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Memory Engine: Your Infrastructure’s Long-Term Memory

The Memory Engine is RubixKube’s knowledge system that captures, stores, and learns from every incident, configuration change, and remediation action. It’s the reason RubixKube gets smarter over time instead of repeating the same mistakes.
The Problem It Solves: When your best SRE leaves the company, years of tribal knowledge walks out the door. The Memory Engine ensures nothing is ever forgotten.

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Beta Release - Learning in Progress

The Memory Engine is active and learning in RubixKube Beta:Available NOW: - Incident capture and storage
  • RCA report generation and history
  • Pattern recognition
  • Knowledge graph construction
  • Context-aware retrieval for manual decisions
Enhanced in Future Releases: - Automatic remediation based on memory (currently: suggestions only)
  • Proactive prevention automation (currently: alerts and recommendations)
  • Cross-cluster learning
  • Memory-driven auto-tuning
Your Memory Engine is building knowledge NOW that will power autonomous features when released.

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What is the Memory Engine?

Think of the Memory Engine as your infrastructure’s collective memory - it remembers:

What Happened

Complete timeline of every incident, deployment, configuration change

Why It Happened

Root causes, contributing factors, dependency chains

How It Was Fixed

Remediation steps that worked (and those that didn’t)

What We Learned

Patterns, correlations, and insights extracted from experience

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How It Works

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1. Continuous Capture

Every significant event is captured automatically: 
Incident Event:
  timestamp: 2025-10-03T14:23:15Z
  type: pod_crash
  resource: checkout-service-7f9d
  reason: OOMKilled
  
  context:
    - memory_limit: 512Mi
    - memory_usage_at_crash: 487Mi
    - uptime: 4h 12m
    - recent_deploy: v2.3.1 (2h ago)
    - traffic_spike: +40% at 14:15
    
  related_events:
    - deploy_event: v2.3.1
    - metric_anomaly: memory_growth_rate
    - code_change: payload_size_increase
Metadata: - Resource details (pod, deployment, service names)
  • Timestamps (when it happened, how long it lasted)
  • Status changes (states before/during/after)
Context: - Logs (relevant excerpts, not full dumps)
  • Metrics (CPU, memory, network at time of incident)
  • Events (Kubernetes events, deployment history)
  • Configuration (resource limits, env vars, labels)
Relationships: - Dependencies (what depends on what)
  • Correlations (concurrent anomalies)
  • Causation (what caused what)
Privacy Note: Application data and business logic are NEVER stored.

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2. Knowledge Graph Construction

The Memory Engine builds a knowledge graph connecting concepts: 
[OOMKilled] ----causes----> [Pod Crash]
     |
     |----occurs_when----> [Memory > 95% of limit]
     |
     |----correlates_with----> [Traffic Spike]
     |
     |----fixed_by----> [Increase Memory Limit]
     |                          |
     |                          |----typical_increase----> [512Mi → 1Gi]
     |                          |----success_rate----> [94%]
     |
     |----prevented_by----> [HPA Configuration]
                                  |----scales_at----> [80% memory]
This graph grows richer over time.

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3. Pattern Recognition

The Memory Engine identifies recurring patterns:

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“This happens every Monday at 9am”

  • Traffic spikes at specific times
  • Batch jobs causing resource contention
  • Deployment windows correlating with incidents
Action: Proactive scaling or resource allocation

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”When X happens, Y follows 80% of the time”

  • Database connection pool exhaustion → API timeouts
  • Config map changes → Pod restart loops
  • Version upgrades → Memory leaks
Action: Predict Y before it happens, prepare mitigation

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”This fix works 95% of the time for this issue”

  • Proven remediations for common failures
  • Optimal resource sizing for workload types
  • Effective rollback strategies
Action: Apply proven fixes confidently

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”This approach made things worse”

  • Changes that caused cascading failures
  • Remediations that didn’t work
  • Configurations that led to instability
Action: Avoid known-bad approaches

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4. Context-Aware Retrieval

When a new incident occurs, Memory Engine: 1.Compares current situation to historical incidents 2.Ranks by similarity (matching symptoms, environment, timing) 3.Retrieves relevant past RCAs and fixes 4.Suggests proven solutions from memory Speed: < 500ms from incident detection to memory recall

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The Learning Lifecycle

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How Memory Improves Over Time

1

Week 1: Initial Learning

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Baseline Establishment

  • Observes normal operation patterns
  • Records resource usage baselines
  • Captures configuration state
  • Builds initial dependency graph
Capability: Detection only
2

Week 2-4: Pattern Formation

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First Incidents Handled

  • 5-10 incidents processed
  • Root causes identified (with human verification)
  • Remediations recorded
  • Initial patterns emerging
Capability: Suggestions with low confidence
3

Month 2-3: Confidence Building

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Pattern Validation

  • 30-50 incidents in memory
  • Recurring patterns validated
  • Fix success rates calculated
  • Predictions starting
Capability: High-confidence suggestions
4

Month 4+: Mature Intelligence

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Institutional Knowledge

  • 100+ incidents stored
  • Complex patterns recognized
  • Proactive prevention enabled
  • Auto-remediation trusted
Capability: Autonomous operations (with guardrails)
The more you use RubixKube, the smarter it gets. After 6 months, your Memory Engine knows your infrastructure better than any single person.

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What’s Stored in Memory?

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Data Categories

  • Incidents
  • Remediations
  • Configurations
  • Patterns

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Every failure, degradation, or anomaly:

  • When it happened
  • What failed
  • Impact radius (affected services/users)
  • Duration
  • Resolution steps
  • Who was involved
  • Final outcome

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Memory-Powered Features

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1. Intelligent RCA

Memory Engine makes Root Cause Analysis context-aware : Without Memory: > “Pod crashed due to OOMKilled. Increase memory.” With Memory: > “Pod crashed due to OOMKilled. This is the 3rd occurrence this month, all after v2.x deployments. Previous fixes (512Mi→1Gi) resolved similar incidents in an average of 45 seconds with 100% success rate. Root cause likely: payload size increase in v2.3.1 (deployed 2h ago). Recommend: Increase memory to 1Gi AND investigate memory leak in v2.3.1.” See the difference?Context transforms diagnosis.

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2. Proactive Prevention

Memory enables prediction before problems :
Before deploying v2.4.0, Memory Engine checks:
  • Similar versions: v2.3.x had 40% incident rate
  • Change size: Large payload changes previously caused OOM
  • Dependencies: Database schema migration required (high risk)
Risk Score: 7/10 (High) ** Risk Score: 7/10 (High)** Deploy to canary first, watch memory metrics
Memory tracks trends:
  • Database connections grow 10% monthly
  • Current pool: 100 connections
  • Usage trending to 95% in 3 weeks
Alert: “Increase connection pool before hitting limits”
Timing: Proactive, not reactive
Memory knows the “golden state”:
  • Production config changed: HPA min replicas 3 → 1
  • Historical data: Single replica led to 2 incidents
  • Pattern: Low replica count = higher failure rate
Alert: “Configuration drift detected - increased incident risk”

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3. Faster Resolution

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Learning from Success:

Incident TypeFirst OccurrenceAfter 10 SimilarAfter 50 Similar
OOMKilled15min (investigation)2min (memory recall)30sec (auto-fix)
ImagePullBackOff10min (manual)1min (pattern match)15sec (auto-fix)
CrashLoopBackOff25min (debugging)5min (context from memory)2min (known fix)

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Memory accelerates everything.

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Privacy & Data Retention

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What’s NOT Stored

The Memory Engine is designed with privacy-first principles:Never Stored: - Application logs containing business data
  • Secrets, passwords, API keys
  • Customer PII or sensitive information
  • Code or container contents
  • Environment variables with secrets
Only Stored: - Kubernetes metadata (pod names, namespaces)
  • Resource metrics (CPU, memory, network)
  • Event data (pod crashed, deployment updated)
  • Configuration changes (resource limits changed)
  • Incident timelines (what happened when)

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Retention Policy

Data TypeRetention PeriodPurpose
Hot Memory30 daysFast pattern matching
Warm Memory90 daysTrend analysis
Cold Memory1 yearLong-term patterns
Archived2 yearsCompliance and deep analysis
After retention expires, data is automatically purged.

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Memory Engine in Action

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Example: Preventing a Friday Outage

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Thursday 3pm:

Memory Engine: "Analyzing deployment pipeline..."

Pattern detected:
- Deployments on Friday afternoon: 12 total
- Incidents within 4 hours: 5 (42% failure rate)
- Monday-Thursday deployments: 48 total  
- Incidents within 4 hours: 2 (4% failure rate)

Insight: Friday deployments 10x riskier

Recommendation: Postpone v3.1.0 deployment to Monday
              OR: Deploy to canary environment first
              OR: Ensure full SRE coverage on-call

Risk saved: High probability of weekend incident
This is institutional knowledge in action.

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Interfacing with Memory

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Query the Memory Engine

You can ask the Memory Engine questions:
  • Via Chat
  • Via Dashboard
  • Via API
You: "Have we seen this error before?"

SRI Agent: "Yes, this 'connection refused' error occurred 
3 times in the last month:

1. Sept 15: Fixed by restarting redis-master (2min)
2. Sept 22: Fixed by scaling redis replicas (5min)
3. Sept 29: Fixed by increasing redis memory (3min)

All occurred during high traffic (>1000 req/s).

Suggest: Scale redis replicas preemptively during traffic spikes."

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Advanced Memory Features

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Cross-Cluster Learning (Coming Soon)

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Shared Intelligence Across Your Organization:

  • Incident in prod-us-east → Learned by prod-eu-west
  • Fix discovered in one cluster → Available to all clusters
  • Patterns recognized faster with more data
  • Best practices propagate automatically
Privacy: Only anonymized patterns are shared, never sensitive data.

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Memory-Driven Automation

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Auto-Tuning Based on History:

Memory learns optimal scaling parameters:
  • Traffic pattern: Spike at 9am daily
  • Historical response: Manual scale-up at 8:55am
  • Memory action: Auto-scale at 8:50am preemptively
Result: Zero impact from predictable load
Memory tracks actual usage vs limits:
  • Pod never uses >400Mi but limit is 1Gi (wasted)
  • OR: Pod frequently hits limit and gets OOMKilled
Suggestion: Right-size based on 95th percentile usage + 20% buffer
Memory recognizes leading indicators:
  • Pattern: API latency >200ms precedes crash 90% of time
  • Current: Latency at 180ms and rising
Alert: “Potential crash in ~15 minutes. Remediate now?”

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Memory vs Traditional Systems

CapabilityTraditional RunbooksWiki/ConfluenceMemory Engine
UpdatesManualManualAutomatic
SearchKeyword onlyKeyword onlySemantic + pattern matching
ContextStatic docsStatic docsLive, evolving context
PatternsHumans identifyHumans identifyAI identifies automatically
AccessibilityDuring business hours24/7 but passive24/7 and proactive
AccuracyOutdated quicklyOutdated quicklyAlways current
IntegrationSeparate toolSeparate toolNative to SRI system

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Benefits of the Memory Engine

Faster Incident Resolution

First occurrence: 15 minutes to diagnose
** 10th occurrence:** 30 seconds (memory recall)** 91% time saving**

Improved SRE Onboarding

Before: 6 months to become effective With Memory: 2 weeks (context in every RCA)New hires productive immediately

Reduced Repeat Incidents

Traditional: 30% of incidents are repeats
With Memory: 5% repeats (caught early)** 83% reduction in déjà vu**

Institutional Continuity

Employee turnover: Knowledge stays
Team changes: Continuity maintainedZero knowledge loss

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Practical Examples

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Example 1: The Mysterious 2am Crash

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Initial Incident (Week 1):

2:15 AM: API gateway crashes
Investigation: 45 minutes
Root cause: Database connection pool exhaustion
Fix: Increase pool size 50 → 100
Memory stored:

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Second Occurrence (Week 3):

2:12 AM: API gateway showing high latency
Memory Engine: "Pattern match! Similar to Oct 3 incident"
Alert: "Database pool usage at 92% - crash imminent"
Auto-fix proposed: Scale pool to 150 (based on current load)
Result: Incident prevented
Time: 90 seconds from detection to fix
No 2am wake-up call. Memory saved the day (and the night).

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Example 2: The Learning Curve

Month 1: OOMKilled incident
  • Manual investigation: 20 minutes
  • Fix applied: Increase memory
  • Memory stores: Symptoms, cause, fix
Month 2: Similar OOMKilled
  • Memory recalls: Previous incident
  • Suggests fix: Increase memory (90% confidence)
  • SRE reviews and approves: 2 minutes
Month 3: Third OOMKilled
  • Memory recognizes: Known pattern
  • Auto-applies fix: Memory increased
  • Verifies success: Monitoring metrics
  • Total time: 30 seconds
  • Human involvement: None (reviewed in morning)

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This is learning in action.

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Memory Health and Maintenance

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Ensuring Memory Quality

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Quality Control Mechanisms:

1.Human Feedback Loop - SREs can mark incidents as “not helpful” 2.Success Verification - Track if suggested fixes actually worked 3.Confidence Scoring - Low-confidence memories flagged for review 4.Automatic Pruning - Obsolete patterns removed (e.g., after major architecture changes)
Yes! Memory management tools allow you to:
  • Archive specific incidents
  • Mark patterns as “ignore”
  • Reset memory for specific services/namespaces
  • Export memory for migration
Access:Settings → System → Memory Management

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Surprisingly little:

  • Average incident: ~50KB (metadata + context)
  • 1000 incidents: ~50MB
  • Patterns/graphs: ~100MB
Total after 1 year: Typically less than 500MBMemory is efficient by design.

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Memory-Powered Insights

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Dashboard Views

The Memory Engine powers several dashboard features:
RubixKube Dashboard powered by Memory Engine
What you see: - ** What you see:** - RCA reports generated from memory
  • Active Insights - Issues detected using learned patterns
  • Trend Predictions - Forecasts based on historical data

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Frequently Asked Questions

Currently: Each cluster has its own memory (isolated).Coming Soon (Q1 2026): Cross-cluster learning where:
  • Patterns from prod inform staging
  • Multi-cluster deployments share knowledge
  • Organization-wide best practices emerge

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Memory adapts:

  • Gradual changes: Memory updates naturally
  • Major migrations: Mark a “reset point” to start fresh
  • Architecture shifts: Old patterns deprecate automatically
You can always reset memory if needed (Settings → System).
Yes! For migrations or backups:
# Export memory to JSON
rubixkube memory export --output memory-backup.json

# Import into new cluster
rubixkube memory import --input memory-backup.json
Useful when moving between environments or disaster recovery.

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Feedback mechanism:

  1. Memory suggests a fix
  2. You mark it as “not helpful” or “made things worse”
  3. Memory records: This pattern → This fix = Bad outcome
  4. Future: Won’t suggest that fix for that pattern
Memory learns from mistakes too.

What is SRI?

Understand Site Reliability Intelligence

Agent Mesh

How agents use memory to collaborate

Guardrails

Safety mechanisms for memory-driven actions

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Next Steps

See Memory in Action

Watch a real scenario where memory prevents an outage

Start Building Your Memory

Install RubixKube and begin capturing knowledge