code AI assistant using tools such as OpenAI GPTs, ChatGPT Custom Actions, Zapier, and Make.
Apply security, privacy, and responsible AI practices when connecting AI agents to workplace platforms and organizational data.
Evaluate AI agent performance by testing outputs for accuracy, consistency, usefulness, reliability, and alignment with the intended workflow.
Create and present a final applied AI agent or digital assistant that addresses a real professional or organizational need, supported by clear documentation and reflection.
Identify workplace tasks and organizational needs that can be appropriately supported through custom AI assistants and automation.

Course Overview

This course introduces participants to the design and use of custom AI agents without coding. Participants learn how to build task-specific AI assistants using tools such as OpenAI GPTs, ChatGPT Custom Actions, Zapier, and Make. The course focuses on real workplace applications, including automation, communication, content workflows, customer support, reporting, and internal operations. Through guided practice and a final applied project, participants develop an AI assistant that supports a clear professional or organizational need while applying responsible AI, privacy, and human oversight practices.

Weekly Class Structure

Each week, students can access the content, including readings and assigned videos. Based on this material, students complete assignments such as discussion forum posts or project-based work to be submitted on the platform. On Saturdays, students meet with the instructor in a live online session to review concepts, ask questions, and engage in demonstrations and guided exercises.

Live sessions

Weekly live sessions are built into the course to provide feedback, critique, and discussion. They are a core part of the learning process, simulating real-world creative review settings where both design decisions and the use of AI are examined and refined. During these sessions, students present their work, explain their process, and receive input from both the instructor and peers. This structure supports iteration, critical thinking, and clearer communication of creative decisions.

Work Load

Students should expect approximately 8–12 hours per week, including the Saturday Live Session, readings, exercises, AI-based explorations, and project development.

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Building Custom AI Agents Course

Week 1: Understanding AI Agents and Use Cases

Instructions

During Week 1, participants will review the course introduction, expectations, and final project requirements. They will study assigned materials on AI agents, no-code automation, and responsible AI use, with attention to how these tools can support real workplace tasks. Participants will analyze examples of AI agents used in business, education, marketing, operations, and customer support, then identify one professional task or workflow that could be supported by a custom AI assistant. As they develop their initial idea, they should consider what the agent should do, who it would support, what information it would need, and where human review should happen.

Classwork

Participate in the live class discussion on AI agents and workplace automation.
Review sample AI agent use cases and identify the purpose, audience, inputs, outputs, and risks for each example.
Complete a guided use case worksheet for a possible AI agent idea.
Share an initial project idea and receive instructor or peer feedback.

Assignment

Submit an AI Agent Use Case Proposal. The proposal should describe a real professional or organizational need, explain why an AI assistant would be useful, identify the intended users, and outline the main tasks the agent should support.

Discussion

Discuss one workplace task or process that could be improved with a custom AI assistant. Explain the current challenge, how an AI agent could help, and what risks or limits would need to be considered.

Week 2: Designing the AI Agent Workflow

Instructions

During Week 2, participants will move from an initial idea to a structured AI agent design. They will review materials on workflow design, prompt instructions, knowledge sources, and automation planning. Participants will define the purpose and scope of their AI agent, identify the specific task sequence it will support, and map how the agent will receive input, process information, use tools, produce outputs, and include human review. They will also revise their project idea based on feedback from Week 1 and consider responsible design questions, including what the agent should not do, what information it should not access, and when a human should review or approve the output.

Classwork

Participate in a live workflow mapping activity.
Analyze examples of weak and effective AI agent instructions.
Draft the core instructions for your AI agent.
Create a basic workflow map showing how the agent will operate.
Identify possible risks related to privacy, accuracy, tone, reliability, or misuse.

Assignment

Submit an AI Agent Design Plan. The plan should include the agent’s purpose, target users, use case, required tools, core instructions, workflow steps, expected outputs, success criteria, and human oversight points.

Discussion

Share one design decision you made for your AI agent. Explain why that decision matters and how it helps the agent produce more useful, reliable, or responsible results.

Week 3: Building, Connecting, and Testing the AI Agent

Instructions

During Week 3, participants will begin building a working draft or prototype of their AI agent using no-code tools such as OpenAI GPTs, ChatGPT Custom Actions, Zapier, and Make. They will add clear instructions that define the agent’s role, task, tone, limits, and expected output format. Participants will also organize any knowledge sources, files, examples, or process information needed for the agent to function effectively. When appropriate, they will connect the agent to no-code automation tools and test it with realistic user scenarios. Throughout the process, participants will document what works, what does not work, and what needs to be improved before the final project.

Classwork

Participate in a live building session.
Test the agent with sample prompts or workflow scenarios.
Review peer or instructor feedback on the prototype.
Troubleshoot common issues such as vague instructions, poor output quality, missing context, inaccurate responses, or unsafe automation steps.
Revise the agent based on testing results.

Assignment

Submit an AI Agent Prototype Report. The report should include a description of the working prototype, sample test prompts or scenarios, sample outputs, problems found during testing, and planned revisions before the final submission.

Discussion

Describe one issue you discovered while testing your AI agent. Explain what caused the issue, how you plan to improve it, and what this taught you about building reliable AI-supported workflows.

Week 4: Final Project, Reflection, and Presentation

Instructions

During Week 4, participants will complete, test, and prepare to present their final AI agent or digital assistant project. They will conduct a final round of testing using realistic workplace scenarios and revise the agent’s instructions, workflow, outputs, or automation steps as needed. Participants will prepare a short presentation or demonstration that explains the problem or need, shows how the agent works, and identifies the workflow it supports. They will also write a reflection explaining their design process, testing results, limitations, responsible AI decisions, and possible future improvements.

Final Project

Submit a completed AI agent or digital assistant that addresses a real professional or organizational need. The final project should include the agent or prototype, a short explanation of the use case, the workflow it supports, the tools used, sample outputs, and documentation explaining how the agent should be used.

Final Reflection

Submit a short reflection that explains what you created, why you created it, what worked well, what challenges you encountered, how you addressed responsible AI concerns, and how the agent could be improved in the future.

Presentation

Present the final AI agent or digital assistant to the class. The presentation should explain the problem or need, demonstrate how the agent works, describe the workflow, identify key design decisions, and explain the agent’s limitations and human review points.