AI Agents Clearly Explained: From LLMs to Autonomous Workflows
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Understanding the power and potential of AI agents no longer requires a technical background. As AI tools become integral to how we work and live, grasping the difference between simple chatbots and fully autonomous AI agents is key for anyone using or evaluating AI-driven solutions. This post breaks down the evolution from passive Large Language Models (LLMs) to dynamic AI agents, using real-world examples and actionable insights based on Jeff Su’s "AI Agents, Clearly Explained."
What Are AI Agents? Why Should You Care?
Most explanations of AI agents are either too technical or too basic. Yet, if you use AI tools—even without coding skills—understanding how agents differ from basic chatbots helps you make smarter decisions and unlocks new workflow possibilities.
"AI agents are not just smarter chatbots; they are autonomous decision-makers that reason, act, and iterate to achieve goals."
Key Value: Grasping how AI agents work can help you automate more, save time, and stay ahead as AI reshapes everyday workflows.
Level 1: Large Language Models (LLMs)
What Are Large Language Models?
LLMs like ChatGPT, Google Gemini, and Claude are advanced AI models trained on massive text datasets. They excel at:
- Generating and editing text
– Summarizing information - Answering general knowledge questions
How They Work:
- You provide a prompt or question (input).
- The LLM generates a response (output) based on its training data.
Traits of LLMs:
- Passive: They only respond when prompted.
- Limited Access: They don’t know your personal or company data unless you tell them.
Example:
- "Draft an email requesting a coffee chat." — The LLM provides a well-written email.
- "When is my next coffee chat?" — The LLM fails, since it can't access your calendar.
Tip: LLMs are powerful but can't act on or access private data without explicit connection or integration.
Level 2: AI Workflows – Connecting the Dots
What Are AI Workflows?
AI workflows string together multiple tasks or tools, often involving LLMs and external data sources. These are typically:
- Predefined by humans (the "control logic")
- Linear or branching, but always limited to the steps you specify
How Workflows Work:
- You set rules: e.g., "If I ask about a personal event, fetch the info from my Google Calendar."
- The workflow follows these steps, sometimes using APIs or multiple tools.
Example Workflow:
- Fetch event from Google Calendar → Get weather for event day → Convert summary to audio
Limitations:
- Can only follow pre-set paths
- Still requires human decision-making for changes or improvements
Retrieval Augmented Generation (RAG)
- RAG allows LLMs to "look up" answers from external sources before responding.
- Still part of a workflow, not an agent—because the human sets the lookup rules.
Pro Tip: RAG is useful for up-to-date or proprietary information, but doesn’t grant true autonomy.
Level 3: AI Agents – Autonomous Reasoning and Action
What Makes an AI Agent?
The leap from workflows to agents is about autonomy:
- The AI agent replaces the human as the decision-maker.
- It reasons about the best way to achieve a goal.
- It takes action, uses tools, and iterates until the goal is met.
Traits of AI Agents:
- Reason: Decide how to approach a goal or solve a problem.
- Act: Use available tools (APIs, web apps, databases) to execute steps.
- Iterate: Review outputs, self-critique, and repeat until satisfied.
Example:
Instead of you deciding how to summarize news and draft LinkedIn posts, the agent:
- Determines best sources and methods (e.g., fetch links, summarize, draft posts).
- Chooses which tools to use (Google Sheets vs. Excel, Perplexity, Claude, etc).
- Adjusts outputs autonomously for quality (e.g., makes posts funnier or more engaging without manual prompt rewriting).
Real-World AI Agent Examples
- Andrew Ng’s Vision Agent: Automatically identifies and tags videos of skiers—not by following a set script, but by reasoning what a skier looks like and searching footage accordingly.
- Workflow Automation: Agents can schedule social posts, summarize articles, and improve their own outputs without human intervention.
"The defining trait of an AI agent is that it receives a goal, reasons about how to achieve it, and acts—iteratively—until the goal is met."
Actionable Tips: How to Move Toward Agentic AI in Your Workflows
1. Identify Tasks Ready for Autonomy
- Look for repetitive workflows where you’re the "decision bottleneck."
- Example: Social media scheduling, report generation, or customer support responses.
2. Integrate Tools that Enable Reasoning
- Use platforms (like Make.com, Zapier, or custom agent frameworks) that allow LLMs to choose next steps, not just follow scripts.
3. Allow for Iteration and Self-Critique
- Add feedback loops: let the agent evaluate, refine, and improve its own outputs.
- Example: AI-generated LinkedIn posts are critiqued by another model before publishing.
4. Monitor and Adjust
- Start with pilot projects, monitor agent performance, and iterate on setup.
- Keep humans in the loop for oversight, especially for critical tasks.
Key Differences: LLMs vs Workflows vs AI Agents
| Level | Who Decides? | Can Access External Data? | Can Iterate? | Example Use Case |
|---|---|---|---|---|
| LLM | Human | No | No | Writing a single email |
| AI Workflow | Human (sets path) | Yes (via APIs, RAG) | No (unless human) | Daily social post automation |
| AI Agent | AI (autonomous) | Yes | Yes | End-to-end content creation |
Conclusion: Unlocking the Power of AI Agents
AI agents represent the next evolution in automation and intelligence—moving from reactive chatbots and rigid workflows to autonomous problem-solvers. By understanding the distinctions and gradually integrating agentic capabilities, you can streamline your work, unlock new efficiencies, and stay at the forefront of the AI revolution.
Key Takeaways:
- LLMs are powerful responders, but passive.
- AI workflows automate multi-step processes, but require human direction.
- AI agents reason, act, and iterate—autonomously achieving goals.
Ready to level up your workflows with AI agents? Start by identifying tasks you can delegate, experiment with agentic frameworks, and let AI handle more of the decision-making. For more practical guides and in-depth tutorials, check out related articles on AI automation and workflow optimization.
If you found this helpful, explore our guide on building prompt databases in Notion or subscribe for more actionable AI insights!
