AI Agents Explainer
This use case is a polished, instructional explainer designed to help MBA and MIS students understand how the term “AI agent” is used differently across technical, business, and platform-specific contexts. The goal of this work is to bridge misunderstandings, clarify terminology, and prepare leaders to evaluate AI tools in real organizations.
This piece was developed using AI prompting, iterative refinement, and structured feedback — exactly the type of professional communication CIDM 6096 emphasizes. It reflects how generative AI can assist in creating educational materials, training documentation, and organizational onboarding resources.
Purpose of the Explainer
The explainer was created to:
Translate complex AI terminology into business-friendly language
Compare how different companies and ecosystems define “agents”
Reduce confusion for students and professionals entering AI-driven environments
Demonstrate clear, structured educational writing made with the support of generative AI
Create a reusable asset that other students, colleagues, or leaders can benefit from
This aligns directly with course objective #5: “Create a library of resources to lead others to adopt generative AI in an organization.”
The Workflow
Research: Prompted ChatGPT to "Compare OpenAI AgentKit vs. LangChain for a non-technical audience."
Structuring: Used AI to generate the outline for the 1-page explainer.
Refinement: Verified the definitions of "autonomous vs. semi-autonomous."
Evidence of Work:
Key Concepts Covered
1. What an AI Agent Actually Is
A core definition based on shared meaning across:
academic research
software engineering
commercial AI platforms
workflow automation tools
The explainer establishes that, at the core, an agent is:
“An autonomous or semi-autonomous system that perceives inputs, reasons over them, and takes actions toward goals.”
2. Why the Term Is Confusing
Different platforms use “AI agent” to mean different things:
Machine learning
Autonomous decision-makers or reinforcement-learning systems
LLM platforms
Goal-driven chains of reasoning + tool calls
Workflow tools (Zapier, n8n)
Automated task sequences triggered by conditions
Business conversations
“Any automated system that feels smart”
3. Platform Examples
The explainer includes practical comparisons such as:
OpenAI AgentKit
LangChain agents
Anthropic “tool-using” models
Zapier / n8n task agents
Custom GPTs
These examples help bridge conceptual gaps between theory and actual business tools.
Why This Use Case Matters for Business
Understanding “AI agents” is foundational for:
designing automated workflows
evaluating AI vendors
managing digital transformation
communicating effectively with technical teams
preventing over-hype or misunderstanding
ensuring responsible implementation
Leaders who misunderstand “agents” risk:
buying the wrong tools
mis-scoping projects
misunderstanding autonomy vs. assistance
overestimating AI capabilities
underestimating governance and safety needs
This explainer helps prevent those mistakes.
How AI Helped Build This Explainer
AI was used to:
generate draft structures
Compare platform definitions
refine table formatting
clarify differences in audience expectations
elevate tone to a professional, instructional voice
Polish sections for clarity and cohesion
The final document represents a human–AI collaboration, where AI handled early drafting and comparison tasks, and human judgment shaped the clarity, structure, and alignment to academic standards.
How This Supports the CIDM 6096 Learning Goals
This use case demonstrates:
structured prompting
concept synthesis
business-ready instructional communication
research translation into actionable insight
organizational readiness training
creation of shareable learning materials
This piece is a strong example of how generative AI can be used not just to answer questions but to produce professional learning assets.
Download / View Original File
📄 AI Agents Explainer (PDF)