Faraday agents: Why we changed the way we're talking about our core products

It's 4pm on a Monday and your boss has just dropped the news: lead conversion rates are tanking, even as volume has grown dramatically. You need a fix by Friday.

Your team is drowning in leads ranging from red-hot to ice-cold—but how do you sort through them?

Luckily you have a tool like Faraday in your belt, which springs into action, collecting and transforming all the relevant data, normalizing it for the situation, extracting representative training sets, engineering features automatically, building dozens of candidate models for the problem, tuning hyperparameters on the fly, and applying the right model to each lead at the right time to gauge its quality.

It’s not just a predictive AI tool, what you have is an AI agent. And it's the ultimate asset for engaging customers at scale, whether that's managing leads, messaging new offers, or personalizing an on-site experience.

Faraday has been building and operating AI agents for thousands of brands for more than 12 years. Now that popular terminology has caught up, we thought we'd share a bit more on the agentic phenomenon.

A little context

AI agents have actually been around for quite some time. In fact, the idea of machines acting independently to achieve goals dates back nearly a century to science fiction stories like Karel Capek's 1920 play Rossum's Universal Robots (which coined the term, ‘robots’) and Isaac Asimov’s 1940 classic short story, “Robbie,” But these robot dreams didn’t take long to transition from science fiction to reality.

Shakey the Robot, photo credit: computerhistory.org

Indeed, as early as the 1960s, real-world examples of rudimentary robots, such as the Shakey the robot were already starting to appear. Shakey was the first mobile general-purpose robot and could perceive its surroundings and reason with complex commands, breaking them down into basic chunks without needing explicit instructions for each step. Shakey was also capable of planning and navigation, and significantly influenced modern robotics and AI techniques. But in reality, Shakey was, by modern standards, a little shaky.

Shakey could solve problems and utilized symbolic reasoning like modern agentic AI but was massively limited by weak computational power and rigid programming frameworks. For these reasons it couldn’t compare to the complexity and reasoning ability of modern systems. But despite these limitations, Shakey and other early robots like it marked major steps forward in our ability to produce machines that could solve problems and make decisions independently. However, the progress was slow, the hardware was clunky, and Shakey and his friends all required an entire room of computers to perform simple tasks—so it wasn’t exactly the autonomous future Asimov had imagined.

But while Shakey was still breaking down commands, fiction had already imagined AI with greater capabilities. Take Robbie, the robotic nanny from Asimov’s story. Unlike Shakey, Robbie made independent decisions, learned from experience, and adapted his behavior. Though he doesn’t run on data pipelines or neural networks like modern AI agents, at his core, Robbie functions much like they do, by making decisions and fulfilling tasks independently (and, admittedly, with a cool metal body, but we’re getting there). And where Shakey fell short, today’s AI agents don’t face the same limitations, and the gap between fiction and reality continues to shrink.

Ironically, the main theme of this story was the misunderstanding of new and emerging technologies. With that in mind, let’s break down what AI agents really are, no fiction, just facts.

Why AI agents are everywhere now

Fast forward to 2025, and AI agents are no longer just science fiction. They’re all over the place: managing supply chains, handling customer service, writing emails, and yes, optimizing marketing campaigns. But why the sudden explosion in AI agent adoption?

A few reasons:

• Computational power: Advances in cloud computing and GPUs mean that what used to require a warehouse of machines now runs on your laptop.
• Better data availability: AI thrives on data, and today, there’s more of it than ever before.
• Automation hype: Everyone wants AI to do the boring stuff so they can focus on the big picture (or maybe take a longer lunch break).

How AI agents get jobs done

Modern AI agents operate by processing vast amounts of data, learning from patterns, and making decisions based on predefined goals. They typically rely on machine learning models, neural networks, and sophisticated algorithms to analyze information, predict outcomes, and take action.

Some agents function through reinforcement learning, continuously improving their performance based on feedback, while others follow rule-based systems with explicit instructions. Whether it’s a chatbot answering customer inquiries, an autonomous robot navigating an alien environment, or even a predictive model sorting through data, scoring it and then forwarding it to Shopify, AI agents work by combining perception, decision-making, and execution—often in real time—to accomplish tasks with minimal human intervention.

Common misconceptions about AI agents

But as uncle Ben almost said, with great hype comes great misunderstanding. Here are a few common misconceptions about AI agents:

• AI agents are sentient. Nope. They analyze data, follow rules, and optimize for objectives, but they don’t “think” or “feel” in any meaningful way (sorry Robbie, we still love you).
• AI replaces humans. Not exactly. AI agents assist and augment human decision-making rather than fully replacing it. The best use cases involve AI handling repetitive or overly complex tasks while humans focus on strategy.
• More data = better AI. This is sometimes true, but as the saying goes: 'Garbage in, garbage out.' More data doesn’t always mean better AI and quality matters more than quantity, especially when you’re looking for actionable business results.
• AI agents are just fancy chatbots. This one’s a little off too! While chatbots are one application, AI agents go far beyond simple conversations. They can analyze data, make decisions, and automate complex tasks in a variety of fields.

AI agents for the customer experience

Faraday is a flexible and fully composable platform that automatically processes and analyzes your data, delivering actionable insights to a range of targets. By using data and AI agents to make informed decisions and automate complex tasks, Faraday helps businesses streamline processes, optimize strategies, and drive growth. We tap into the true potential of AI agents to help businesses make smarter, faster, and more data-driven decisions, without needing a PhD in machine learning.

So how does Faraday work as an AI agent? Let's break it down:

1. Independent decision-making: AI agents act autonomously to achieve specific goals. Faraday independently analyzes consumer data, identifies patterns, and makes predictions without constant human oversight.
2. Goal-oriented actions: Agents perform tasks to accomplish defined objectives. Faraday's purpose is clear: to predict consumer behavior and optimize customer acquisition strategies to generate value. View some of our customer stories to see how much impact this can make.
3. Learning and adaptation: True agents improve over time. Faraday continuously refines its models based on feedback and new data, getting smarter with each interaction and iteration.
4. Environment interaction: AI agents observe and respond to their environment. Faraday processes vast amounts of customer data, market signals, and business metrics to inform its decisions.

In short, Faraday builds and operates AI agents for businesses that want data-driven growth, without the guesswork. Unlike Robbie the robot, it won't babysit your kids, but it will make sure your marketing efforts aren't stuck in the past (and if you insist, we do have a founder named Robbie).

Want to see how Faraday's AI agents can optimize your customer acquisition strategy? Let's talk.