Why agentic AI is better for complex automation

Hello to all! Eleo here (still pronounced ‘Leo’ if you forgot or haven’t read some previous awesome content ??), and I’ve got a question for you: Ever wonder how AI agents can work like your own SEO team, each with their own job, speeding up your results?

Imagine automating your SEO like running a business, with each agent doing their part.Ready to ZAP your SEO into shape with agentic automation? Let’s dive in! ??

Let's make this super simple. Think of AI agents like members of different teams in a company. Imagine how, in a business, you have different departments—each with its own role. There's one team for marketing, another for finance, another for customer service.?

All these teams work independently, but together, they achieve the company’s big goals.?

AI agents are pretty much the same. Let's break it down.

Agents = Distributed Workers

In the real world, when a company tackles a big project, it doesn’t rely on one person. You’ve got teams working on their part: marketing is pushing campaigns, sales is closing deals, support is solving customer issues.?

Everyone has a task, but together, they make the company move. AI agents work the same way in automation.

Each AI agent has a specific job:

• One agent might focus on keyword research for SEO.
• Another handles site audits, checking for broken links or missing metadata.
• A third might handle backlink monitoring, ensuring that all links are healthy and relevant.

These agents work independently, but they collaborate—like teammates—to get the big job done faster and more efficiently (Markovate , E42).

But what exactly is an agent?

AI agents aren’t just regular bots—they're powered by Large Language Models (LLMs) like GPT-4, which means they can understand, process, and act on huge amounts of data in ways that feel smart and adaptive.

Why Use LLMs for AI Agents?

Large Language Models (LLMs) are like supercharged brains for these agents. They’ve been trained on tons of data—think of them as interns who have read every book in the library. This makes them great at tasks that involve analyzing language, processing data, or generating content.

For example:

• Keyword research agent: An LLM-powered agent can sift through millions of search queries and tell you which keywords will give your content the best chance of ranking.
• Site audit agent: It can crawl your website, flag broken links, and check your metadata, ensuring your site is in top shape.

But not every LLM is suited for every task. Just like you wouldn’t ask your HR team to write code, different LLMs are better at specific jobs. Some LLMs are better at text generation (like writing blog posts), while others excel at analyzing huge datasets (like checking your entire backlink profile).

Also, Smaller models often have better and precise outputs. Perfect for slavery?!?

Let’s break down how some of these smaller LLMs are used in real:

1. T5 (Text-to-Text Transformer) – Used for tasks like text summarization and translation in industries like finance, T5 is precise and efficient, ideal for handling complex text processes.

2. DistilBERT – A lightweight version of BERT, 60% smaller but retains 97% of BERT’s performance. It’s perfect for fast, real-time applications like customer support chatbots (McKinsey & Company)

3. ALBERT – Optimized for low-resource environments, ALBERT is great for text classification in mobile apps, with almost the same performance as larger models like BERT.

4. MiniLM – Compact and efficient, MiniLM excels at document ranking and knowledge queries. Microsoft uses it for quick document understanding.

5. TinyBERT – Specifically built for edge devices like smartphones, TinyBERT processes text fast for mobile applications like voice assistants or email filtering.

These models deliver high performance while being faster and more efficient, making them ideal for precise tasks in resource-limited settings. Zappit leverages similar models to power fast and accurate SEO automation!

Teamwork Makes the Dream Work

Let’s say your company is about to launch a new product. You’ve got:

• Marketing working on ads and promotions.
• Design creating the visuals.
• Development building the product.

Similarly, when you automate SEO with agents:

• A data analysis agent looks at trends in search data.
• A content generation agent writes blog posts.
• A monitoring agent watches your site’s performance to make sure nothing breaks.

All these agents work together in harmony. This is what’s called distributed learning—just like real-world teams learn their jobs and improve over time, these agents do too (Droids in Business Systems).

"Coming together is a beginning. Keeping together is progress. Working together is success." - Henry Ford

Real-Life Example: How Companies Use Agentic Automation

1. Amazon: Amazon uses AI agents to handle everything from product recommendations to supply chain logistics. Each agent works on a specific task: one analyzes customer behavior, another handles product fulfillment, and a third optimizes delivery routes. It’s this distributed, agent-based approach that makes Amazon’s operations so efficient (McKinsey & Company).
2. Zappit: Zappit is another prime example of agentic automation in action. Zappit uses specialized agents for SEO automation, each focusing on a different part of the SEO puzzle. There’s an agent for keyword research, another for backlink tracking, and even one for optimizing on-page content. By distributing the workload across different agents, Zappit helps companies get more accurate, faster SEO results without burning out their teams.
3. Netflix: Netflix’s recommendation engine runs on an agent-based model. Different AI agents monitor your viewing habits, analyze popular content trends, and predict what you’re likely to watch next. It’s why Netflix is so good at suggesting that perfect show for you right when you need it (McKinsey & Company).

Why Metrics Matter

Just like companies track performance metrics (sales, customer satisfaction, etc.), you can measure how well your AI agents are working. Here are some common metrics:

• Task completion time: How quickly does the agent finish its job?
• Accuracy: How often does it make mistakes (hopefully never)?
• Collaboration efficiency: Do agents share data and insights well, or do they trip over each other?

With these metrics, you can tweak your agents to work even better—just like giving feedback to a human team to help them improve (E42 ,Campion Software).

So next time you think of automation, remember: it’s not about one AI doing everything, but a team of agents, each with its own job, coming together to zap through your tasks and make life easier. Whether it’s Zappit helping with your SEO, Amazon revolutionizing logistics, or Netflix finding your next binge-worthy series, agentic automation is the future.

The tasks we break, like paths we tread,

Each agent pulls its weight ahead.

With effort shared, the work flows free,

Together builds the strategy.

In pieces small, the big is won,

And SEO shines when all is done. ??

Now, before you sneak off, I’ve noticed a serious lack of likes and comments around here… ?? Don’t make me send my agents to check on you! Hit that like or drop a comment before they do! ????

Zappit—where agents do the heavy lifting for your SEO.

https://zappit.ai/