Think Like an Agent (TLA30)
A hands-on class teaching business professionals to write effective AI prompts and design accountable AI agents for issue-management review work.
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- Private class for your team
- Live expert instructor
- Online or on‑location
- Customizable agenda
- Proposal responses same day as request
Course Overview
Move beyond AI curiosity and into practical, responsible AI use. In this hands-on class, participants learn how to use generative AI and agents to strengthen issue-management work while keeping human judgment, review, approval, validation, and accountability at the center. The course builds confidence with effective prompting techniques, including how to define clear objectives, provide useful context, set smart constraints, choose the right output format, and refine results. Participants will explore how AI can help uncover documentation gaps, improve clarity, organize complex information, suggest stronger revisions, and prepare thoughtful questions for human review.
The class also introduces a practical framework for designing issue-management agents, including how to define the business problem, identify trusted inputs and sources, shape useful outputs, build guardrails, test workflows, measure success, and maintain appropriate oversight. Through realistic examples and guided exercises, participants apply these skills to issue quality review, root cause and action plan review, and action plan closure review, leaving with practical strategies they can use right away to improve consistency, traceability, governance, and review quality.
Course Benefits
- Apply a five-part prompting framework (objective, context, constraints, format, iteration) to real issue-management scenarios
- Use AI to support issue quality review, root cause/action plan review, and closure evidence review while keeping human judgment central
- Design a narrow-scope AI agent by defining its purpose, users, inputs, trusted sources, outputs, guardrails, and success criteria
- Test an agent against standard, exception, missing-input, high-risk, and adversarial scenarios
- Apply a responsible-use checklist to validate AI outputs before acting on them
Delivery Methods
Delivered for your team at your site or online.
Course Outline
- Course Purpose
- Introduces practical use of generative AI and agents to support issue-management work
- Emphasizes that AI supports consistency, organization, review, and drafting but does not replace human judgment, validation, approval, or accountability
- Prompting Skills
- Explains how to write effective prompts using a clear objective, context, constraints, format, and iteration
- Provides prompt structures and examples for issue quality review, root cause/action plan review, and closure evidence review
- Reinforces the need to review AI-generated results for accuracy, source alignment, assumptions, and human decision points
- Prompting Use Cases
- Issue Quality Review: Identify missing elements, vague language, unsupported conclusions, and rating concerns
- Root Cause and Action Plan Review: Evaluate taxonomy alignment, root cause quality, and action plans using the STAR principle
- Action Plan Closure Review: Compare approved commitments to submitted evidence and identify gaps or validation needs
- Agent-Building Skills
- Defines what an AI agent is and explains how to design one around a narrow business problem
- Covers purpose, users, inputs, trusted sources, outputs, guardrails, workflow, testing, success criteria, and governance
- Practical Agent Examples
- Presents three issue-management agents: Issue Quality Reviewer, Root Cause Classification and Action Plan Quality Reviewer, and Action Plan Closure Reviewer
- Describes each agent's purpose, inputs, outputs, human oversight, value, and workflow
- Testing and Governance
- Explains how to test agents using standard, exception, missing-input, high-risk, and adversarial scenarios
- Highlights common mistakes such as overly broad scope, outdated sources, weak human oversight, and allowing AI to invent missing facts
- Responsible Use and Final Activities
- Provides a responsible-use checklist for validating AI outputs before use
- Includes a final activity where participants design an issue-management agent
- Ends with key takeaways and an action plan for applying prompting and agent-building skills responsibly
Class Materials
Each student receives a comprehensive set of materials, including course notes and all class examples.
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