The three types of AI agents and how to get them to work for you

Okay, sometimes I get passionate about cool technology. We’ll be in a meeting and I’ll excitedly demo some new idea or share something about an interesting startup. As the product manager working with “Emerging Products” at Contentstack, a big part of my job is staying up to date on these things.

But a lot of times the things I talk about are still theoretical. Or even if they’re working in some industries, they are not yet enterprise ready.

That used to be the case with AI agents, but with some of the new agentic capabilities that the models have now, I think they’ve moved from an “interesting demo” to “oh man, this is actually useful.”

The fact is, if you're not thinking about agents yet, you're already behind. Sorry if that sounds harsh, but (according to a Hostinger report) 78% of organizations are already using AI somewhere, and the ones getting real ROI are the ones who understand agents. So let's dig into what they are, how they work, and most importantly, where you should start.

What are AI agents, really?

Here's the simple formula: Agents = LLM + APIs + Instructions/Goals

That's it. Every AI agent out there is basically a large language model that can understand context, has been given specific goals or instructions and has access to tools (APIs) to actually do stuff. 

At Contentstack, we're seeing three distinct types emerge, and understanding the differences is crucial to understand where to get started.

The three types of agents

1. AI assistants

These agents live right in your workspace; think Microsoft Office, Google Workspace, or in our case, the Contentstack admin. The key here? They're authenticated to YOU. They can't do anything you can't do, which is extremely important in an enterprise environment.

Imagine if there were a powerful agent helping you in your daily work. You could do things like:

• "Take the last three iPhone reviews I've done, summarize them all together and create a new summary article called ‘The year in review’"
• "Have we missed translating any blog posts into German in the last two weeks?"
• "I just uploaded 24 new products. Can you check them against our company Knowledge Vault and see if any of the information seems off?"

The beauty of co-pilots? They're basically productivity on steroids. We have customers who are saving HOURS per week with AI automation. That's real time back in your day for the stuff that actually matters.

2. Workflow agents

These are like automations, but way smarter. They trigger when something happens (such as a post was published, an audience milestone, or content was updated) and then they actually think about what to do next.

Real examples we're implementing:

• Checking every piece of content for Personally-Identifiable Information (PII) before it goes live
• Auto-translating content but then checking if it matches your brand voice in that language
• Monitoring when products go out of stock and automatically updating all related content

These are your quick ROI wins. Seriously. Find your most annoying repetitive task and throw a workflow agent at it.

3. Autonomous agents

Autonomous agents run in the background, constantly monitoring, analyzing and taking action based on what they find.

We're looking at them for:

• Scanning for outdated content across thousands of pages
• Finding broken links before customers do
• Identifying duplicate or conflicting information across your content ecosystem
• Monitoring performance metrics and alerting you when something's off

The key with autonomous agents? Start with read-only operations. Let them find problems and tell you about them before you let them start fixing things automatically.

Agent-to-agent communication

Okay, this is the part that gets me genuinely excited. We're building the infrastructure for agents to talk to each other, and it's going to change everything.

Two main protocols are emerging:

• MCP (Model Context Protocol) from Anthropic: Think of it as the HTTP for agents
• A2A (Agent-to-Agent Protocol) from Google: More focused on capability discovery, task delegation and agent-to-agent communication

Here's why this matters

Imagine this scenario: A content-monitoring agent notices you're about to promote a product on your homepage. It reaches out to your inventory management agent to check stock levels. That agent talks to your supply chain agent to see if more inventory is coming. All of this happens in seconds, automatically.

Or consider this: When someone searches in ChatGPT, it spawns multiple research agents that visit hundreds of websites. But here's the kicker: the agent is visiting your site, not the user. Your organic traffic is already dropping because of this. Sites that aren't agent-friendly are going to get left behind.

This isn't science fiction. It's happening right now.

Where should you actually start?

I know everyone wants to jump straight to autonomous agents doing all the work while you sip margaritas on a beach. But that's not how this works. Here's your actual game plan:

Step 1: Get your house in order

• Audit your data: If your data is a mess, your agents will be too
• Document your APIs: Agents need to know which tools they can use
• Check your legal/compliance situation: Some industries have specific AI guidelines

Step 2: Identify your most painful repetitive tasks

These are your workflow agent opportunities. If someone on your team is doing the same thing over and over, that's where you'll get immediate ROI.

Step 3: Test Automations in read-only mode

Test with Automations first. Let them find problems before they fix problems. Build trust incrementally.

The reality check

Agentic AI is moving incredibly fast. What was cutting-edge six months ago might be table stakes today. But here's what I've learned from actual implementations:

1. Perfect is the enemy of good — Start somewhere, even if it's small
2. Measure everything — You can't improve what you don't track
3. Your infrastructure matters more than your AI — Fix your APIs and data first
4. Change management is harder than the tech — Bring your team along for the ride

The bottom line

Businesses that figure out how to effectively use AI agents are going to run circles around those that don't. It's not about replacing people — it's about giving people superpowers.

The question isn't whether you should be looking at AI agents. The question is: Which type makes sense for your business right now?

Start with co-pilots. Get some wins. Build confidence. Then expand from there.

Because honestly? This is the most exciting time I've seen in tech in years. We're not just making things faster, we're fundamentally changing how work gets done.

What's your take on AI agents? Which type are you most interested in trying first? Reach out to me on LinkedIn. I'd love to hear what challenges you're thinking about solving.