Daniel Dines

Because I think very few people understand what are the use cases where RPA is really used and valuable, and why actually agentic doesn't work for those use cases. I can elaborate. Can we unpack them? So the sweet spot for RPA is to automate tasks that span multiple business systems and are of medium to high complexity. So they can span multiple steps. Usually it can be even 100, 200 of steps.

Because I think very few people understand what are the use cases where RPA is really used and valuable, and why actually agentic doesn't work for those use cases. I can elaborate. Can we unpack them? So the sweet spot for RPA is to automate tasks that span multiple business systems and are of medium to high complexity. So they can span multiple steps. Usually it can be even 100, 200 of steps.

Because I think very few people understand what are the use cases where RPA is really used and valuable, and why actually agentic doesn't work for those use cases. I can elaborate. Can we unpack them? So the sweet spot for RPA is to automate tasks that span multiple business systems and are of medium to high complexity. So they can span multiple steps. Usually it can be even 100, 200 of steps.

But the keys, they are rule-based. The input is structured and then the steps that you go are rule-based. But they, in a way, they capture the company knowledge. within the rules. Even simple things like if the VAT starts with this particular two numbers, then you have to take this particular flow. But you capture it in rules. This is very important. But these automations are very reliable.

But the keys, they are rule-based. The input is structured and then the steps that you go are rule-based. But they, in a way, they capture the company knowledge. within the rules. Even simple things like if the VAT starts with this particular two numbers, then you have to take this particular flow. But you capture it in rules. This is very important. But these automations are very reliable.

But the keys, they are rule-based. The input is structured and then the steps that you go are rule-based. But they, in a way, they capture the company knowledge. within the rules. Even simple things like if the VAT starts with this particular two numbers, then you have to take this particular flow. But you capture it in rules. This is very important. But these automations are very reliable.

They simply work until the underlying system changes. Now, when it comes to agentic, LLMs are actually not good at following repetitive steps. You are not going to have LLMs multiply to numbers. No, you are going to follow an algorithm and you are going to use creep language or you will program it, right? This is kind of the same with automations.

They simply work until the underlying system changes. Now, when it comes to agentic, LLMs are actually not good at following repetitive steps. You are not going to have LLMs multiply to numbers. No, you are going to follow an algorithm and you are going to use creep language or you will program it, right? This is kind of the same with automations.

They simply work until the underlying system changes. Now, when it comes to agentic, LLMs are actually not good at following repetitive steps. You are not going to have LLMs multiply to numbers. No, you are going to follow an algorithm and you are going to use creep language or you will program it, right? This is kind of the same with automations.

LLMs work relatively well when we are dealing with unstructured parts in a business process. It's sometimes the enterprise knowledge, it's difficult to express in rules. You can eventually. It's very difficult. There is a lot of tribal knowledge on the top of the public knowledge that human user is supposed to have.

LLMs work relatively well when we are dealing with unstructured parts in a business process. It's sometimes the enterprise knowledge, it's difficult to express in rules. You can eventually. It's very difficult. There is a lot of tribal knowledge on the top of the public knowledge that human user is supposed to have.

LLMs work relatively well when we are dealing with unstructured parts in a business process. It's sometimes the enterprise knowledge, it's difficult to express in rules. You can eventually. It's very difficult. There is a lot of tribal knowledge on the top of the public knowledge that human user is supposed to have.

So when you cannot express in rules, then you can build an agent that will mimic what the user will do, but with the intent that will reduce the human input on that part of the process. You cannot really eliminate a task using agentic. Because in a way, agentic AI, it's about delivering something autonomously.

So when you cannot express in rules, then you can build an agent that will mimic what the user will do, but with the intent that will reduce the human input on that part of the process. You cannot really eliminate a task using agentic. Because in a way, agentic AI, it's about delivering something autonomously.

So when you cannot express in rules, then you can build an agent that will mimic what the user will do, but with the intent that will reduce the human input on that part of the process. You cannot really eliminate a task using agentic. Because in a way, agentic AI, it's about delivering something autonomously.

Well, this is a great question. Thank you. And I think this is the essence why we have it right at the agentic AI table. The answer, in short, is a rule-based and non-deterministic part actually sit within the context of a business process. It's like a long, long business process like order to cash or procure to pay. You'll have non-deterministic parts and you'll have deterministic parts.

Well, this is a great question. Thank you. And I think this is the essence why we have it right at the agentic AI table. The answer, in short, is a rule-based and non-deterministic part actually sit within the context of a business process. It's like a long, long business process like order to cash or procure to pay. You'll have non-deterministic parts and you'll have deterministic parts.

Well, this is a great question. Thank you. And I think this is the essence why we have it right at the agentic AI table. The answer, in short, is a rule-based and non-deterministic part actually sit within the context of a business process. It's like a long, long business process like order to cash or procure to pay. You'll have non-deterministic parts and you'll have deterministic parts.

It makes really sense to have the same technology and put them in the same framework. This is why agentic orchestration is so important. We have the technology that connects all the parts of the process and we have the technology to automate those steps in the process. Think about it as a metaphor. Robots are more like low-skilled employees, while agents are high-skilled employees.

It makes really sense to have the same technology and put them in the same framework. This is why agentic orchestration is so important. We have the technology that connects all the parts of the process and we have the technology to automate those steps in the process. Think about it as a metaphor. Robots are more like low-skilled employees, while agents are high-skilled employees.