Skip to main contentReasoning Engine
Deductive AI’s reasoning engine is the core intelligence that orchestrates investigations, analyzes evidence, and generates insights across your entire production stack.
Multi-Agent Architecture
The reasoning engine coordinates specialized agents, each optimized for a specific domain:
Reasoning Process
1. Query Understanding
The engine first parses and understands the user’s intent:
2. Hypothesis Generation
Instead of pursuing a single theory, the engine generates multiple hypotheses:
- Hypothesis 1: Recent code change caused the issue
- Hypothesis 2: Infrastructure scaling event triggered the problem
- Hypothesis 3: Configuration change introduced a bug
- Hypothesis 4: External dependency failure
3. Parallel Investigation
Each hypothesis is investigated in parallel:
4. Evidence Collection
Agents collect evidence from multiple sources:
- Code: Commits, PRs, code diffs, file contents
- Metrics: Time series data, aggregations, anomalies
- Logs: Error messages, stack traces, event sequences
- Infrastructure: Resource changes, scaling events, config updates
- Alerts: Incident timelines, notification history
5. Causal Analysis
The engine builds causal relationships:
6. Confidence Scoring
Each hypothesis receives a confidence score based on:
- Evidence Strength - How strong is the supporting evidence?
- Causal Links - How clear are the causal relationships?
- Temporal Alignment - Do events align in time?
- Historical Patterns - Have we seen similar patterns before?
7. Synthesis & Response
The engine synthesizes findings into a coherent response:
- Primary Conclusion - The most likely root cause
- Supporting Evidence - Key evidence pieces
- Alternative Theories - Other possibilities with lower confidence
- Recommended Actions - Next steps to resolve or investigate further
Advanced Capabilities
Temporal Reasoning
The engine understands time relationships:
- Before/After - Event sequencing
- Simultaneous - Concurrent events
- Delayed Effects - Causation with time delays
- Cyclical Patterns - Recurring behaviors
Cross-Domain Correlation
The engine correlates events across domains:
- Code changes → Metric changes → Log errors → User impact
- Infrastructure events → Application behavior → Business metrics
- External events → System response → Incident timeline
Learning & Adaptation
The engine learns from:
- Historical Incidents - Patterns from past resolutions
- User Feedback - Corrections and confirmations
- Team Knowledge - Runbooks, wikis, best practices
- Production Patterns - Normal vs. abnormal behaviors
Parallel Execution
Investigations run in parallel to minimize latency:
Caching
Frequently accessed data is cached:
- Recent code changes
- Common metric queries
- Frequently searched logs
- Infrastructure state snapshots
Incremental Updates
The engine updates its understanding incrementally:
- New evidence refines hypotheses
- Confidence scores update in real-time
- Timeline builds progressively
Next Steps