The Complete Guide to Knowledge-Based Agents in AI

Quick Overview:

• A knowledge based agent in AI is a decision-making system that uses stored facts and logical rules, rather than just immediate input, to reason and select actions.
• Core Components: Every agent operates with a Knowledge Base (storing facts and rules) and an Inference Engine (performing logical reasoning).
• Working Cycle: The agent follows a loop of Perception (collecting input), Knowledge Updating, Reasoning (matching rules to facts), and Action Execution.
• Applications: This type of agent is critical in regulated fields like Healthcare (diagnosis support), Cybersecurity (threat flagging), and advanced Customer Support.

To master the design and deployment of these specialized systems, we will see these concepts in detail in this guide, along with expert-curated Agentic AI courses that can advance your career.

Overview of Knowledge Based Agents in AI

A knowledge based agent in AI uses stored facts and logical rules to decide actions. It doesn’t rely only on current inputs. It checks past data, reasons through rules, and selects actions that match the situation.

You see these agents in expert systems, virtual assistants, diagnosis tools, and automated decision platforms.

Key points

• It works with two core units: a knowledge base and a reasoning system.
• It can explain why it made a decision.
• It performs better in domains that need structured reasoning.
• It stands apart from basic reactive agents because it uses past knowledge instead of simple triggers.

Also Read: What Is Agentic AI? The Simple Guide to Self-Driving Software

Comparison Table: Types of AI Agents

Agent Type	How It Works	Example Use Cases
Simple Reflex Agent	Acts only on current input using condition–action rules	Thermostats, simple bots
Model-Based Agent	Uses internal state plus current input	Navigation, monitoring
Goal-Based Agent	Chooses actions that move it toward a goal	Planning, route finding
Utility-Based Agent	Selects the most beneficial action using utility scores	Recommenders, resource planning
Knowledge Based Agent in AI	Uses stored facts and rules for reasoning	Diagnosis systems, expert systems

Also Read: Types of Agents in AI: A Complete Guide to How Intelligent Agents Work

Key Components of a Knowledge Based Agent in AI

A knowledge based agent in AI runs on four main parts. Each part handles a specific role that helps the agent reason, update knowledge, and act.

1. Knowledge Base

You store facts, rules, and domain details here.
It holds statements like “If symptom X appears, test for Y.”
This acts as the memory of the agent.

What it may contain

• Facts
• Definitions
• Constraints
• If–then rules

2. Inference Engine

This part handles reasoning.
It reads the rules, matches them with facts, and creates conclusions.

Common reasoning styles

• Forward chaining
• Backward chaining

Also Read: No Code AI: A Beginner’s Guide to Building AI Without Writing Code

3. Sensor Interface

This collects input from the environment or data source.
The agent uses this input to update its knowledge or trigger reasoning.

4. Actuator Interface

This performs actions based on the agent’s decision.
Actions may include sending messages, triggering responses, or controlling devices.

5. Learning Module (Optional)

This updates old knowledge or adds new rules.
It improves the accuracy of the knowledge based agent in ai over time.

Also Read: Agentic AI vs Generative AI: What Sets Them Apart

How Knowledge Based Agents Work

A knowledge based agent in AI follows a clear loop. It collects data, updates knowledge, reasons through rules, and performs actions. Each step depends on the quality of stored facts and the logic behind them.

Step 1. Perception

The agent receives input through sensors or data sources.
This can be text, signals, user queries, or environment readings.

Step 2. Knowledge Updating

The agent checks if the new input matches existing facts.
It stores new details or modifies old ones to keep the knowledge base current.

Step 3. Reasoning and Decision Making

The inference engine reads the rules and matches them with facts.
It picks the rule that fits the current situation and draws a conclusion.

Step 4. Action Execution

The agent sends the final decision to actuators.
This can trigger a response, run a command, or control a system.

Also Read: How to Build Your Own AI System: Step-by-Step Guide

Table: How Knowledge Based Agents Work

Step	What Happens	Example Actions
Perception	Collects input from the environment	Reading a symptom, scanning a value
Knowledge Updating	Adds or modifies facts in the knowledge base	Updating patient records, storing new rules
Reasoning & Decision Making	Matches rules with facts and draws conclusions	Selecting a diagnosis, choosing a response
Action Execution	Performs the chosen action	Sending a reply, triggering a device

Architecture of Knowledge Based Agents in AI

The architecture of a knowledge based agent in ai follows a structured design. Each unit handles sensing, storing knowledge, reasoning, and taking action. These units work together to support logical and traceable decisions.

Simple Reflex + Knowledge Model

• Uses basic conditions, action rules supported by stored facts.
• Checks the current state, matches a rule, and performs an action.
• Works well when tasks follow clear patterns.

Goal-Based Knowledge Agent Model

• Acts to reach a defined goal.
• Uses stored knowledge to pick the best steps.
• Helps in planning tasks that need multi-step decisions.

Also Read: Generative AI vs Traditional AI: Which One Is Right for You?

Utility-Based Knowledge Agent Model

• Chooses actions that give the highest benefit.
• Uses scoring methods to compare possible outcomes.
• Helps when tasks need trade-offs or ranking options.

Table: Architecture Comparison

Architecture Type	How It Works	Strengths	Use Cases
Simple Reflex + Knowledge	Uses rules plus stored facts	Quick decisions	Monitoring, alerts
Goal-Based Model	Moves toward a target state	Clear planning	Route finding, assistants
Utility-Based Model	Ranks actions by benefit score	Better choices	Recommendations, decision tools

Also Read: How to Learn Artificial Intelligence: A Step-by-Step Roadmap

Applications of Knowledge Based Agents in AI

A knowledge based agent in ai supports tasks that need clear reasoning, rule checks, and domain accuracy. You see these agents across multiple sectors where decisions depend on structured knowledge.

1. Healthcare

• Used in diagnosis support and treatment suggestions.
• Helps doctors check symptoms against medical rules and patient history.

2. Customer Support

• Powers chatbots that answer queries using stored rules.
• Guides users through step-wise solutions.

3. Cybersecurity

• Identifies risk patterns and unusual activity.
• Matches threats with known rules to trigger alerts.

4. Education

• Supports adaptive tutoring tools.
• Answers student questions using subject-specific facts.

5. Manufacturing and Automation

• Helps with fault detection and quality checks.
• Uses rules to monitor machines and predict required actions.

Also Read: Types of AI: From Narrow to Super Intelligence with Examples

Table: Key Application Areas

Domain	Example Tasks
Healthcare	Suggesting tests, checking symptoms
Customer Support	Answering FAQs, guiding steps
Cybersecurity	Flagging threats, sending alerts
Education	Solving queries, guiding lessons
Manufacturing	Detecting faults, planning actions

Become an Expert in Agentic AI with upGrad!

Agentic AI is transforming industries by enabling systems to make decisions and perform tasks autonomously, without human intervention. However, many professionals face challenges in mastering this complex technology, from understanding its core principles to implementing it in practical scenarios. 

With practical insights and hands-on projects, upGrad’s courses equip you to build AI solutions for industries like healthcare, finance, and autonomous vehicles. 

Here are some free courses to help you start your journey in the AI sector.

• Fundamentals of Deep Learning and Neural Networks
• Introduction to Generative AI
• Learn Python Libraries: NumPy, Matplotlib & Pandas
• ChatGPT for Developers
• Analyzing Patterns in Data and Storytelling

Struggling to utilize the full potential of Agentic AI? upGrad offers personalized career counseling to help you choose the best path as per your goals. You can also visit your nearest upGrad center to gain hands-on experience through expert-led courses and real-world projects.