Definition

What is a reasoning engine?

Stephen J. Bigelow, Senior Technology Editor

A reasoning engine is an iterative software system that mimics human problem-solving capabilities, making inferences or reaching conclusions based on rules, logic and established facts or data. Also known as a reasoner, semantic reasoner or rules engine, a reasoning engine is a central element of generative artificial intelligence applications, such as AI agents.

These engines apply three general types of reasoning:

Deductive reasoning draws inferences from known facts, applying generally accepted ideas to specific cases. For example, all birds have feathers; a crow has feathers; therefore, a crow is a bird.
Inductive reasoning draws general conclusions from specific or limited experiences, using these instances to make broader determinations. For example, every bird I've seen eats seeds; therefore, all birds eat seeds.
Abductive reasoning draws conclusions from probability and circumstance, inferring the most likely explanation from available evidence. For example, there is a tear in the curtain; the cat loves to claw fabrics; the cat was alone at home; therefore, the cat tore the curtain.

Reasoning engines are fundamentally different from simpler models, such as direct rule sets or predefined decision trees. Reasoning engines process enormous data sets, offering highly dynamic decision-making capabilities that complement human thought processes.

How does a reasoning engine work?

A reasoning engine applies varied techniques to reach an objective in a step-by-step process with five clear phases:

Input. User prompts are input, and the reasoning engine analyzes the prompt to parse goals, actions and methodologies. Input is typically handled with associated technologies, such as natural language processing (NLP) and large language models (LLMs), similar to the technologies used with ChatGPT, Gemini or Vertex AI.
Analysis. The reasoning engine translates the user's intentions, employing the parsed input as a plan of action. Plans range from processing data to creating workflows that solve complex tasks.
Execution. The autonomous reasoning engine completes the plan it created, attempting to address the user's prompt. The plan may include accessing data; provisioning compute resources; executing function calls, such as application program interfaces (APIs); performing compute tasks; and applying the reasoning approaches -- deductive, inductive and/or abductive -- to finish the work.
Validation. The reasoning engine then evaluates the outcome of its actions compared to the original plan. Validating the results may involve automated testing; checking for errors, such as hallucinations; and interacting with the user for feedback. Reasoning engines learn from data validation and refine their own analysis and execution processes.
Iteration. When there is deviation between objective and outcome, the reasoning engine starts again and repeats the cycle, gathering more information if needed, adjusting the plan, executing those adjusted plans and reevaluating the results until reaching the desired outcome.

What is the difference between reasoning and search engines?

The main difference between reasoning engines and search engines is the scope of detail in their responses.

A search engine is primarily a lookup system that finds and returns relevant responses based on keywords, providing broad but shallow results. Search engines are typically better suited to simple, direct questions. For example, a search engine answers the query, "Who sang 'Sweet Baby James?'" with the reply, "James Taylor."

A reasoning engine analyzes information, makes inferences, demonstrates an understanding of context and correlates details in ways that resemble human thought. This enables reasoning engines to answer complex questions, solve problems or offer conclusions far beyond a search engine's capabilities. For example, a reasoning engine addresses the question, "What does the song 'Sweet Baby James' mean?" with a detailed response, including artistic influences and cultural or life events that provide deeper context.

Components of a reasoning engine

A reasoning engine understands user input, plans an effective set of actions to satisfy that input and executes the plan to generate an intended result. Several major components join to create a reasoning engine, including the following:

User interface. Any reasoning engine starts with a user interface (UI) capable of receiving and interpreting user prompts. This typically involves NLP and LLM platforms that parse input and establish its meaning and context. The UI also provides output, including text, speech and tangible actions.
Data. The reasoning engine needs access to relevant and extensive data sources for processing. Data is organized within a structured database for better data access performance. Data sometimes comes from outside or third-party sources depending on the query.
Inference engine. The heart of the reasoning engine, the inference engine applies the rules and logic needed to process data, draw conclusions and make decisions in accordance with user goals.
Machine learning models. An array of trained machine learning (ML) models usually supports an inference engine, assisting in the execution of logic and rules, applying processes, assembling workflows, and supporting continued learning and process refinement through feedback and user interactions.
Integrations. The reasoning engine typically includes integrations -- software connectors and APIs among them -- to smooth interactions across business databases and enterprise applications, such as enterprise resource planning systems. Specific integrations vary with the reasoning engine's design and intent.
Compute platform. The reasoning engine, a collection of software entities, demands a comprehensive hardware infrastructure to function, including compute, storage and network resources. For example, part of the compute platform includes working memory to hold in-progress processing. The compute infrastructure exists in a local data center, the cloud or a mix of both based on the reasoning engine's design and capabilities.

Types of reasoning engines

Depending on the certainty required and kind of data used, multiple reasoning engines are suited for integration into an AI system. In practice, an AI system combines two or more reasoning engines within a single AI application to improve decision-making performance and outcomes. The following are the four main types of reasoning engines:

ML-based reasoning. ML-based reasoning uses data-driven approaches similar to other types of analytics: Vast amounts of data are analyzed, patterns are identified, predictions are made based on those patterns and suitable conclusions are drawn. ML-based reasoning is best when comprehensive and reliable data is readily available. However, creating specific rules is sometimes difficult or impractical. For example, a spam filter analyzes an email based on learned patterns from previous spam, including keywords, sender's address or email structure, to determine if current email is possibly spam.
Rule-based reasoning. Rule-based reasoning relies on established standards grounded in traditional logic to reach conclusions or make decisions. Rule-based reasoning is best in tandem with clear and undisputed knowledge or relationships. For example, drivers typically wear a seat belt because of the potential danger -- either a fine if stopped by police or an injury from an accident -- in not wearing one.
Semantic reasoning. Semantic reasoning relies on detailed models to represent rules and relationships that adhere to principles of knowledge representation and reasoning. Semantic reasoning uses such models to create and grow a knowledge base or make new discoveries. It is best when data is complex and requires detailed interpretation. For example, if a dog is a type of animal and Rex is a dog, then the reasoning engine infers that Rex is also an animal.
Probabilistic reasoning. Probabilistic reasoning relies on probability and statistics to make inferences and determinations. Probabilistic reasoning is best when data is limited, uncertain or incomplete, or if a range of potential outcomes requires evaluation. For example, a medical diagnostic assistant reports a 65% chance a patient has a particular condition based on symptoms, medical imaging, test results and other data.

Benefits of reasoning engines

Reasoning engines provide vital business benefits to assist enterprise-scale decision-making, including the following:

Better-informed decisions. As with other ML models, reasoning engines process huge volumes of data, identify patterns, and find relationships faster and more accurately than human teams.
Complex task solutions. Reasoning engines support the processing, organization and execution of complex business tasks and workflows, leading to more secure, efficient, cost-effective and consistent business processes. The need for human intervention is eliminated unless results need validation or approval.
Self-learning. Reasoning engines evaluate results, optimize decision-making, learn new data and adapt to changing needs without traditional retraining.

Challenges with reasoning engines

Despite their broad benefits and use cases, reasoning engines pose challenges that business and technology leaders must consider, including the following:

Complexity. Reasoning engines are complex AI entities that demand extensive knowledge in application design and AI operations. Development and maintenance of reasoning engines are typically resource-intensive and require a team with comprehensive skill in AI software design.
Management. Reasoning engines and AI systems in general are noted for their autonomy, processing data, making decisions and taking actions with little or no human intervention. However, oversight remains a requirement. Sharp business leaders work to ensure the security of data and transactions, establish meaningful metrics to track the performance -- both successes and failures -- of reasoning engines, receive reporting and timely alerts, and act immediately to remediate any engine problems.
Understanding. Reasoning engines use decision-making patterns that are often opaque and difficult to understand, posing serious compliance and security risks. After all, if the business cannot fully understand how an AI is making decisions, is the system safe and reliable to use in production? Reasoning engines and other AI systems must consider the importance of explainable AI to ensure AI behaviors are transparent, understandable and trustworthy.
Adaptability. Not all reasoning engines are equally adaptable. For example, rule-based reasoning engines struggle with adaptation once rules are established. Conversely, highly adaptable reasoning requires strict behavioral guardrails to prevent unintended consequences of inaccurate decision-making. Consider how the reasoning engine handles change or edge cases.

Use cases for reasoning engines

Because of their performance in disparate environments, reasoning engines are widely accepted and employed, regardless of industry or situation. The following are some current use cases:

Chatbots. Rather than using static decision trees, reasoning engines understand content and context in conversations or questions, empowering them to provide meaningful assistance and information.
Help desk. As with chatbots, reasoning engines combine technical diagnostics with the interpretation of reported behaviors to diagnose problems and take autonomous corrective actions.
Fraud detection. Reasoning engines process enormous volumes of transactional data and identify potential fraud or other malfeasance.
Personalization. Reasoning engines create messaging and recommend products to specific customers based on demonstrated behaviors and known preferences.
Medical diagnosis. Using extensive medical knowledge base data, reasoning engines collect and analyze patient symptoms, telemetry and imaging to provide diagnoses and suggest further action.
Process optimization. Reasoning engines make dynamic changes to workflows, inventory and logistics to improve manufacturing, shipping, maintenance and supply chain operations.
Document analysis. Reasoning engines review contracts and other legal documents to summarize content and identify errors, conflicts or risks that a human review may miss. The same technology assists scientific research, processing huge data sets and producing theories based on established science.