What is a knowledge-based agent?
A knowledge-based agent is an AI system that uses a knowledge base and an inference engine to reason and act. The knowledge base stores domain information, while the inference engine applies rules to known facts and derives conclusions. Its basic cycle is Percepts → Knowledge Base → Inference → Action. For example, a technical support agent can match a printer problem with troubleshooting rules, recommend a solution, and explain the basis for that recommendation.
How does a knowledge-based agent work?
A knowledge based agent in artificial intelligence usually follows a repeated reasoning cycle. It receives information from its environment. It converts that information into structured facts. It then queries its knowledge base before selecting a response or action.
The perception stage
The perception stage collects information from the environment. The source may be a user message, a sensor reading, a transaction record, or a system event.
Raw input is not always ready for logical reasoning. The agent may need to normalize the input first. It can identify important entities, detect conditions, and convert the result into structured percepts.
For example:
User input: The printer has power, but it does not print.
Percepts: printer_has_power, printer_not_printingThis conversion gives the inference engine a consistent format for rule matching.
The TELL–ASK–PERFORM cycle
A classic knowledge based agent uses three core operations:
| Operation | Purpose | Example |
|---|---|---|
| TELL | Adds new information to the knowledge base | Record that an order is delayed |
| ASK | Queries the knowledge base for a conclusion | Check whether the order qualifies for support |
| PERFORM | Carries out the selected action | Send a tracking update |
The inference and action stages
The inference engine checks which rules match the available facts. It then derives one or more conclusions. A planner or action selector can use those conclusions to choose the next step.
A reliable agent should distinguish between a confirmed conclusion and a possible explanation. When the knowledge base does not contain enough information, the agent should ask for clarification or route the case to a human.
What are the main components of a knowledge-based agent?
The architecture of knowledge based agents combines several layers. Each layer has a separate responsibility. This separation makes the system easier to test and maintain.
Knowledge base
The knowledge base stores the facts and rules that an agent uses for reasoning. For example, it may record that a customer has an active subscription and that active subscribers can access a specific support channel.
A knowledge base may use a database, knowledge graph, or rule engine, depending on the domain. Its quality is critical because missing or incorrect information can produce unreliable conclusions and actions.
Knowledge representation
Knowledge representation defines how information is expressed inside the system. Propositional logic handles simple true-or-false statements, while first-order logic uses variables and relationships to represent more complex knowledge.
Other approaches include IF–THEN production rules, which are easy to review in expert systems. Ontologies define domain concepts and their relationships, while knowledge graphs use those relationships to connect entities.
Inference engine
The inference engine applies rules to information in the knowledge base. Forward chaining starts with known facts and applies matching rules until it reaches a conclusion.
Backward chaining starts with a goal and searches for supporting facts. More advanced systems may use techniques such as resolution or unification to process logical statements with variables.
Explanation and belief revision
The explanation module identifies the facts and rules behind a conclusion. This reasoning trace helps users understand why the agent selected a particular answer or action.
Belief revision manages changes and conflicts within the knowledge base. It uses priority policies to determine which information should remain active, which is especially important in regulated workflows.
Where are knowledge-based agents used?
Knowledge based agents are useful when a system must follow explicit domain knowledge. They are especially valuable when the reasoning process needs to be reviewed after an action.
Medical decision support
A medical decision support system can compare symptoms against clinical rules. It may identify possible conditions or recommend the next information to collect.
Such a system should support medical professionals rather than replace them. Its output requires appropriate review, particularly when the decision affects patient care.
Customer support automation
A support agent can search a product knowledge base before answering a customer. It can use business rules to determine whether the issue qualifies for a refund, escalation, or troubleshooting flow.
This process helps keep responses consistent. It also gives the support team a record of why a particular action was selected.
Compliance and decision support
Compliance systems can check transactions against internal policies. They can also flag cases that require additional approval.
A knowledge-based system is useful here because policies can be represented as explicit rules. When a policy changes, the relevant rule can be reviewed and updated.
Education and equipment diagnosis
An intelligent tutoring system can use a student’s previous answers to identify a learning gap. It can then select a suitable explanation or exercise.
An equipment diagnosis system can combine sensor readings with maintenance rules. It may recommend an inspection or create a repair request when certain conditions are met.
What are the benefits and limitations?
Benefits
Knowledge-based agents offer several practical advantages:
Explainability: The system can connect a conclusion to the facts and rules that produced it.
Consistency: The same rule can be applied in the same way across similar cases.
Domain expertise: Teams can encode specialized knowledge for a particular industry.
Maintainability: A rule can often be updated without retraining an entire model.
Auditability: Decision records can show which information influenced an action.
Goal-oriented reasoning: The agent can reason toward a defined objective instead of generating an unrelated response.
These advantages make knowledge based agents suitable for structured workflows. They are less dependent on hidden model behavior when their decisions rely on explicit rules.
Limitations
Knowledge-based agents require continuous maintenance to remain accurate and effective. Building the knowledge base takes time and domain expertise, while outdated information or conflicting rules can lead to unreliable decisions. Version control, regular reviews, and clear rule priorities are therefore essential.
Traditional rule systems may also struggle with ambiguous, noisy, or out-of-domain input. As the knowledge base grows, inference can become more expensive, so careful indexing and rule organization are needed to maintain performance.
How to build a simple knowledge-based agent
The following example uses an IT support scenario. It shows the basic structure without treating a rule-based prototype as a production system.
Step 1: Define the knowledge base
Start with facts and rules for a narrow domain:
KNOWLEDGE_BASE = {
"printer_has_power": True,
"printer_not_printing": True,
"print_queue_stuck": False,
}
RULES = [
{
"conditions": {"printer_has_power", "printer_not_printing"},
"conclusion": "check_print_queue",
},
]A small knowledge base is easier to review. The facts should use consistent names so the inference engine can match them reliably.
Step 2: Convert input into structured facts
The perception layer converts user input into facts. A real system may use natural language processing for this step.
A simple prototype can begin with predefined inputs:
percepts = {
"printer_has_power",
"printer_not_printing",
}This keeps the example focused on reasoning rather than language parsing.
Step 3: Apply the inference rules
The inference engine checks whether all conditions for a rule are present:
def infer(percepts, rules):
conclusions = []
for rule in rules:
if rule["conditions"].issubset(percepts):
conclusions.append(rule["conclusion"])
return conclusionsThe function returns the conclusions supported by the current percepts. It does not invent a conclusion when the required facts are missing.
Step 4: Select an action
The action layer maps a conclusion to a response:
def perform(conclusions):
if "check_print_queue" in conclusions:
return "Open the print queue and remove any stuck job."
return "Request more information before continuing."A production agent would apply permissions and safety checks before calling an external tool.
Step 5: Explain the result
A useful agent should show why it selected the action:
conclusions = infer(percepts, RULES)
response = perform(conclusions)
print(response)The system can also log the matched rule. This creates a basic reasoning trace that a developer or support specialist can inspect.
This example represents a minimal knowledge based agent. A larger implementation would need input validation, rule priorities, knowledge versioning, error handling, and human escalation.
Meet Kimi Agent: turn knowledge into finished work
Traditional knowledge-based agents depend on explicit facts and rules. Kimi Agent takes a broader, modern approach to knowledge-intensive work. Give it an outcome, and it can plan the task before completing the required steps with built-in tools. You can use it to research a topic, process source material, or create an editable deliverable without building a separate orchestration layer.
Bring your source knowledge into the task
A useful agent needs access to the right context. Kimi lets you upload as many as 50 files, with a limit of 100 MB per file. It supports PDFs and common office documents. You can also add images, TXT files, or video when the task requires more context.
This makes existing material part of the workflow. For example, you can ask Kimi Agent to analyze a collection of reports, identify important findings, and turn the result into a structured document. The files provide task-specific knowledge, while your prompt defines the intended outcome.
Turn research into an editable deliverable
Kimi Agent can work across several product surfaces, so the result does not need to stop at a chat response:
Create a research report with supporting sources through Kimi Deep Research.
Build a working multi-page website from a short brief.
Generate an editable PPT presentation from source materials.
Kimi can also handle document and spreadsheet workflows. This allows one task to move from information gathering to a usable output without requiring you to transfer the content between separate tools.
Scale a large goal with Kimi Agent Swarm
Some tasks are too large for a single sequential workflow. Kimi Agent Swarm can divide a goal among more than 300 sub-agents and support up to 4,500 parallel tool calls.
This parallel structure is designed for large-scale search and long-form writing. It can also support batch tasks that would take much longer to complete one item at a time. The sub-agents work on separate parts of the goal, while the overall workflow remains tied to the requested outcome.
Conclusion
Knowledge-based agents in AI use structured knowledge to reason about situations and select actions. Their explicit rules make decisions easier to explain and review. Their main challenges come from knowledge maintenance, conflicting rules, and incomplete input. Modern systems can combine knowledge-based reasoning with machine learning, RAG, and generative AI. Kimi provides a useful reference point for knowledge-intensive workflows, while traditional knowledge-based agents remain valuable when decisions must follow clear domain rules.