Most people’s knowledge of AI stops at Chat GPT. The “GPT” part of which stands for “Generative Pretrained Transformer”. Didn’t know that? Good, let’s break it down:
Generative: The AI model's ability to generate new content rather than relying solely on pre-existing data
Pretrained: The system has been trained on a large dataset to learn patterns, structures, and relationships in language.
Transformer: The underlying neural network infrastructure that allows the model to process and generate text quickly and efficiently.
To say the absolute least, OpenAI set a whole new bar and got an absurd head start once they introduced Chat GPT, which has placed OpenAI among the top 20 most visited websites in the world during the 6-month span the platform’s been out. But this success alone won’t sustain them forever, especially since it’s influenced rival companies and researchers who are working tirelessly to catch up by developing their own products.
The Other AI Systems on The Rise
When you give a tool to the public, it’s a certainty that they will do whatever they can to make it as beneficial to them as possible. This mentality has led to the creation of Computer Agents which essentially act as a computer’s personal assistant. In case you’ve never heard of a computer agent, we’ll break the concept down.
The entire premise for computer agents is to do one thing: automate the tasks required to reach goals. This advanced AI infrastructure goes beyond basic copywriting and idea generation. It’s more so tailored to cater to the needs of enterprise businesses.
There are 3 key types of agents: Deliberative, Hybrid, and Reactive. While all are unique in their processes and abilities, they share the common goal which is to complete tasks as quickly and efficiently as possible. But not in the same way that Chat GPT responds to your prompts, computer agents use APIs to work with applications and services to perform tasks on your behalf.
Here is a closer look at each one:
Deliberative Agents: Deliberative agents use advanced planning and decision-making algorithms to perform complex tasks. Ever heard someone say “Use your brain”? Well, these systems do just that. They take their databases of past experiences and use them to analyze problems and make informed choices based on the situation they’re presented with. While they may not have a brain, they generate a plan of action to achieve the intended goal. These helpers are great for tasks that require foresight and optimization.
Reactive Agents: These tools are the complete opposite of what we know about deliberative agents. As the name implies, the system responds to its environment, except without any clear understanding of its purpose. The agent's behaviour and the rules they follow are usually pre-determined and don’t depend on reasoning. It has no learning capacity and instead relies on inputs to trigger pre-programmed responses. For tasks that need quick responses to a predictive environment (like an assembly line), this tool can be a game changer.
Hybrid Agents: With hybrid agents, you get the best of both worlds. These are your self-driving cars, Siri on your iPhone, and even the robots used for manufacturing. These systems combine the strengths of both deliberative and reactive agents to form one process. They can reason and plan like deliberative agents but also react quickly to dynamic environments just like reactive agents. They’re that perfect balance between preplanned actions and on-the-fly adaptation, which makes them great for tasks that require flexibility.
You might be wondering then, “What’s the point of deliberative and reactive when there’s a hybrid?” Each agent serves a unique purpose, and there are going to be times when a hybrid isn’t as suitable a choice. Here are 4 reasons why:
Complexity: While hybrid agents can handle a wide variety of tasks with the conjoined capabilities of deliberative and reactive agents, the integration does add complexity to the system. In some cases, the simplicity and efficiency of reactive or deliberative agents are simply better for the task which as everyone in IT knows - tasks never need to be more complex.
Application Requirements: Different applications have different requirements. Some tasks prioritize speed and real-time responsiveness, while others require careful planning and decision-making. Having that variety of agent types allows programmers to select the most suitable one based on the application’s specific needs.
Specialization: As we said before, reactive agents are best for applications where immediate reactions are crucial, such as emergency systems— especially cybersecurity. Deliberative agents, on the other hand, will cover the big-picture tasks. It’s just like a development team, members have their unique roles and abilities but all contribute to an end goal.
Resource Constraints: Depending on the available computational resources, it may be more practical to use simple reactive or deliberative agents as opposed to a complex hybrid agent. This consideration becomes crucial when dealing with limited processing power or memory constraints in resource-constrained environments.
How it Helps Businesses
Artificial Intelligence, Machine Learning, Autonomous Agents, you name it— it’s cool, but how can people use it to help their business? These systems integrate to fit the custom requirements of your product or service. As the trends suggest, if a company is not trying to automate as much of their workflows as possible, they’re putting themselves at a major competitive disadvantage.
Why would a company not want to move toward automation? Among many reasons, here are the most common:
A simple lack of awareness/underestimating new technology
Resistance to change
Security concerns
The complexity of the process
The investment
Volatile business environment
Most of these concerns are to be expected since the process of digital transformation— especially with AI involved— can be a significant undertaking with no clear guarantees. However, this is what it takes for companies to survive in times of change.
If a business leverages AI, ML, and these autonomous agents with a clear strategy defined and aligns them with their unique goals, they can embrace automation and adapt to the evolving landscape. What we’re getting at here is that leveraging the capabilities of systems like deliberative, hybrid, and reactive agents, such as a GPT, can drive efficiency and innovation, especially in the long term.
Frameworks Behind Computer Agents
Integrating computer agents into a business’s internal system requires a structured approach. Because of this, there are several framework options:
Behavior Tree (BT) Framework: The Behavior Tree framework is used for designing reactive agents. It structures an agent's behaviour into a hierarchy of tasks and conditions. The agent then evaluates these tasks and conditions in real-time and makes decisions based on its immediate environment and the rules you’ve given it.
Belief-Desire-Intention (BDI) Model: The BDI model is a very popular framework used in designing deliberative agents. It focuses largely on the agent's beliefs about the world, its desires or goals, and its intentions to achieve those goals. This model puts a big emphasis on reasoning, decision-making, and planning capabilities.
Sense-Plan-Act (SPA) Architecture: SPA is a popular hybrid architecture used to create highly intelligent agents. Each step is part of a cycle that is sensing the environment, planning its actions, and executing those actions. This is considered the foundation of intelligence that every robot needs.
The architecture enables agents to combine reactive behaviours with some higher-level deliberative processes. This makes responsiveness happen in real-time through the reactive component, while also providing the ability to reason, plan, and then make strategic decisions using the deliberative component.
Multi-Agent Systems (MAS): Simply put, MAS frameworks involve multiple agents working together to achieve common goals. Using a MAS framework, agents can coordinate their actions and communicate to solve problems that would be impractical for a single agent to handle alone.
The Takeaway
With a solid infrastructure in place, businesses can get the most out of integrating computer agents into their workflows. Whether it's leveraging reactive agents for quick responses, deliberative agents for strategic decision-making, or hybrid agents for the best of both, businesses first need to think about how their goals align with their technology selection.
Written By Ben Brown
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ISU Corp is an award-winning software development company, with over 17 years of experience in multiple industries, providing cost-effective custom software development, technology management, and IT outsourcing.
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