Generate and evaluate explanatory hypotheses from incomplete observations. Use when diagnosing anomalies, explaining unexpected outcomes, or inferring causes from effects. Produces ranked hypotheses with evidence and confidence scores.
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name
reasoning-abductive
description
Generate and evaluate explanatory hypotheses from incomplete observations. Use when diagnosing anomalies, explaining unexpected outcomes, or inferring causes from effects. Produces ranked hypotheses with evidence and confidence scores.
Abductive Reasoning
Generate best explanations from observations. The logic of diagnosis and inference to cause.
Anomaly or surprise present (metric deviation, unexpected behavior)
Need to explain an observation (why did X happen?)
Incomplete data, must infer cause
Diagnostic context (errors, issues, failures)
Multiple possible explanations exist
Don't use when:
Cause is already known (use causal execution instead)
Need to compare decision alternatives
Resolving stakeholder disagreements
Four-Stage Process
Stage 1: Observation
Purpose: Transform raw data into structured anomaly description.
Input:
observation:raw_data:"Conversion dropped from 12% to 7% in Q4"context:timeframe:"Q4 2025"baseline:
"12% historical average"
current:
"7% observed"
surprise_level:
0.8
# How unexpected is this?
Process:
Quantify the deviation (% change, sigma from mean)
Identify temporal boundaries (when did it start?)
Scope the phenomenon (which segments affected?)
Rate surprise level (expected vs unexpected)
Output:
anomaly:description:"42% drop in conversion rate (12% → 7%)"deviation:"-5 percentage points, -42% relative"temporal:"Started week 3 of Q4, persists through Q4"scope:"All segments equally affected"surprise:0.8baseline_source:"12-month rolling average"
Stage 2: Hypotheses Generation
Purpose: Generate diverse possible explanations without judgment.
Rules:
Generate ≥5 hypotheses (more is better initially)
Include obvious AND non-obvious causes
Consider internal AND external factors
Don't filter yet - cast wide net
Hypothesis Categories:
Category
Examples
Technical
Site issues, bugs, performance
Product
Features, pricing, positioning
Market
Competition, trends, seasonality
Operational
Team changes, process issues
External
Economy, regulations, events
Output:
hypotheses:-id:H1cause:"Website performance degradation"category:technicalmechanism:"Slow load times → abandonment"-id:H2cause:"Competitor launched aggressive pricing"category:marketmechanism:"Price undercut → customer diversion"-id:H3cause:"Seasonal Q4 shopping behavior change"category:marketmechanism:"Holiday spending patterns differ"-id:H4cause:"Product-market fit weakening"category:productmechanism:"Customer needs evolving away"-id:H5cause:"Sales qualification criteria changed"category:operationalmechanism:"Different lead quality entering funnel"# ... continue until exhaustive
Stage 3: Evidence Evaluation
Purpose: Score each hypothesis against available evidence.
For each hypothesis, evaluate:
Criterion
Question
Score
Explanatory power
Does it fully explain the anomaly?
0-1
Simplicity
Fewest assumptions required?
0-1
Coherence
Consistent with other known facts?
0-1
Testability
Can we verify/falsify it?
0-1
Prior probability
How likely independent of this data?
0-1
Evidence Collection:
evidence:H1_technical:supporting:-"Page load time increased 2s in Q4"(confidence:0.9)-"Mobile bounce rate up 15%"(confidence:0.85)contradicting:-"Desktop conversion stable"(confidence:0.8)net_score:0.65H2_competitor:supporting:-"Competitor launched Oct 15"(confidence:1.0)-"Google Trends shows competitor interest up"(confidence:0.7)contradicting:-"Our traffic unchanged"(confidence:0.9)net_score:0.55# ... evaluate all hypotheses
conclusion:primary_cause:"Website performance degradation"confidence:0.75mechanism:"2s increase in load time caused 42% more abandonment,
consistent with industry benchmarks (1s = ~7% conversion loss)"contributing_factors:-"Seasonal patterns may account for 10-15% of drop"ruled_out:-"Competitor pricing (traffic unchanged, not price-sensitive segment)"remaining_uncertainty:-"Whether mobile-specific or site-wide"-"Whether fix will fully restore conversion"next_steps:-"Verify: Run A/B test with performance fix (high priority)"-"Measure: Mobile vs desktop split post-fix"-"Monitor: Competitor activity (low priority)"suggested_next_mode:causal# Ready to act on diagnosis
Quality Gates
Gate
Requirement
Failure Action
Hypothesis count
≥5 hypotheses
Generate more before proceeding
Category diversity
≥3 categories
Expand hypothesis search
Evidence present
≥1 data point per top-3
Gather more evidence
Confidence threshold
≥0.6 for best
Flag as inconclusive
Testability
Best hypothesis testable
Propose test design
Common Failure Modes
Failure
Symptom
Fix
Anchoring
First hypothesis gets all attention
Force diversity in Stage 2
Confirmation bias
Only seek supporting evidence
Require contradicting evidence
Complexity creep
Elaborate explanations preferred
Weight simplicity appropriately
Premature closure
Stop at first plausible cause
Complete all 4 stages
Output Contract
abductive_output:conclusion:primary_cause:stringconfidence:float# 0.0-1.0mechanism:string# How cause produces effecthypotheses:ranked: [ScoredHypothesis] # All evaluatedruled_out: [string] # Definitively excludedevidence:supporting: [EvidenceItem]
contradicting: [EvidenceItem]
gaps: [string] # What evidence is missing?uncertainty:remaining_questions: [string]
confidence_bounds: [float, float] # Low, highnext:immediate_actions: [string]
tests_to_run: [string]
suggested_mode:optional<ReasoningMode>trace:stages_completed: [1, 2, 3, 4]
duration_ms:inthypotheses_generated:intevidence_points:int
Example Execution
Context: "Enterprise conversion dropped 40% last quarter"
Stage 1 - Observation:
Anomaly: 40% drop (15% → 9%) in enterprise conversion
Temporal: Started week 5 of Q3, accelerated Q4
Scope: Enterprise only, SMB stable
Surprise: 0.85
Stage 2 - Hypotheses:
H1: Enterprise buyer behavior changed (economic uncertainty)
H2: Sales team restructuring disrupted relationships
H3: Competitor launched enterprise-specific offering
H4: Our enterprise pricing became uncompetitive
H5: Product gaps for enterprise use cases
H6: Longer sales cycles (not drop, just delay)
H7: Key account manager departures
Primary: Sales cycle elongation (not true drop) + AM departures (relationship gaps)
Confidence: 0.72
Mechanism: Economic uncertainty extended CFO approval cycles by 45 days;
AM departures created relationship gaps in 6 key accounts
Next: Wait 45 days to see if "delayed" deals close (causal monitoring)
Immediately backfill AM roles (causal action)
