AI Agents - Buzzword or Accelerator?
If you’re like me, you’ve been flooded with headlines about AI agents, agentic AI, AI systems with agency, and more. And like me, you may be wondering what aspects of AI agents represent real technology advancements that are appropriate for enterprise deployment, and which are just gimmicks and marketing hype.
The good news is that AI agents do represent a significant step forward in the evolution of the ability of generative AI (GenAI) to solve practical problems. Think of an AI agent as a small software program that was written to carry out a single task, but imagine that, at the heart of this program, there is an intelligent orchestrator that can adapt to variations in how the task is done.
This is very different from a traditional software application in two important ways. First, the agent is able to communicate easily with humans, in natural language or through more formal data structures, depending on the situation. So, an agent could take a spoken instruction from a human, ask a clarifying question, do some processing, and then respond with either a spoken response, or a very structured response, such as an API call, a document, or a form. A few years ago, that alone would have been amazing, but in the current era of GenAI, “talking” to a computer is old hat.
This is where the second critical difference between agents and traditional applications comes in: The agent has the ability to plan and adapt to changing conditions. In the past, even the simplest task required detailed advance planning to create a flow chart of (hopefully) all the variations in how the inputs and responses might change on the path to completing the task. Now, the GenAI at the heart of the agent can break a complex problem into small parts, tackle those small parts, and adapt the plan as things change.
Let’s illustrate this with an example. To make myself a cup of coffee this morning, I had to turn on my espresso machine, grind the beans, get an appropriate cup, and pull the shot. But what if I wanted different beans, or if I had to get the cup from the dishwasher rather than the cupboard? What if the espresso machine needed more water? To program all of the potential contingencies in the flow chart ahead of time for even a simple task can be difficult or impossible. If the GenAI at the heart of my AI agent can automatically adapt to these variations, and even ask for clarification (“Did you want the Arabica beans from Colombia or the Robusta Beans from Vietnam?”), then the path to a successful assistant is much more clear.
You may ask, “Can’t a regular GenAI interface do all this for me already? Why build an agent?” At Orchestrated Intelligence, we have developed user experience agents that can answer complex questions about supply chain cost-performance tradeoffs. During this process, we have identified additional benefits of agents that I will outline in my next article.
There are many companies offering AI agents to handle a variety of tasks, from simple text summarization all the way to making restaurant reservations and other “personal assistant” activities. The quality and utility of each of these services vary widely, so I recommend you try before you buy. I’ve seen some “time saving” agents actually make executives more busy by constantly asking questions and sending alerts.
The bottom line, though, is that AI agents are a powerful new manifestation of GenAI that give us better control over how we get things done, with the security controls and performance that we expect from enterprise applications.
If you’re like me, you’ve been flooded with headlines about AI agents, agentic AI, AI systems with agency, and more. And like me, you may be wondering what aspects of AI agents represent real technology advancements that are appropriate for enterprise deployment, and which are just gimmicks and marketing hype.
The good news is that AI agents do represent a significant step forward in the evolution of the ability of generative AI (GenAI) to solve practical problems. Think of an AI agent as a small software program that was written to carry out a single task, but imagine that, at the heart of this program, there is an intelligent orchestrator that can adapt to variations in how the task is done.
This is very different from a traditional software application in two important ways. First, the agent is able to communicate easily with humans, in natural language or through more formal data structures, depending on the situation. So, an agent could take a spoken instruction from a human, ask a clarifying question, do some processing, and then respond with either a spoken response, or a very structured response, such as an API call, a document, or a form. A few years ago, that alone would have been amazing, but in the current era of GenAI, “talking” to a computer is old hat.
This is where the second critical difference between agents and traditional applications comes in: The agent has the ability to plan and adapt to changing conditions. In the past, even the simplest task required detailed advance planning to create a flow chart of (hopefully) all the variations in how the inputs and responses might change on the path to completing the task. Now, the GenAI at the heart of the agent can break a complex problem into small parts, tackle those small parts, and adapt the plan as things change.
Let’s illustrate this with an example. To make myself a cup of coffee this morning, I had to turn on my espresso machine, grind the beans, get an appropriate cup, and pull the shot. But what if I wanted different beans, or if I had to get the cup from the dishwasher rather than the cupboard? What if the espresso machine needed more water? To program all of the potential contingencies in the flow chart ahead of time for even a simple task can be difficult or impossible. If the GenAI at the heart of my AI agent can automatically adapt to these variations, and even ask for clarification (“Did you want the Arabica beans from Colombia or the Robusta Beans from Vietnam?”), then the path to a successful assistant is much more clear.
You may ask, “Can’t a regular GenAI interface do all this for me already? Why build an agent?” At Orchestrated Intelligence, we have developed user experience agents that can answer complex questions about supply chain cost-performance tradeoffs. During this process, we have identified additional benefits of agents that I will outline in my next article.
There are many companies offering AI agents to handle a variety of tasks, from simple text summarization all the way to making restaurant reservations and other “personal assistant” activities. The quality and utility of each of these services vary widely, so I recommend you try before you buy. I’ve seen some “time saving” agents actually make executives more busy by constantly asking questions and sending alerts.
The bottom line, though, is that AI agents are a powerful new manifestation of GenAI that give us better control over how we get things done, with the security controls and performance that we expect from enterprise applications.
