If you’ve already started your intelligent automation journey, chances are you’re aware of robotic process automation (RPA) and artificial intelligence (AI).
But more recently, the concept of agentic AI has emerged, leaving many wondering, “Is this just another tech trend, or is there something truly different here?”
The truth is that RPA, traditional AI, and agentic AI are similar but different in the impact they can make.
Let’s break it down together, using a seemingly random metaphor: dogs. Yes, dogs.
What’s the Difference Between RPA, Traditional AI, and Agentic AI?
- Think of RPA as a puppy fresh out of obedience school: A puppy that just completed obedience school can complete simple beginner’s tasks, like sitting when you say “sit”. Still, it might get confused when you say, “roll over,” just like RPA can take on rule-based tasks, but can’t pivot when process exceptions come up.
- Think of traditional AI as a dog trained to fetch specific toys: This dog can fetch a red ball from a pile of toys, but it might bring a sock instead of the ball occasionally, just as traditional AI excels at using data to work smarter but may hallucinate with a reasonable yet incorrect answer.
- Think of agentic AI as a service dog: A service dog understands its owner’s unique needs, adapts to new situations, and problem-solves by making decisions like stopping before a crosswalk when a car runs a red light or rerouting through a side street when the main road is closed. This is similar to how agentic AI has autonomy and the ability to handle complex workflows and learn from experience.
Now that we’ve set the scene, let’s take a closer look:
What is RPA?
RPA bots are fantastic at following rules, automating repetitive tasks, and mimicking human actions with minimal intervention. But here’s the catch: RPA is only as smart as the rules you give it.
Much like a dog fresh out of obedience school might excel at sitting upon hearing “sit” but fail to respond to a new command like “roll over”, when something unexpected happens (i.e. an exception, a new format, a change in process), RPA needs a human to step in to help it know what to do next. It’s not built to learn, adapt, or make decisions on its own.
What is Traditional AI?
Traditional AI executes pre-defined commands with precision, although like a well-trained dog that occasionally may fetch the wrong item, both traditional and generative AI are generally limited by their training and programmed scope. This means they won’t typically generate content or respond to situations outside those boundaries
What is Agentic AI?
Agentic AI asks, “What are you trying to achieve?” and figures out the best way to get there. It’s proactive, anticipating the needs and taking initiative rather than just reacting to commands or specific situations. Even more, it can handle complex workflows, adapt to new data, and make decisions in real time, just as a service dog can learn from experience, take on goals, and problem-solve when the unexpected occurs.
Agentic AI Use Cases
- Hiring: Agentic AI screens candidates across platforms, adapting to changing criteria and handling exceptions without manual intervention.
- Healthcare: Agentic AI in healthcare navigates patient data, insurance rules, and compliance needs, learning and improving as it goes.
- Resource Planning: Agentic AI allocates staff across platforms like Salesforce and Monday.com, helping you respond to shifting priorities and business needs.
Choosing the Right Dog for the Job
Ready to explore RPA, traditional AI, or Agentic AI further? Drop your questions or comments below, we’re here to be your most trusted partner on your intelligent automation journey.
