Not long ago, "AI" was something people talked about in futuristic terms—self-driving cars, humanoid robots, or distant sci-fi promises. Today, AI has quietly become a daily utility. We ask it to write emails, summarize documents, translate text, and brainstorm ideas.
But just as people got comfortable with chatting to AI, the conversation started to shift again. Instead of hearing only about "chatbots" or "LLMs," we now hear terms like AI Agents, Agentic Workflows, and Autonomous Systems. For many users, this feels like stepping out of a familiar pond and into a much deeper ocean.
To understand what is actually changing—and why it matters—we can break the evolution of AI into three clear levels:
Passive Models, Structured Workflows, and Autonomous Agents.
Level 1: Large Language Models (LLMs) – Knowledge Synthesizers
At the foundation of today's AI boom are Large Language Models (LLMs) such as ChatGPT, Claude, and Gemini, developed by organizations like OpenAI, Anthropic, and Google.
How LLMs Work
At this level, the relationship between humans and AI is linear and passive. You provide a prompt, and the model generates a response based on patterns it learned from massive amounts of text data.
An LLM does not "understand" the world in a human sense. Instead, it predicts the most likely next word (or token) based on statistical relationships. That's why it can sound confident, fluent, and intelligent—yet still be wrong.
In many ways, an LLM is a brilliant mimic. It reflects humanity's collective knowledge back to us in a polished form.
The Critical Limitations
Despite their power, LLMs have two major blind spots:
1. The Information Silo
LLMs are trained primarily on public or generalized data. They do not automatically know:
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Your calendar
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Your internal documents
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Your private Slack messages
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The spreadsheet you edited five minutes ago
Without explicit integration, your personal or company data simply does not exist to them.
2. Passive Nature
An LLM is reactive. It does nothing unless prompted.
It does not:
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Check your inbox on its own
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Notice a deadline approaching
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Decide to follow up with a client
In this stage, the human is the orchestrator. You decide what to ask, when to ask it, and how to use the output. The AI thinks only when you tell it to.
Level 2: AI Workflows – The Rise of Managed Logic
The second level introduces something new: structure.
If Level 1 is like having a dictionary, Level 2 is like following a recipe.
What Is an AI Workflow?
An AI workflow is a predefined sequence of steps where AI is embedded into a process. Humans design the logic:
"First, do A. Then take the result and do B. Finally, output C."
This is where tools like Make.com, Zapier, and internal automation systems come into play. AI becomes part of a pipeline rather than a standalone chat window.
The Role of RAG (Retrieval-Augmented Generation)
One of the most important ideas at this level is RAG. Despite the intimidating name, the concept is simple:
Before the AI answers, it retrieves relevant information from an external source.
For example:
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You ask, "What meetings do I have tomorrow?"
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The workflow queries your Google Calendar.
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The retrieved data is passed into the LLM.
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The LLM generates an accurate, personalized answer.
This solves the "information silo" problem from Level 1—but only in places where humans explicitly wire the system.
The Fragility of Workflows
Level 2 workflows are efficient, but they are also brittle.
As demonstrated in many automation demos—such as Jeff Su's examples connecting Google Sheets with AI models—the system works beautifully as long as reality matches the script.
If something unexpected happens:
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A new data source is needed
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The question changes slightly
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The environment evolves
The workflow fails. It cannot adapt unless a human redesigns it.
At this level, humans are still the architects. Automation exists, but true intelligence does not.
Level 3: AI Agents – Autonomous Decision Makers
Level 3 represents a fundamental shift: decision-making power moves from humans to AI.
Instead of giving AI a script, we give it a goal.
The ReAct Framework: Reason + Act
Most modern AI agents follow some version of the ReAct framework:
Reasoning
The agent breaks a goal into sub-tasks.
For example:
"Research this company and write a personalized outreach email."
The agent reasons:
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I need to find their website
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I should read recent news
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I should identify potential pain points
Acting
The agent selects tools dynamically:
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Web search
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Document reader
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CRM system
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Spreadsheet or database
Observation & Iteration
After each action, the agent evaluates the result.
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Was the information sufficient?
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Should I try another search?
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Do I need more context?
This loop—reason, act, observe, repeat—is what makes agents adaptive.
Self-Correction and Multi-Agent Systems
One of the most powerful ideas in agentic systems is self-critique.
In advanced setups:
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One agent generates a draft
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Another agent reviews it against criteria
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They iterate internally
The human only sees the final result.
This is no longer "AI helping you write."
This is AI managing quality on its own.
A Real-World Mental Model: The "Skier" Example
AI educator Andrew Ng offers a helpful way to visualize these levels.
Imagine you have 10,000 hours of mountain video footage and want to find every clip containing a skier.
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Level 1 (LLM):
The AI explains what a skier looks like. You still watch everything yourself. -
Level 2 (Workflow):
You program rules: "Look for humans + snow." Results improve, but false positives remain. -
Level 3 (Agent):
You give a goal: "Find and tag all skiers."
The agent reasons about movement, equipment, context, zooms in when unsure, and delivers a curated result.
That difference is autonomy.
How to Build Real Agentic Systems with n8n, CrewAI, and Copilot Studio
For many people, AI still means opening a chat window and typing a question. That works—but it is only the starting point. If you want AI to actually do work for you, you need to move beyond chatting and into AI agents.
This guide focuses on hands-on thinking, not theory. Instead of asking what is an agent, we will answer a more useful question:
“How do I build something that behaves like an agent today?”
We will walk through three practical setups using:
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n8n
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CrewAI
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Microsoft Copilot Studio
Each example shows:
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What the agent's goal is
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What tools it can use
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How decision-making actually happens
Before You Start: Think in Goals, Not Steps
Traditional automation asks you to define every step.
Agentic systems start with a goal.
Instead of:
"First check this, then do that, then send an email."
You think:
"Keep me informed about X and take action when needed."
The more clearly you define the goal, the smarter your agent can behave.
Example 1: Building a Semi-Autonomous Agent with n8n
What n8n Is Best At
n8n is ideal if you want:
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Visual workflows
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Full control over integrations
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AI agents that interact with real systems (Sheets, APIs, email, databases)
n8n does not "feel" intelligent by default—but it becomes powerful when you let AI decide inside the workflow, instead of hardcoding everything.
Agent Goal
"Monitor new leads, decide if they are worth following up, and draft a personalized response."
Tools the Agent Can Use
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Google Sheets (lead source)
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OpenAI / Claude node (reasoning + writing)
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Email or CRM API
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Optional: Web search API
How the Agent Works (Conceptual Flow)
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Trigger
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A new row is added to a Google Sheet (new lead).
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Context Gathering
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Pull company name, website, notes.
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Optional: search the company online.
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Reasoning Step (Key Agent Moment)
Instead of hard rules, you ask the LLM:"Based on this information, is this lead high, medium, or low priority? Explain why."
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Decision Branching
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If high priority → draft a personalized email
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If medium → add to review list
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If low → ignore or archive
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Action Execution
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Send email
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Update CRM
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Log reasoning output for transparency
Why This Is Agentic (Not Just Automation)
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The AI evaluates, not just processes.
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You can change the goal without rebuilding everything.
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The agent adapts when inputs vary.
This is n8n acting as a lightweight agent runtime, not just a workflow builder.
Example 2: A True Multi-Agent System with CrewAI
What CrewAI Is Best At
CrewAI is designed specifically for agent collaboration:
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Multiple agents
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Clear roles
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Internal debate and review
If n8n is about orchestration, CrewAI is about thinking.
Agent Goal
"Research a topic and produce a high-quality article suitable for publication."
Agents and Roles
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Research Agent
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Finds sources
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Extracts key points
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Writer Agent
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Drafts the article
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Structures the narrative
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Editor Agent
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Reviews clarity
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Checks tone and logic
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Requests revisions
How the System Operates
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You give a single goal:
"Write a practical guide to AI agents for beginners."
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The Research Agent decides:
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What to search
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When information is sufficient
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The Writer Agent:
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Uses research output
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Creates a first draft
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The Editor Agent:
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Critiques the draft
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Requests changes if needed
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The system loops internally until quality criteria are met.
Why This Is Powerful
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Humans don't micromanage.
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Quality improves through internal feedback.
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The system resembles a small digital team.
This is AI as coworkers, not tools.
Example 3: Business-Focused Agents with Microsoft Copilot Studio
What Copilot Studio Is Best At
Copilot Studio shines in:
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Enterprise environments
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Microsoft ecosystem (Outlook, Teams, SharePoint)
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Non-technical deployment
It is less flexible than open frameworks, but far easier to adopt at scale.
Agent Goal
"Answer employee HR questions accurately using internal company documents."
Tools the Agent Can Use
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SharePoint documents
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Internal HR policies
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Outlook / Teams chat interface
How the Agent Operates
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User Question
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"How many vacation days do I have left?"
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Context Retrieval
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Agent searches internal HR docs
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Pulls policy and user-specific data
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Reasoning
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Interprets policy
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Applies it to the user's role
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Response
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Delivers a clear answer
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Optionally links source documents
What Makes This an Agent
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It reasons over private data.
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It adapts answers to user context.
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It reduces human workload without manual routing.
This is agentic behavior wrapped in enterprise safety rails.
Final Takeaway: Start Simple, Think Agentically
You don't need a perfect agent on day one.
Start by asking:
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What decision am I still making manually?
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What context does AI need to make it instead?
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What tools should AI be allowed to use?
Once you shift from step-by-step instructions to goal-based thinking, you are already designing agents.
AI agents are not about replacing humans.
They are about removing friction between intention and execution.
