What is Human Reasoning?

Let's start with a fundamental question: What is human reasoning?

According to Kenneth J. Kurtz et al., human reasoning can be broadly described as a set of cognitive processes by which humans take an initial set of information and generate inferences as part of a decision-making process. The inferences produced range from deterministic to probabilistic conclusions. Essentially, human reasoning is a cognitive process of using logic and information—whether existing or new—to make decisions. This involves consuming large sets of knowledge, contextual information, and prior experiences, then converting them into logical decision-making steps to accomplish tasks.

Human reasoning can be categorized into five main types: Deductive, Abductive, Inductive, Analogical, and Causal reasoning. Each follows specific patterns when applied to a given set of knowledge. For instance, deductive reasoning starts with premises and associated observations, then draws inferences to arrive at a specific conclusion as deterministic set of steps to be followed.

In this blog, we'll explore four of these reasoning types with relevant ITOps examples to understand how AI agents can leverage these patterns for automation.

What is Deductive Reasoning?

In deductive reasoning, conclusions are drawn from a set of premises. When the premises are true, the conclusion must also be true—there's no chance of it being false. Essentially, it's a process of drawing valid inferences from given premises.

Logical Pattern:

1. P → Q (First premise: conditional statement)
2. P is true (Second premise: the antecedent)
3. Therefore, Q is true (Conclusion: the consequent)

General Pattern: Premise₁, Premise₂, ... Premiseₙ → Conclusion

ITOps Examples:

Example 1: Security Incident Analysis

• Premise 1: If a user account shows login from two geographically distant locations within 1 hour, it indicates credential compromise.
• Premise 2: User "john.doe" logged in from New York at 10:00 AM.
• Premise 3: User "john.doe" logged in from Singapore at 10:30 AM.
• Deduction: The account "john.doe" is likely compromised. Initiate incident response protocol.

Example 2: Application Crash Root Cause Analysis

• Premise 1: All Java applications crash with OutOfMemoryError when heap usage exceeds allocated memory.
• Premise 2: Application logs show: java.lang.OutOfMemoryError: Java heap space
• Premise 3: Heap size is configured to 2GB (-Xmx2g)
• Premise 4: Memory profiler shows actual heap usage reached 2.1GB before crash.
• Deduction: The application requires more heap memory. Increase -Xmx setting or optimize memory usage.

What is Abductive Reasoning?

Abductive reasoning works backward from an observation to find the most likely explanation. Unlike deduction (certainty from rules) or induction (patterns from data), abduction asks: "What's the best explanation for what I'm observing?"

Pattern: Observation → Best Explanation (Hypothesis)

ITOps Examples:

Example 1: Sudden CPU Spike

Observation: CPU usage suddenly jumped from 20% to 95% at 3:00 AM.

Possible Explanations:

• Scheduled batch job started
• Malware/cryptominer infection
• Infinite loop in application code
• DDoS attack
• Memory leak causing excessive garbage collection

Best Explanation: Most likely a scheduled batch job, because:

• The timing is consistent (3:00 AM is common for batch jobs)
• Cron logs show a data processing job started at 3:00 AM
• CPU returns to normal after job completes

Example 2: Container Keeps Restarting

Observation: Kubernetes pod restarts every 5 minutes with exit code 137 (SIGKILL).

Possible Explanations:

• Application crash/panic
• OOM (Out of Memory) kill
• Health check failure
• Resource limit exceeded
• Deadlock causing timeout

Best Explanation: OOM kill, because:

• Exit code 137 indicates SIGKILL (often from OOM)
• dmesg shows: "Out of memory: Kill process..."
• Memory usage graph shows steady climb to limit
• No application error logs before termination

What is Inductive Reasoning?

Inductive reasoning draws conclusions from patterns observed in present and historical data. The conclusions are probabilistic in nature and represent generalized hypotheses that explain the observed patterns in the most likely sense.

Pattern: Multiple Observations → Generalized Pattern/Rule

ITOps Examples

Example 1: Failure Patterns Before Crashes

Observations:

Inductive Conclusion: When memory usage exceeds 94%, the application is likely to crash within 4-6 minutes.

Application: Implement predictive alerting to trigger warnings at 94% memory threshold.

Example 2: Error Rate and Traffic Correlation

Observations:

• 1,000 requests/sec → 0.1% error rate
• 2,000 requests/sec → 0.2% error rate
• 3,000 requests/sec → 0.3% error rate
• 4,000 requests/sec → 0.4% error rate
• 5,000 requests/sec → 0.5% error rate

Inductive Conclusion: Error rate increases linearly with request volume at approximately 0.01% per 100 requests/sec.

Application: Predict error rates based on incoming request volume and set capacity planning thresholds.

What is Causal Reasoning?

Causal reasoning identifies relationships between causes and their effects. It enables systems to establish connections between events—understanding which preceding events resulted in current outcomes. This involves temporal reasoning, where the sequence and timing of events matter.

Pattern: Cause → Mechanism → Effect (with temporal order and evidence)

ITOps Examples

Example 1: Memory Leak Causes Application Crash

Causal Chain:

Event: "Memory leak in code" → Heap usage grows continuously → Garbage collection becomes more frequent → GC pauses increase → Application becomes unresponsive → Eventually: OutOfMemoryError → Application crashes

Evidence of Causation:

• Temporal order: Memory growth precedes crash
• Reproducibility: Pattern happens repeatedly
• Mechanism: Java heap exhaustion triggers OOM exception
• Intervention: Fix the memory leak (e.g., remove references to objects in static collection) → Crashes stop

Example 2: Missing Database Index Causes Query Slowdown

Causal Chain:

Event: "Index dropped during migration" → Database uses full table scan instead of index seek → Query must read all 10M rows → Disk I/O increases 100x → Query time increases from 50ms to 30 seconds

Evidence of Causation:

• Temporal order: Index drop timestamp matches slowdown start
• Mechanism: Query execution plan changed from index seek to table scan
• Reproducibility: Recreating index restores performance
• Controlled experiment: Same query with index = fast; without index = slow
• Intervention: Recreate the index → Query performance restored

Conclusion

Understanding these reasoning patterns is crucial for building effective AI agents for ITOps automation. Each reasoning type serves a distinct purpose:

• Deductive reasoning provides certainty for rule-based automation
• Abductive reasoning helps diagnose problems by finding best explanations
• Inductive reasoning enables predictive capabilities from historical patterns
• Causal reasoning uncovers root causes and supports intervention planning

Deductive reasoning is most effective when rules are well-defined and outcomes are certain. AI agents apply deductive logic to IT operations scenarios requiring deterministic decision-making, such as automatic alert triggering, access control, threat detection, policy enforcement, and runbook automation. In these cases, if specific conditions are met, the agent can confidently execute predetermined actions without ambiguity.

Abductive reasoning comes into play when AI agents must diagnose issues by identifying the most plausible explanation for observed events. When an anomaly occurs in IT systems—such as unexpected service degradation or system behavior—abductive reasoning enables agents to evaluate multiple hypotheses and converge on the best explanation based on available evidence, even when complete information is unavailable.

Inductive reasoning is valuable when AI agents need to discover patterns from historical observations and contextual data. This reasoning type is particularly well-suited for predictive maintenance and capacity planning scenarios, where agents analyze trends across multiple incidents or performance metrics to forecast future behavior and proactively prevent issues before they occur.

Causal reasoning is applied when AI agents must trace the exact chain of events leading to a specific outcome. This reasoning type is critical for root cause analysis, enabling agents to establish temporal relationships between events, understand the mechanisms by which failures propagate, and identify the precise cause of incidents—ultimately supporting effective remediation and preventing recurrence.

As AI agents become more sophisticated, incorporating these reasoning capabilities will enable them to move beyond simple automation toward intelligent, context-aware decision-making in complex IT operations environments.

References

1. Kurtz, K. J., Gentner, D., & Gunn, V. (1999). "Reasoning". In B. M. Bly & D. E. Rumelhart (Eds.), Cognitive Science (pp. 145-200). Academic Press. https://doi.org/10.1016/B978-012601730-4/50006-8
2. "Universal Landscape of Human Reasoning" - https://arxiv.org/html/2510.21623v1
3. Dietz, E., Fichte, J. K., Hamiti, F. (2022). "A Quantitative Symbolic Approach to Individual Human Reasoning". In Proceedings of the 44th Annual Meeting of the Cognitive Science Society (CogSci'22)
4. "Inductive or Deductive? Rethinking the Fundamental Reasoning Abilities of LLMs", ACL 2024 Workshop on Natural Language Reasoning and Structured Explanations
5. Psychology of Reasoning - https://en.wikipedia.org/wiki/Psychology_of_reasoning