Introduction
In customer support, one of the key hurdles in providing fast and effective responses is the product information and associated policies by a human agent. Offering a satisfactory resolution to the customer within the outlined company policy can be a challenging task when dealing with complex customer queries under tight deadlines.
Though most companies offer useful knowledge resources to inform and educate agents, vast informational sources can be overwhelming when you are in a high-stakes situation. Among the many AI tools are knowledge-based agents, which play a critical role in analyzing vast resources, performing logical reasoning, and providing inferences from knowledge bases for major decision-making.
In this article, we explore knowledge-based agents in AI to understand their components, types, working, advantages, and common use cases.
What are knowledge-based agents in AI?
Knowledge-based agents in AI are specialized AI systems providing logical reasoning and actionable insights by analyzing structured knowledge repositories. These tools can react in complex situations by accessing stored information and using reasoning that is similar to a human.
Such AI systems find applications in workflows that require quick and efficient decision-making by processing knowledge, logical rules, and data for an effective solution. Customer support, finance, and legal systems are some of the areas that can benefit from the use of knowledge-based AI agents.
Data indicate that organizations using Gen AI-enabled customer service agents saw a 14% increase in issue resolution per hour. This is a clear indication of the productivity that AI agents bring to the workplace with their application.
However, to ensure their complete application, it is necessary to understand their architectural framework. In the next section, we take you to the architectural components of knowledge-based agents in AI.
What are the components of knowledge-based agents in AI?
The knowledge-based agents in AI comprise three main components:
The knowledge repository: This is the centralized location where the knowledge-based agents in AI store all the structured information and rules that enable them to perform logical reasoning.
The inference engine: It is the reasoning component that draws facts and information from the knowledge base to form conclusions or actionable insights.
- Sensors & action interface:The sensor allows the AI agent to perceive and understand its environment, while the action component provides solutions based on the environmental input. Some AI agents possess an optional machine-learning capability that allows them to learn continuously and update themselves through various experiences.
What are the types of knowledge-based agents in AI?
To precisely understand the importance of knowledge-based agents in AI, it is important to understand other AI agent types. Here is a brief detail on each type of knowledge-based agent in AI to present a comparative view.
1. Simple reflex agents
These are the simplest forms of AI agents that follow a set of predefined rules to analyze and perform actions based on specific inputs. Such AI agents do not possess the capability of logical reasoning. Hence, they are limited to performing simple repetitive tasks. Since these agents are adaptable and follow preset rules, they fail to handle complex scenarios.
2. Model-based reflex agents
These agents use internal models to build representations of the environment, and by using data, they can predict outcomes. Such AI agents offer a good representation of AI systems that are efficient at contextual understanding.
3. Sensors & action interface
The sensor allows the AI agent to perceive and understand its environment, while the action component provides solutions based on the environmental input.
Some AI agents possess an optional machine-learning capability that allows them to learn continuously and update themselves through various experiences.
4. Goal-based agents
These AI agents achieve specific goals by focusing on attending activities that help them achieve their objectives. Such types of AI agents often assist in customer support where time-bound responses are essential.
5. Utility-based agents
Utility-based agents are a step ahead of goal-based agents. They do not just focus on achieving their objectives but deliberate on multiple factors that will enable them to reach maximum value for their goal.
6. Learning agents
These agents are continuous learners by absorbing new information and adapting to new processes that give them the best results. They are highly efficient in customer support as constantly changing dynamic environments require them to adapt and improvise to serve users well.
7. Knowledge-based agents in AI
Knowledge-based systems in AI combine logical reasoning, information processing, adaptability, and decision-making capability based on real-time inputs. Such agent types are useful in situations where a vast amount of knowledge processing is required combined with logical inference for effective results.
How do knowledge-based agents in AI work?
A knowledge-based agent in AI goes through the following steps to perform its function;
Receive input or query : The sensors in knowledge-based agents in AI work to gather information or input from their environment. It can be in the form of a query, a command from another integrated tool, or an auto-trigger action.
Match input to defined logic : The system begins interpreting the input by using natural language processing. It matches the input statement or command with the logical rule to comprehend what needs to be done.
Infer solutions : In the next step, the knowledge-based system in AI accesses all the stored information or data to combine it with logical reasoning to find the best solution.
Deliver solution : Lastly, the agent delivers an output, in the form of actionable insights. Based on the interface, the output can be in a textual format or action that triggers automated reactions, etc.
Advantages of knowledge-based agents in AI?
Here are some key advantages of knowledge-based agents in AI.
Consistent results : Since machines lack human emotions, you can expect knowledge-based agents in AI to provide results with consistent logical reasoning without any bias.
Improved decision-making : These agents apply logical reasoning and rules from available knowledge, which makes them reliable in highly complex workflows. Additionally, with their high speed, they can analyze vast resources, which allows them to deliver outputs almost instantly.
Cost optimization : While knowledge-based agents in AI take care of repetitive and complex scenarios, human agents are free to focus on tasks that require critical thinking and interpersonal skills. In such cases, all resources are used optimally.
Transparency in decision-making : Since knowledge-based agents in AI process inputs through logical reasoning or set rules, it is easier to track back and confirm the reasons for reaching a specific output.
Machine learning for upgrades : Some advanced models use machine learning to acquire new learnings from their experiences and feedback.
- Handle complex domains : Since knowledge-based agents in AI use structured knowledge and logic, they are useful in industries and processes that often involve complex scenarios such as medicine, law, or finance.
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Wrapping up
In a world where decisions have to be made swiftly and still need to be accurate, knowledge-based agents are the foundation of smart AI systems. They employ structured data, logic, and inference to allow machines to think more like humans—they do so rationally, transparently, and adaptive.
Unlike black-box models, knowledge-based agents give explainable reasoning. Thus, the decisions can be trusted, checked, and improved. Whether it is diagnosing complicated medical conditions, directing legal decisions, or enhancing customer support, their part in domain-specific, high-stakes decision-making cannot be substituted.
As businesses and technologies advance, the combination of knowledge-based agents with other AI techniques such as machine learning and natural language processing will only make them more powerful. They are certainly not just the tools of automation, but also the ones that allow clarity, consistency, and smarter strategies.
Frequently Asked Questions
Knowledge-based agents in AI are intelligent systems that use structured data and set logical rules to provide actionable insights. They apply logical reasoning to resolve complex queries similar to human reasoning. This enables them to work through complex problems that require consistent application of intelligence and problem-solving.
There are five key agents in AI, each working on its specific rules to achieve a desired objective:
Simple reflex agents
Model-based reflex agents
Goal-based agents
Utility-based agents
- Learning-based agents
These are advanced AI systems designed to interact with their environment, acquire knowledge through these interactions, and process this knowledge to improve itself with time.
About the Author
Nisha Sneha
Nisha Sneha is a passionate content writer with 5 years of experience creating impactful content for SAAS products, new-age technologies, and software applications. Currently, she is contributing to Kenyt.AI by crafting engaging content for its readers. Creating captivating content that provides accurate information about the latest advancements in science and technology has been at the core of her creativity.
In addition to writing, she enjoys gardening, reading, and swimming as hobbies.
