How to pick between AI agents and AI automation

Name: How to pick between AI agents and AI automation
Uploaded: 2025-04-04T00:00:46.000Z
Duration: 27 min 37 s
Channel: Nick Saraev
Description: - Traditional automation is a reliable, linear flow without AI that can automate repetitive tasks and increase consistency across processes. - AI automation inserts AI within a fixed workflow to tailor outputs, like personalized emails, while preserving a largely predetermined path. - AI agents use

27.8K views

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April 4, 2025

Nick Saraev

How to pick between AI agents and AI automation

TL;DR

AI agents are not the same as AI automation or traditional automation. The best choice depends on the problem: traditional automation is a fixed left-to-right flow, AI automation adds a single AI node for content personalization, and AI agents decide actions and tool use. For ROI, many cases still favor simpler automation.

Transcript

AI agents and AI automations are fundamentally different things. They are tools in your kit. And just like with any tool, I see a lot of people using the wrong one for the job. So, in this video, I'm going to run you through the differences between AI agents and AI automation, which different problems these two tools solve, and how to choose the ri... Read More

Key Insights

AI is used to describe three system types: traditional automation, AI automation, and AI agents.
Traditional automation operates on a fixed left to right flow with no AI components.
AI automation adds an AI node within an otherwise linear workflow to personalize outputs.
Dynamic AI automation uses an AI to decide which path to take based on input data, increasing routing flexibility.
AI agents differ from dynamic AI automation by letting the AI decide control flow and tool usage without a fixed path.
A real ROI with AI agents is not guaranteed yet and depends on the problem and implementation.
NADN AI agents exemplify an agent approach where the model calls tools and makes decisions.
The speaker distinguishes AI agents as decision makers that select tools, not just automated steps.
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Questions & Answers

Q: What is the difference between traditional automation and AI automation?

Traditional automation is a fixed left to right workflow that executes a sequence of actions without AI. It relies on predefined steps and is highly reliable for routine tasks. AI automation adds a single AI node within that flow to personalize content or adapt actions based on data, while preserving much of the original structure.

Q: What makes AI agents different from AI automation?

AI agents differ because they use a large language model to decide the control flow of an application. They can choose which tools to call and in what order, rather than following a predetermined sequence. This allows dynamic decision making and more flexible problem solving, but requires different considerations for ROI and governance.

Q: What is an example of an AI automation flow?

An example is a traditional automation flow that includes an AI node for personalization. The flow still processes data left to right, but the AI node can tailor content such as emails based on client data, resulting in more customized outputs without changing the overall pipeline.

Q: What qualifies as an AI router rather than an AI agent?

An AI router uses AI to decide routes within a predefined set of pathways but does not create new paths. It remains constrained by the baked in logic, selecting among established routes. AI agents, by contrast, can create and execute novel sequences by calling multiple tools as needed.

Q: How does dynamic AI automation differ from static AI automation?

Dynamic AI automation uses AI to decide which path to take in real time, based on incoming data. It introduces routing decisions that can alter the workflow rather than simply filling in fields or adjusting content. This increases flexibility but still operates within a mapped framework.

Q: Is NADN AI agents considered an AI agent by the speaker?

No, NADN AI agents are not considered full AI agents by the speaker. The speaker emphasizes that real AI agents must be able to independently decide control flow and call tools, whereas NADN examples still rely on structured pathways and tool calls within a defined system.

Q: What is the practical takeaway for ROI when comparing these tools?

The practical takeaway is that for many businesses, traditional automation or AI automation can deliver reliable ROI by improving efficiency and personalization. AI agents may offer advanced flexibility, but achieving ROI requires careful evaluation of the specific problem, expected benefits, and how the agent will be governed and integrated.

Q: What guidance does the speaker offer about hype versus practical use?

The speaker cautions against hype by explaining the fundamental differences between the three system types and illustrating where each fits best. He advocates for choosing the right tool for the job based on the problem, not on trends, and emphasizes ROI and practicality over novelty.

Summary & Key Takeaways

Traditional automation is a reliable, linear flow without AI that can automate repetitive tasks and increase consistency across processes.
AI automation inserts AI within a fixed workflow to tailor outputs, like personalized emails, while preserving a largely predetermined path.
AI agents use a language model to select tools and decide actions autonomously, offering flexibility but requiring careful evaluation of ROI and use case.