Learn
Connect
Blog
Resource hub
Featured resource
2026 Tech Forecast
2026 Tech Forecast

1,500+ tech insiders, business leaders, and Pluralsight Authors share their predictions on what’s shifting fastest and how to stay ahead.

Download the forecast

Introduction to Agentic AI: A No-Code Approach

Course Summary

This course introduces non-technical professionals to the emerging world of agentic AI systems. Participants will explore the core concepts behind autonomous agents powered by large language models (LLMs), understand the decision points between different AI deployment strategies, and build a working no-code agent prototype using Make.com and OpenAI. The course sets up participants for deeper technical or strategic training and provides shared terminology to collaborate effectively across roles.

Purpose
Develop a foundational understanding of agentic AI systems and build a no-code agent prototype 
Audience
Non-technical professionals and anyone interested in the practical applications of AI agents
Role
Product Managers | Project Managers | Business Analysts
Skill level
Beginner
Style
Lecture | Hands-on Activities
Duration
1 day
Related technologies
Agentic AI | LLMs | No-Code Platforms
 
Learning Objectives
  • Explain what agentic AI is and where it applies in real workflows
  • Describe the basic components of LLM-powered agents
  • Compare agents with alternatives like Retrieval-Augmented Generation (RAG) and fine-tuning
  • Build a simple, no-code autonomous agent 

What you'll learn:

In this course, you'll learn:

Introduction to Agentic AI and Autonomy

  • Defining autonomy and agentic behavior in AI systems
  • Mapping the agentic spectrum: from scripts to LLM-powered agents
  • When agentic systems make sense in workflows
  • Core terminology: agents, tools, environments, and memory

Architectures of Agentic Systems

  • High-level anatomy of agent systems: memory, tools, reasoning loop
  • Comparing agent frameworks: ReAct vs. planner-executor
  • Mental models: how LLM agents “think” with intermediate steps
  • Visual walkthrough: how decisions lead to actions

Agents vs. RAG vs. Fine-Tuned Models

  • Tradeoffs between agents, retrieval-based systems, and fine-tuning
  • Understanding complexity, cost, and interpretability
  • Use case matrix: which AI approach fits the task

Constructing an AI Agent (No Code)

  • Designing a no-code agent workflow using Make.com and OpenAI
  • Building a Slack triage bot with message filtering
  • Modifying workflows to respond to urgency or keyword triggers
  • Best practices for prototyping agent logic with no-code tools

Risks & Guardrails for Autonomous Systems

  • Common failure modes; hallucinations, loops, misalignment
  • Guardrails in no-code systems: prompt limits, retries, and timeouts
  • Debugging and interpreting unexpected behaviors

Ideation and Use Case Mapping

  • Framework for scoping agent use cases in business contexts
  • Individual ideation: drafting two pilot workflows
  • Group feedback and alignment with agent capabilities 
  • Preparing for scale: success metrics and ownership questions

Introduction to Agentic AI: A No-Code Approach

real world content

Real-World Content

Project-focused demos and labs using your tool stack and environment, not some canned "training room" lab.
expert practitioners

Expert Practitioners

Industry experts that bring their battle scars into the classroom.
experiential learning

Experiential Learning

More coding than lecture, coupled with architectural and design discussions.
fully customized

Tailored Outlines

Once-size-fits-all doesn't apply to training teams. That's where we come in!

Dive in and learn more

When transforming your workforce, it’s important to have expert advice and tailored solutions. We can help. Tell us your unique needs and we'll explore ways to address them.

Let's chat

By clicking submit, you agree to our Privacy Policy and Terms of Use, and consent to receive marketing emails from Pluralsight.
Related Pages