Knowledge-based agents in AI are the driving forces behind many of the platforms, programs, and initiatives that streamline all areas of an organization through the implementation of artificial intelligence.
Used in conjunction with some of the most powerful LLMs in the world, such as OpenAI GPT-4o, Anthropic Claude, and Google Gemini, you’ll find AI agents behind the artificial intelligence tools you already use. Other examples are AI-powered customer service agents, fraud detection agents, and IT support agents.
An agent AI assistant is designed to make your life easier and streamline complex and complicated tasks that can take humans hours, days, or even weeks to accomplish.
However, understanding how AI agents work and how to develop the skill for using AI agents, is a complicated venture in its own, which requires time and a team of experts to guide the way.
At C1M, we are revolutionizing how organizations across all industries integrate artificial intelligence into their operations to create new and perpetual opportunities for productivity and growth.
The best way to get a leading edge on understanding and utilizing AI agents is to start a conversation with C1M. However, for now, take a closer look at the mechanics of AI agents, from how they store data to how they utilize experiences to become even more powerful and productive.
What is an AI agent?
An AI agent is a technology, software, and/or application with a defined knowledge base for a specified function or task. This includes the ability to initiate problem-solving actions and the intelligence to select the appropriate action based on the input or context.
AI agents are designed to be self-sufficient when understanding varying inputs or requests. They can gather the right data to form a response and make decisions in order to perform or initiate the correct action.
Sometimes, a system of AI agents, known as Agentic AI, will work together to achieve a goal, which creates a more complex artificial intelligence “brain” for more multi-faceted or complex projects and tasks.
For the purposes of this primer on AI agent skills and operations, however, we’ll focus on AI agents themselves and not the broader systems that they may be a part of. This will help you understand how they work and how they create a path from problem to solution.
How AI Agents Shape the Future of Technology and Business
AI agents are becoming instrumental in many industries that deal with large sets of data. For this reason, they eliminate extensive research and repetitive tasks that no longer need to be a routine part of an organization’s day-to-day operations.
For example, in the healthcare industry, AI agents can help with scheduling appointments and processing insurance and payment data. Additionally, they can even examine a patient’s medical information to make treatment recommendations, saving hours of time for medical personnel.
In the financial realm, AI agents can help assess an applicant’s risk for a new loan or line of credit, and answer account and banking questions. It can even provide financial, or investment advice based on market trends and historical data.
Simply put, using AI agents can serve multiple roles and adapt to various goals without limitation. Instead, businesses can adapt to fill gaps in any industry, using vast amounts of data as a foundation for future actions.
How AI agents work
Just like humans, AI agents accomplish these multi-pronged tasks through memory systems that can process, store, and retrieve relevant data based on a given situation or query. In this vein, an AI agent’s “memory” is typically divided into two categories – short-term and long-term memory – each serving different roles in the AI agent’s overall cognitive framework.
Knowledge representation and long-term memory
The long-term memory aspect of AI agents is where the bulk of the essential backbone data is stored. Long-term memory stores imperative information for extended periods of time, from days to years. This allows AI agents to remember and build on past experiences for future use. This is similar to how humans store information that they use throughout their lifetime, such as how to ride a bike or add and subtract.
Long-term memory basically allows AI agents to routinely access essential information and knowledge, adapt or edit this knowledge as needed, and learn from past experiences for future actions.
This facilitates deeper reasoning and better functionality as AI agents build a growing database that helps them make decisions or solve problems.
Methods of knowledge representation
Knowledge representation refers to the process of structuring and encoding vast data about the world into a format that AI agents or computer systems can understand.
In AI agents, the primary methods of knowledge representation include:
- Logical Representation (Predicate Logic): Using formal logic to express knowledge through statements and rules, enables AI to perform reasoning and interference.
- Semantic Networks: Represents knowledge in a graphical structure of nodes (concepts) and edges (relationships), making it easier to understand connections between entities.
- Frame Representation: Organizes knowledge into structured frameworks (frames) that store attributes and values, like object-oriented programming.
- Production Rules: Utilizes “if-then” rules to guide AI decision-making, commonly used in expert systems.
- Conceptual Dependency: Represents knowledge through a standardized framework of meaning, reducing ambiguity and enabling a better understanding of natural language.
Each method defines how an AI system stores and structures information for future use, influencing the ability to process, infer, and apply knowledge effectively.
Short-term memory and communication reasoning
Short-term memory in AI agents holds information for a brief period, ranging from a few seconds to a few minutes. It retains data only as long as it remains relevant and useful for the current action or situation.
Furthermore, short-term memory can be considered the immediate workspace for AI agents. It’s the area where AI agents can save and access information that directly relates to an ongoing task, communication, or project.
For example, chatbots and virtual assistants have short-term memory that keeps track of the current conversation. Therefore, pertinent info about a user’s responses and replies is stored to complete a customer service or other task.
However, once this interaction is over, the AI Agent doesn’t need to remember this highly specific data that is only helpful and useful in this one conversation, (like Brad from New York needs help restarting his modem.)
Mechanisms of learning in AI agents
AI agents use multiple and corresponding mechanisms to gather and acquire new knowledge, which includes the following processes:
- Supervised learning – supervised learning uses labeled data to train AI agents and models to predict outcomes based on known patterns. In supervised learning, the correct action is already known for every data point.
- Unsupervised learning – Unsupervised learning analyzes and uncovers patterns with unlabeled data where there are no pre-defined categories or labels. In unsupervised learning, AI agents or models find hidden structures and relationships without any human instruction or guidance, which allows the algorithms to learn by themselves.
- Reinforcement learning – Reinforcement learning is learning through trial and error. In such, the AI agent or model interacts with the environment, receives feedback on its actions through rewards or penalties, and then revises its response or behavior as needed. Over time, the AI agent garners a deep knowledge on appropriate actions based on this history of positive and negative responses.
- Continuous Learning – Once a knowledge base has been established, AI agents adapt and modify their behavior in real time as environmental or situational conditions change, allowing them to react to new information or new interactions, as well as evolving data inputs.
- Challenges to continuous learning – Keep in mind that there can be potential challenges in continuous learning, including catastrophic forgetting, which occurs when an AI agent or system learns new information and rapidly and drastically forgets old info that is still essential for the AI agent to function correctly.
The future of AI agent development and how your business can benefit
AI agents are already transforming industries, but this is just the beginning. As technology evolves, these intelligent systems will gain greater autonomy, hyper-personalization, seamless integration, and enhanced collaboration – making them more powerful and adaptable than ever before.
Imagine AI agents that anticipate your business needs, automate complex workflows, and work alongside your team effortlessly, boosting efficiency and innovation.
Also, concerns like bias and transparency are also being actively addressed, ensuring AI becomes more ethical and reliable. Now is the time to explore what AI agents can do for your business.
Reach out to C1M today and discover how AI can transform the future of your business.
