AI Agents

Guide to understanding and implementing AI Agents

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Hey — It’s Hussein 👋

I’ve been wanting to write about today’s topic for quite some time. But have been wrapped up in some exciting new products at Arta that I can’t share just yet.

However, what I can share, are some particularly useful insights from my work and research. Let’s explore the evolving world of AI agents, which are the application of LLM-driven AIs we are all waiting for (and hopefully working on!).

Let’s talk about AI Agents! 🤖

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What are AI Agents?

The term "AI Agent" is thrown around quite a bit these days. While many are just using the keyword to ride the wave of interest in AI, I think it’s worth revisiting what an AI Agent really is, especially with how fast AI is evolving.

The technical and true definition of an AI Agent is a system capable of making its own decisions to get things done. Basically, think of it like a robot that can do tasks on its own to achieve specific goals.

But these days, I’d tweak that definition to cover not just automated robots but also AI-driven software that works with other pieces of software to get stuff done. It’s like having a team of mini AIs that chat with each other to complete a task.

This broader view is how I would describe the current use of the term AI Agents and the scope of what we are covering today.

Why this matters?

For three reasons.

First, it’s what’s happening. Startups are launching with AI Agents. Large companies are launching new products with AI Agents. Both of which are following the refined definition of AI Agents.

Do a quick search on Product Hunt for AI Agents and you’ll see what I mean.

AI Agents on Product Hunt

Second, understanding where we are and what the market is doing with the latest technology is important as you think about where your business, startup, or company is in relation to the latest technology.

Third, following the new definition also means it’s not as technically challenging as one might first think. As you roll out your AI Agents without the “fully autonomous” part, you can start to learn about the tech, get established, and be ready to implement the self-running features once they are within reach.

How to get started with AI Agents?

Here’s a quick 4-step guide to implement AI Agents. Use this as a guide and make modifications to fit your need and business:

1. Define Use Cases

The first step is knowing what you need the AI Agents for and what the benefits are of having them. Some ideas:

  • Improve your customer service chatbot

  • Automate a manual operational flow

  • Personalize marketing efforts to increase engagement

  • Onboard new employees or new customers

If you can’t find an obvious use case where AI Agents would be useful, do not force it. I’ve read countless stories of businesses investing a lot of time and resources to add AI to their existing products but don’t get any traction or usage on those features.

2. Take Small Steps

When building a new product, it’s always best to figure out what a possible MVP (minimum viable product) could be. When looking to get into AI Agents, the same concept applies.

Are there small steps (your MVP) you can take to get you started? Don’t overhaul your entire product or infrastructure just yet. Review your use cases and figure out what could be a small use case and step to start with.

3. Build, Buy or Rent

Before you spend your time building your own AI Agents from the ground up, do a search for your use case on Product Hunt or the GPT Hacks AI Tools Directory.

You may be able to find an existing tool that you can “rent” to get you started right away without a long implementation period or costs. If there are no existing solutions you can use, or if your use case is truly unique, then go through your typical buy vs. build analysis.

4. Iterate

Once your AI agents are up and running, continuously monitor their performance and gather feedback. Use this information to refine and optimize the agents for better performance. Remember, AI is not a set-it-and-forget-it solution. It evolves with your business.

Better yet, include a mechanism for feedback so you can have the agent learn and adjust behavior.

One Agent One Task

When creating AI Agents, I am a big proponent of one task per agent, just like I am a proponent of one task per ChatGPT prompt.

For example, say you have an AI Agent that onboards new salespeople onto your team. The onboarding process entails training salespeople on your brand, voice, mission, products, pricing, handling objections, common questions, etc.

Instead of having a single onboarding agent, you would create one for each of the items above so that each individual agent can be focused, fine-tuned, have its own safety guardrails, and control possible hallucinations.

All of these agents should be created so that they are aware of each other’s context and conversation and can run one after the other to complete the onboarding process of a new sales recruit.

Benefits of One Agent One Task

An agent per task creates a modular system. This makes it easier to maintain, modify, monitor, and iterate on agents without having to worry about impacting your entire system.

It’s easier to scale each individual component as needed. You can start with a small allocation of resources and grow each agent individually.

You can modify an agent without having to modify your entire system.

User Experience
You can control the user experience better by managing each part of the interaction individually, which will translate into a better user experience for your end user.

Future Proofing
With modular agents, it’s easier to replace individual components of your system and make improvements where needed or upgrades as LLMs continue to advance so you can always be ready for what’s coming up ahead. For example, GPT-5 is rumored to be released this summer. Depending on the nature of GPT-5, existing GPT-enabled products could start to feel obsolete if not upgraded.

AI Agents are the latest frontier in business innovation. This is the industrial revolution of our times. Companies born in this period have an advantage in that they are starting with AI in mind.

Not to be all gloom and doom on you, but paying attention to what’s happening, what your competitors are doing, and what new startups are launching with AI at the center of what they are doing is critical. You definitely do not want to be left behind.

Hope this quick post helps kick-start your thinking about AI Agents.

Until next time!
Hussein ✌️

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