Artificial Intelligence (AI) has rapidly evolved beyond just chatbots and text generators. If you’re someone who regularly uses AI tools like ChatGPT or Claude and wants to understand what’s really going on behind the scenes, especially when it comes to AI agents, this guide is for you.
In this post, we’ll simplify terms like “agentic workflows,” “multi-agent design patterns,” and “RAG” (Retrieval Augmented Generation) using real-world analogies and practical insights. We’ll explore three progressive levels of AI, from passive responders to autonomous multi-agent systems.
3 Levels of AI Pyramid
Level 1: Large Language Models (LLMs) — The Foundation
AI tools like ChatGPT, Gemini, and Claude operate on top of LLMs, which are trained to respond to prompts by generating text. This is what most people are familiar with.
Example: You ask ChatGPT to write a birthday message. It responds instantly with a warm, well-structured paragraph. But it has limits:
- LLMs can’t access real-time or personal data, such as your Google Calendar, unless they are explicitly integrated.
- LLMs are passive — they only act when prompted and don’t take initiative.
This is known as “non-agentic” or one-hot prompting, which involves a single prompt and a single response.
Level 2: AI Workflows — Adding Logic and Tools
Workflows are a step above LLMs. They add a sequence of tasks to solve problems or retrieve data across platforms.
Example: You set up a workflow where every time you ask about a personal event, the AI pulls information from your calendar and returns a response. If you ask, “What time is my meeting with Elon Musk?”, the AI can retrieve your calendar data.
However, if you follow up with, “What’s the weather like that day?”, it will fail—unless that step was predefined.
This highlights a key trait: Workflows follow human-defined paths. You, the user, decide what the AI can or cannot do. A well-known type of workflow is RAG, where AI augments its responses by retrieving external data (like news or documents).
This is a semi-agentic approach: logic and tools are there, but the AI doesn’t decide how or when to use them outside the workflow.
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Level 3: AI Agents — Independent Decision Makers
Now we move on to the exciting part. AI agents transition from following fixed steps to autonomously reasoning, planning, acting, and continually improving their outputs. They act like mini-employees: you give them a goal, and they figure out how to achieve it.
Agentic Workflow vs Non-Agentic Workflow
- Non-agentic: Start-to-finish, single step.
- Agentic: Circular, iterative—AI revises, reflects, and improves its output.
A Real Agent Thinks Like This When Prompted:
“To write this blog, I need to do web research, summarise key points, critique my draft, and revise based on tone and readability. Let me fetch the tools I need and repeat the process until the result is solid.”
And it does that without you having to tell it each step.
The Four Core Agent Design Patterns
According to an AI expert Anding, four foundational “agentic design patterns” help us understand how modern agents work:
- Reflection – AI critiques its own output and iterates to improve.
- Tool Use – AI uses external tools (e.g., calendars, search engines, calculators).
- Planning and Reasoning – AI breaks down a task into logical steps and executes them.
- Multi-Agent Systems – Multiple AI agents work together with different roles (like a writer + editor).
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Multi-Agent Systems: When AIs Work Together
Why stop at one agent when you can have a team?
In a multi-agent architecture, different AIs specialise in tasks, just like humans in a company.
Examples:
- A writer agent drafts an article.
- An editor agent gives feedback.
- A calendar agent checks availability.
- A decision-making agent compiles everything into a report.
These systems can follow patterns like:
- Sequential (assembly line logic)
- Hierarchical (manager-agent relationships)
- Parallel (agents work simultaneously)
- Asynchronous (agents act independently over time)
- Hybrid (a mix of all the above)
No-Code Agent Workflows Are Now a Reality
You don’t have to be a programmer to build AI agents. Using tools like n8n, even beginners can build Telegram-based AI assistants that:
- Talk with you via text or voice.
- Pull tasks from your Google Calendar.
- Create new events.
- Prioritise your to-dos.
This is possible through modular AI design:
- Task: What does the user want?
- Answer: What’s the expected result?
- Model: Which LLM powers the agent?
- Tools: What APIs or services does the agent access?
Future Opportunities: Build the Next AI Agent Business
According to Y Combinator, for every SaaS company, there will be an AI agent equivalent. This means:
“If there’s a SaaS app that automates invoices, there’s room for an AI agent that does it smarter, faster, and with less input.”
Think of Shopify, Canva, Trello, Notion—then imagine an AI-powered agent version.
Now’s the time to experiment, build, and iterate.
Summary: Understanding AI’s Evolution
| Level | Description | Key Trait |
| Level 1: LLM | AI responds based on training data. | Passive, limited knowledge |
| Level 2: Workflow | AI follows human-defined tasks and logic. | Predefined logic, tool access |
| Level 3: Agent | AI reasons, acts, revises, and manages tasks independently | Autonomy, decision-making |
Final Thoughts
AI agents aren’t just futuristic—they’re here and evolving fast. Whether you’re a business owner, a creative, or a curious tinkerer, understanding agentic design unlocks new doors to productivity and innovation.
If this article helped you, consider diving deeper yourself:
- Learn prompt engineering
- Try building a no-code AI workflow
- Start turning your business idea into an agent
Let us know in the comments what kind of AI agent you’d love to see next, or build yourself!
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