What do AI agents actually do?

AI agents are everywhere in the news. They're hyped as the future of work and business automation. But what do they actually accomplish in real businesses today?

What's an AI Agent, Really?

Strip away the buzzwords and you'll find something simple: an AI agent is a large language model (LLM) with access to tools. These tools might:

• Run Google searches
• Pull data from websites
• Execute statistical analyses
• Run ML models on sales leads
• Process customer data

We've talked about leverage before. This idea with agents is the same. Instead of spending time working with each tool, you just need to learn how to communicate your needs to the agent.

And while you should understand the basics of the tools your agent can use, you don't need to be an expert. Expanding beyond your own capabilities is at the foundation of leverage.

There's No One-Size-Fits-All

When you have a hammer, everything looks like a nail. The same happens with LLMs – people want to throw them at every problem. Here's the truth: AI agents won't replace your existing tech stack. They work with it.

The Real Innovation

AI agents shine when they connect existing tools intelligently. Think of them as smart coordinators that:

1. Know when to use which tool
2. Chain tools together in useful ways
3. Make complex tools accessible through conversation

What we're really seeing is the beginning of automating useful work – not just repetitive tasks or simple workflows, but meaningful analysis and decision-making. Previous automation focused on the routine and repetitive. AI agents can automate work that actually creates value: analyzing market trends, spotting business opportunities, diagnosing problems, and generating insights.

Billions Spent on LLM Pretraining = Built-in Intuition

Agents don't just have access to tools – they understand them intuitively. Think about how seasoned analysts work:

• They know that sudden spikes in customer complaints need immediate analysis
• They instinctively cross-reference sales data with marketing campaigns
• They can tell when a statistical anomaly needs human attention
• They understand when to use simple averages versus complex regression models

LLMs have similar intuitions baked into their training. When you tell an AI agent "our conversion rates dropped this week," it inherently knows to:

1. Pull recent conversion data
2. Compare against historical baselines
3. Check for seasonal patterns
4. Look for correlating events
5. Run appropriate statistical tests

This isn't hard-coded logic – it's learned behavior, just like human expertise. The difference? An AI agent can apply this intuition consistently, 24/7, across hundreds of use cases.

Why This Matters

Your company already has powerful tools:

• Analytics dashboards
• Predictive models
• Data processing pipelines
• Customer segmentation systems

Before AI agents, you had to:

• Run these tools manually
• Set up rigid schedules
• Define fixed workflows
• Train people on complex interfaces

Now you can just ask: "Analyze this month's customer churn data and send me the insights." The agent figures out which tools to use and how to combine them.

The Bottom Line

AI agents aren't replacing your existing AI and analytics tools. They're making them work better together. They're turning your collection of powerful-but-isolated tools into an integrated system that anyone can use through natural conversation.

That's not just hype – it's practical value you can implement today.