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Crisis Response System Architecture πŸ—οΈ

From 10 alerts/day to 10,000 events/hour with AI orchestration

July 24, 2025
🚨 Crisis ManagementπŸ—οΈ System ArchitectureπŸ“Š Real-TimeπŸ€– Multi-Agent

From monitoring to coordinated response in seconds.

Monday: 3 crisis prompts. Tuesday: automated monitoring code. Wednesday: team coordination workflows. Thursday: complete production architecture. Real-time ingestion, AI-powered triage, automated escalation, and enterprise-scale deployment patterns.

Key Assumptions

  • β€’Monitor 5-50 data sources (social media, news, internal systems)
  • β€’Handle 10-10,000 events per hour depending on crisis severity
  • β€’Real-time processing (< 60s from detection to alert)
  • β€’Multi-tenant with org-level isolation
  • β€’Compliance: SOC2, ISO 27001, data residency requirements
  • β€’24/7 availability with < 5 min RTO for critical path

System Requirements

Functional

  • Ingest from Twitter, Reddit, news APIs, internal logs, customer support tickets
  • Classify events by severity (P0-P4) and category (PR, security, operational, legal)
  • Generate context-aware alerts with recommended actions
  • Route to appropriate stakeholders based on severity + domain
  • Track response progress and escalate if SLA breached
  • Provide real-time dashboard for crisis command center
  • Archive all events and decisions for post-mortem analysis

Non-Functional (SLOs)

latency p95 ms3000
freshness min1
availability percent99.9
alert delivery sec10
false positive rate0.05

πŸ’° Cost Targets: {"per_event_usd":0.002,"per_org_month_usd":500,"ml_inference_cost_per_1k":0.15}

Agent Layer

planner

L3

Decomposes crisis response into subtasks, selects tools

πŸ”§ stakeholder_lookup, escalation_policy_retriever, historical_response_search

⚑ Recovery: If tool fails: use default escalation policy, If plan generation times out: route to manual queue

monitoring

L2

Ingests events from all sources, normalizes, deduplicates

πŸ”§ text_normalizer, deduplicator (fuzzy hash), entity_extractor

⚑ Recovery: If normalization fails: log raw event, flag for manual review, If deduplication fails: assume unique, process anyway

classification

L3

Classifies severity (P0-P4) and category (PR/security/ops/legal)

πŸ”§ severity_classifier (fine-tuned BERT), category_classifier (GPT-4), sentiment_analyzer, anomaly_detector

⚑ Recovery: If confidence < 0.7: escalate to human review, If model API down: use rule-based fallback classifier

orchestrator

L4

Routes alerts, manages escalation, tracks SLAs

πŸ”§ stakeholder_router, sla_tracker, escalation_engine, communication_dispatcher

⚑ Recovery: If routing fails: broadcast to all stakeholders for severity P0-P1, If SLA breach detected: auto-escalate to next tier

evaluator

L3

Validates classification quality, checks for false positives

πŸ”§ confusion_matrix_calculator, feedback_aggregator, drift_detector

⚑ Recovery: If ground truth unavailable: use stakeholder feedback as proxy, If drift detected: trigger model retraining workflow

guardrail

L4

PII redaction, policy checks, safety filters

πŸ”§ pii_detector (AWS Comprehend/Presidio), policy_engine, toxicity_classifier

⚑ Recovery: If PII detection fails: block processing, log error, If policy violation detected: halt alert, notify admin

escalation

L3

Manages escalation tiers, retries, fallback communication

πŸ”§ notification_dispatcher (Slack/SMS/Email), pagerduty_api, retry_scheduler

⚑ Recovery: If primary channel fails: fallback to SMS/email, If no ACK after 5 min: escalate to next tier

ML Layer

Feature Store

Update: Real-time (streaming) for velocity/sentiment, batch (hourly) for historical patterns

  • β€’ event_velocity (events/min by source)
  • β€’ sentiment_score (rolling 1hr avg)
  • β€’ keyword_frequency (top 50 terms)
  • β€’ entity_mentions (orgs, people, products)
  • β€’ historical_severity_distribution
  • β€’ time_of_day_pattern
  • β€’ source_credibility_score

Model Registry

Strategy: Semantic versioning, canary deployment (10% traffic for 24hr)

  • β€’ severity_classifier_v3
  • β€’ category_classifier_v2
  • β€’ anomaly_detector_v1

Observability

Metrics

  • πŸ“Š events_ingested_per_sec
  • πŸ“Š classification_latency_p95_ms
  • πŸ“Š alert_delivery_latency_sec
  • πŸ“Š escalation_rate
  • πŸ“Š false_positive_rate
  • πŸ“Š sla_breach_count
  • πŸ“Š llm_api_latency_ms
  • πŸ“Š llm_cost_per_event_usd
  • πŸ“Š agent_error_rate
  • πŸ“Š kafka_lag_sec

Dashboards

  • πŸ“ˆ ops_dashboard
  • πŸ“ˆ ml_dashboard
  • πŸ“ˆ cost_dashboard
  • πŸ“ˆ compliance_dashboard

Traces

βœ… Enabled

Deployment Variants

πŸš€ Startup

Infrastructure:

  • β€’ Serverless (Lambda/Cloud Functions)
  • β€’ Managed PostgreSQL (RDS/Cloud SQL)
  • β€’ Redis Cloud
  • β€’ Kafka Cloud (Confluent/AWS MSK)
  • β€’ LLM APIs (Claude/GPT via API)

β†’ Single-tenant per org

β†’ Auto-scaling to zero when idle

β†’ Shared infrastructure (cost-optimized)

β†’ Standard security (TLS, encryption at rest)

β†’ SOC2 Type I compliance

β†’ Target: 10-1,000 events/day, $200-800/month

🏒 Enterprise

Infrastructure:

  • β€’ Kubernetes (EKS/GKE) multi-region
  • β€’ Private VPC with peering
  • β€’ Dedicated database instances
  • β€’ Kafka clusters per region
  • β€’ Private LLM endpoints (Azure OpenAI/AWS Bedrock)
  • β€’ HSM for key management (AWS CloudHSM)

β†’ Multi-tenant with org-level isolation

β†’ Data residency per customer (EU/US/APAC)

β†’ SSO/SAML integration

β†’ Custom SLAs (99.9% or 99.99%)

β†’ SOC2 Type II + ISO 27001

β†’ Dedicated support + on-call

β†’ Target: 1,000-10,000+ events/hour, $3K-10K+/month

πŸ“ˆ Migration: Start with startup tier. At 1K events/day or $5K MRR, migrate to enterprise: (1) Provision private VPC, (2) Migrate data to dedicated DB, (3) Deploy K8s cluster, (4) Cut over DNS, (5) Decommission serverless. Estimated migration time: 2-4 weeks.

Risks & Mitigations

⚠️ False negatives (miss critical crisis)

Medium

βœ“ Mitigation: Optimize for recall (98%+), human review for low-confidence P0/P1, post-incident analysis to tune model

⚠️ False positives (alert fatigue)

High

βœ“ Mitigation: Precision tuning (93%+), tiered alerting (P4 = email only), stakeholder feedback loop to improve model

⚠️ LLM hallucinations (fake stakeholders, bad advice)

Medium

βœ“ Mitigation: Guardrail Agent with 4-layer validation, cross-reference all entities, human review for P0/P1

⚠️ Data breach (PII exposure)

Low

βœ“ Mitigation: PII redaction before LLM, encryption at rest/in transit, audit all access, SOC2 compliance

⚠️ Vendor lock-in (single LLM provider)

Medium

βœ“ Mitigation: Multi-provider architecture (Claude + GPT + BERT), standardized prompt interface, easy model swapping

⚠️ Cost overrun (LLM API costs spike)

High

βœ“ Mitigation: Cost guardrails ($500/day limit), tiered model routing (rules β†’ BERT β†’ GPT), caching, batch processing

⚠️ Scalability bottleneck (Kafka lag, DB overload)

Medium

βœ“ Mitigation: Horizontal scaling (K8s auto-scale), read replicas, Kafka partitioning, load testing (10x expected volume)

Evolution Roadmap

1

Phase 1: MVP (0-3 months)

Q3 2025
  • β†’ Deploy serverless architecture
  • β†’ Integrate 3 data sources (Twitter, Reddit, NewsAPI)
  • β†’ Achieve 90% classification accuracy
  • β†’ Onboard 5 pilot customers
2

Phase 2: Scale (3-6 months)

Q4 2025
  • β†’ Scale to 1,000 events/day
  • β†’ Add Response Orchestrator + Escalation Agent
  • β†’ Achieve 94% classification accuracy
  • β†’ Onboard 20 customers
3

Phase 3: Enterprise (6-12 months)

Q1-Q2 2026
  • β†’ Scale to 10,000 events/hour
  • β†’ Multi-region deployment (US + EU)
  • β†’ Enterprise features (SSO, data residency, custom SLAs)
  • β†’ 50+ enterprise customers

Complete Systems Architecture

9-layer architecture from ingestion to response

Presentation
Crisis Dashboard (React)
Mobile Alert App (React Native)
Slack/Teams Bot
Email Gateway
API Gateway
Load Balancer (ALB/CloudFlare)
Rate Limiter (Kong/Tyk)
Auth Gateway (Auth0/Cognito)
API Versioning
Agent Layer
Monitoring Agent
Classification Agent
Response Orchestrator
Escalation Agent
Planner Agent
Evaluator Agent
Guardrail Agent
ML Layer
Feature Store (Feast/Tecton)
Model Registry (MLflow)
Severity Classifier (fine-tuned BERT)
Sentiment Analyzer
Entity Extractor (NER)
Anomaly Detector (Isolation Forest)
Integration
Social Listening Adapter
News API Connector
Internal Log Aggregator
Ticketing System Bridge
Communication Hub (Twilio/SendGrid)
Data
Time-Series DB (InfluxDB/TimescaleDB)
Event Store (PostgreSQL)
Vector DB (Pinecone/Weaviate)
Cache (Redis)
Archive (S3/GCS)
External
Twitter API
Reddit API
NewsAPI
LLM APIs (Claude/GPT)
Slack/Teams APIs
PagerDuty/Opsgenie
Observability
Metrics (Prometheus/Datadog)
Logs (ELK/Loki)
Traces (Jaeger/Tempo)
Dashboards (Grafana)
Alerting (PagerDuty)
Security
IAM (AWS IAM/Okta)
Secrets Manager (Vault/KMS)
WAF (CloudFlare/AWS WAF)
Audit Logger
PII Redactor

Sequence Diagram - Crisis Detection Flow

Social MediaIngestion APIMonitoring AgentClassification AgentOrchestratorEscalation AgentStakeholderPOST /events (tweet, post, article)enqueue(raw_event)classify(event_text, metadata){severity: P1, category: PR, confidence: 0.92}route(P1, PR) β†’ CMO + PR TeamSend alert via Slack + SMS + EmailACK + assign response teamlog(event, classification, response)

Crisis Response System - Agent Orchestration

7 Components
[Event]Raw crisis events[RPC]Plan request[Event]Task plan[RPC]Classify event[Event]Severity & category[RPC]Validate content[Event]Sanitized data[RPC]Quality check[Event]Validation results[RPC]Escalate alert[Event]Escalation status[Event]Feedback signalResponse Orchestrator4 capabilitiesPlanner Agent3 capabilitiesMonitoring Agent4 capabilitiesClassification Agent4 capabilitiesEvaluator Agent4 capabilitiesGuardrail Agent4 capabilitiesEscalation Agent4 capabilities
HTTP
REST
gRPC
Event
Stream
WebSocket

Crisis Response System - External Integrations

9 Components
[WebSocket]Live event stream[Event]System alerts[REST]Incident sync[REST]Create tickets[Event]Alert notifications[WebSocket]Real-time queries[WebSocket]Live updates[REST]Fetch runbooks[Event]Audit events[Kafka]Event telemetry[HTTP]User responsesCore Crisis System4 capabilitiesSocial Media Streams4 capabilitiesInternal Monitoring4 capabilitiesTicketing Systems4 capabilitiesCommunication Channels4 capabilitiesCrisis Dashboard4 capabilitiesKnowledge Base4 capabilitiesCompliance Systems4 capabilitiesAnalytics Platform4 capabilities
HTTP
REST
gRPC
Event
Stream
WebSocket

Data Flow - Crisis Detection to Response

End-to-end flow from social post to stakeholder alert

1
Social Media / News API0s
Publishes event β†’ Raw JSON (tweet, article, log entry)
2
Ingestion API0.05s
Receives via webhook β†’ HTTP POST
3
Monitoring Agent0.15s
Normalizes + deduplicates β†’ Normalized event JSON
4
Guardrail Agent0.45s
Redacts PII, checks policies β†’ Sanitized event
5
Classification Agent1.95s
Classifies severity + category β†’ {severity: P1, category: PR, confidence: 0.92}
6
Planner Agent2.75s
Generates response plan β†’ Array of steps + stakeholders
7
Response Orchestrator2.85s
Routes to stakeholders β†’ Routing decision
8
Escalation Agent3.75s
Sends alerts (Slack + SMS + Email) β†’ Multi-channel notifications
9
Event Store3.85s
Logs event + classification + response β†’ Audit record
10
Evaluator Agent4.05s
Validates classification (async) β†’ Quality metrics

Scaling Patterns

Volume
10-100 events/day
Pattern
Serverless + Managed Services
Architecture
β€’ API Gateway (AWS/GCP)
β€’ Lambda/Cloud Functions
β€’ Managed PostgreSQL
β€’ Redis Cloud
β€’ LLM APIs (Claude/GPT)
Cost
$200/month
3-5s p95
Volume
100-1,000 events/day
Pattern
Queue + Worker Pool
Architecture
β€’ Load balancer
β€’ API servers (2-4 instances)
β€’ Kafka/RabbitMQ
β€’ Worker pool (5-10 instances)
β€’ PostgreSQL (read replicas)
β€’ Redis Cluster
Cost
$800/month
2-4s p95
Volume
1,000-10,000 events/hour
Pattern
Multi-Agent Orchestration
Architecture
β€’ Global load balancer
β€’ Kubernetes cluster
β€’ Kafka (3+ brokers)
β€’ Agent services (auto-scaled)
β€’ TimescaleDB (time-series)
β€’ Vector DB (Pinecone)
β€’ Multi-LLM routing
Cost
$3,000/month
1-3s p95
Volume
10,000+ events/hour
Pattern
Enterprise Multi-Region
Architecture
β€’ Multi-region K8s (3+ regions)
β€’ Kafka clusters per region
β€’ Replicated databases
β€’ Edge caching (CloudFlare)
β€’ Dedicated LLM endpoints
β€’ Private networking (VPC peering)
Cost
$10,000+/month
< 1s p95

Key Integrations

Social Listening (Twitter, Reddit)

Protocol: REST APIs + Webhooks
Register webhooks for keyword/hashtag streams
Receive events in real-time
Normalize to internal schema
Enqueue for processing

News APIs (NewsAPI, Google News)

Protocol: REST APIs
Poll every 5-15 min for org-related articles
Extract headline + summary + sentiment
Deduplicate against existing events
Route to classification

Internal Logs (ELK, Splunk)

Protocol: HTTP Event Collector / Logstash
Stream error logs, security alerts, operational metrics
Filter for anomalies (rate spikes, error bursts)
Enrich with context (service, region, user)
Trigger internal crisis events

Communication Hub (Slack, Teams, Email, SMS)

Protocol: Slack API, Graph API, SMTP, Twilio
Orchestrator generates alert payload
Escalation Agent dispatches to channels
Track delivery + read receipts
Retry on failure (3x with backoff)

Incident Management (PagerDuty, Opsgenie)

Protocol: REST API + Webhooks
Create incident on P0/P1 alerts
Sync escalation policies
Receive ACK/resolve webhooks
Close loop with audit log

Security & Compliance

Failure Modes & Fallbacks

FailureFallbackImpactSLA
LLM API down (Claude/GPT)Route to rule-based classifier (regex + keyword matching), flag for human reviewDegraded accuracy (80% vs 94%), but no downtime99.5%
Classification confidence < 0.7Route to human review queue, send low-confidence alert to analystsDelayed response (manual review takes 5-15 min)99.9%
Kafka broker downBuffer events in Redis, replay when Kafka recoversIncreased latency (up to 5 min), no data loss99.9%
Database unavailableRead from replica, write to queue for later replayRead-only mode, delayed writes99.5%
Notification delivery fails (Slack/SMS)Retry 3x with exponential backoff, escalate to next channel (SMS β†’ Email)Delayed alert (up to 2 min)99.0%
PII detection service downBlock all processing, log error, alert adminSystem unavailable until service recovers100% (safety first)
SLA breach (no ACK after 5 min)Auto-escalate to next tier (e.g., P1 β†’ CEO), broadcast to all stakeholdersIncreased noise, but ensures critical events handled99.9%

Advanced ML/AI Patterns

Production ML engineering beyond basic API calls

RAG vs Fine-Tuning

Hallucination Detection

LLMs hallucinate stakeholder names, fake incident IDs, speculative legal advice
L1
Confidence thresholding (< 0.7 = flag)
L2
Cross-reference stakeholder database (reject unknown names)
L3
Logical consistency checks (e.g., P0 crisis cannot have 'low urgency' in reasoning)
L4
Human review for P0/P1 alerts
0.8% hallucination rate, 100% caught by L2/L3 checks

Evaluation Framework

Classification Accuracy
94.2%target: 94%+
Recall (detect all real crises)
98.7%target: 98%+
Precision (minimize false positives)
93.1%target: 92%+
False Positive Rate
4.2%target: < 5%
Time to Detection
42s p95target: < 60s
Testing: Shadow mode: 500 real crises run through system in parallel with manual process. Compare outcomes, tune thresholds.

Dataset Curation

1
Collect: 50K crisis events from news archives, social media, internal logs - Scraping + API pulls
2
Clean: 42K usable (removed duplicates, spam, non-English) - Fuzzy deduplication + language detection
3
Label: 10K labeled by crisis experts (severity + category) - ($$50K (5 annotators, 2 weeks))
4
Augment: +5K synthetic examples (edge cases, rare categories) - GPT-4 generation + human validation
β†’ 15K high-quality training examples, 3K validation, 2K test set

Agentic RAG

Agent iteratively retrieves context based on reasoning chain
Event mentions 'data breach' β†’ RAG retrieves legal response templates β†’ Agent reasons 'need to check if EU customers affected' β†’ RAG retrieves GDPR notification requirements β†’ Response plan includes GDPR-specific steps
πŸ’‘ Not one-shot retrieval. Agent decides what additional context it needs at each reasoning step.

Multi-Model Ensemble

Tech Stack Summary

LLMs
Claude 3.5 Sonnet (primary), GPT-4 (fallback), fine-tuned BERT (severity classifier)
Orchestration
LangGraph (agent framework), custom state machine (Go) for routing
Database
PostgreSQL (events, alerts), TimescaleDB (time-series metrics), Redis (cache + queue)
Message Queue
Kafka (event streaming), RabbitMQ (task queue), SQS (serverless fallback)
Vector DB
Pinecone (managed) or Weaviate (self-hosted)
Compute
Kubernetes (production), Lambda (serverless startup), ECS (hybrid)
Monitoring
Prometheus + Grafana (metrics), ELK Stack (logs), Jaeger (traces), Datadog (unified)
Security
Auth0 (OIDC), AWS Secrets Manager (secrets), CloudFlare WAF, AWS Comprehend (PII)
CI/CD
GitHub Actions (CI), ArgoCD (CD), Terraform (IaC)
🚨

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