Workflows, AI Automations, and Agentic AI - What’s Really Different?

1. What Is AI?

AI (artificial intelligence) is any system designed to perform tasks that normally require human thought - like recognizing speech, detecting objects, making predictions, or playing strategy games. AI can be as simple as a spam filter or as complex as a self-driving car.

Key points about AI:

• Broad field: Covers everything from expert systems to computer vision.
• Not always “intelligent”: Many AI systems are narrow - built for one task.
• Machine learning inside: Modern AI often relies on statistical models trained on data.

AI underpins everything else in this article. LLMs, agents, and automations are all subsets or applications of AI.

2. Large Language Models (LLMs)

LLMs are advanced AI systems trained on huge text datasets to understand and generate human-like language. ChatGPT, Claude, Gemini, and LLaMA are all LLMs.

Why LLMs matter:

• They can generate natural, fluent text for summaries, emails, code, or explanations.
• They understand context well enough to answer follow-up questions.
• They act as a “brain” inside many AI tools - but on their own, they don’t take actions.

Common use cases:

• Chatbots and virtual assistants
• Content creation and rewriting
• Code completion
• Language translation

Think of an LLM as a powerful conversational engine. It’s smart about language but can’t interact with the world without extra components.

3. AI Agents

An AI agent is more than a model - it’s a system that can pursue a goal, decide on steps, and act. An AI agent usually includes:

• Reasoning engine: Often an LLM for language-based tasks.
• Tools or APIs: To fetch data, send emails, query databases, or control devices.
• Memory or state: So it can remember previous actions or instructions.
• Planning/autonomy: To adapt its approach if something fails or conditions change.

Example scenario:
A sales assistant agent could:

1. Analyze incoming leads from a CRM.
2. Draft personalized emails using an LLM.
3. Use a calendar API to book meetings.
4. Adjust its strategy if a lead hasn’t replied in a week.

Without the memory and planning, you’d just have a chatbot that answers one question at a time. The agent adds persistence and action.

4. Workflows

A workflow is a pre-defined sequence of steps to accomplish a task. Traditional workflows are rule-based and non-adaptive.

Examples:

• “If a customer submits a form, send an email, then notify sales.”
• “When a file is added to Google Drive, copy it to Dropbox.”

Workflows are the backbone of automation platforms like Zapier, Make, and n8n. They’re predictable and easy to set up but don’t think for themselves.

5. AI Automations

When you add AI tasks to a workflow, you get AI automation. These are still rule-driven sequences, but they use an AI component for smarter output.

Examples:

• Summarizing customer support tickets with GPT before forwarding them to an agent.
• Using a model to classify images before filing them into folders.
• Auto-generating social media captions in Zapier using an LLM.

Key difference from agents:
AI automations react to triggers—they don’t set goals or adjust strategies on their own.

6. Agentic AI

Agentic AI refers to AI systems with greater autonomy and adaptability. They don’t just execute tasks - they can decide which tasks to run, plan multi-step strategies, and coordinate with other agents or tools.

Features of agentic AI:

• Goal-oriented planning across multiple steps.
• Dynamic tool selection - choosing which API or action is best.
• Memory that carries context across sessions.
• Ability to collaborate with other agents or humans.

Agentic AI is still early. Experimental systems like OpenAI’s Agent SDK, CrewAI, Microsoft AutoGen, and Google’s AlphaCode agents are moving in this direction. But most production systems today are closer to AI automations or narrow agents, not fully agentic AI.

7. Comparing the Concepts

8. Why the Distinction Matters

Understanding the difference helps in:

• Project planning: Choosing the right approach saves development time.
• Marketing clarity: Avoiding hype builds trust with customers.
• Career growth: Using correct terms positions you as an informed professional.

If you’re a developer, knowing where your system falls on this spectrum also sets expectations for cost, complexity, and risk.

9. Real-World Use Cases

• Workflows: A small e-commerce store uses Zapier to email customers after a purchase.
• AI Automations: A marketing team uses Make to auto-generate Instagram captions via GPT.
• AI Agents: A support bot handles refund requests, escalates complex cases, and logs data in a CRM.
• Agentic AI: Experimental multi-agent research systems coordinate stock trading strategies or autonomous robotics - still mostly in labs or limited pilots.

10. Future Outlook

Over the next few years, expect:

• More hybrid systems: Combining workflow tools with lightweight agents.
• Better memory and state handling: Even consumer apps will remember context across sessions.
• Increasing enterprise adoption of agentic AI for planning, scheduling, and operations.

But fully autonomous, unsupervised agents operating at scale are still rare—human oversight remains crucial.

11. Getting Started

If you’re building or choosing tools today:

1. Start small: Use AI automations for simple wins like summarizing emails or tagging content.
2. Experiment safely: Try LLM-powered bots inside well-defined workflows.
3. Add memory later: When tasks need context, explore frameworks like LangChain or AutoGen.
4. Plan for oversight: Even advanced agents need monitoring and fallback steps.

Conclusion

AI, LLMs, agents, workflows, automations, and agentic AI form a spectrum - from static rule-based systems to autonomous goal-pursuing entities. Understanding where each fits makes it easier to choose the right approach for your project or business. While agentic AI is still developing, even basic AI automations can deliver significant value today.