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)
π° Cost Targets: {"per_event_usd":0.002,"per_org_month_usd":500,"ml_inference_cost_per_1k":0.15}
Agent Layer
planner
L3Decomposes crisis response into subtasks, selects tools
π§ stakeholder_lookup, escalation_policy_retriever, historical_response_search
β‘ Recovery: If tool fails: use default escalation policy, If plan generation times out: route to manual queue
monitoring
L2Ingests events from all sources, normalizes, deduplicates
π§ text_normalizer, deduplicator (fuzzy hash), entity_extractor
β‘ Recovery: If normalization fails: log raw event, flag for manual review, If deduplication fails: assume unique, process anyway
classification
L3Classifies severity (P0-P4) and category (PR/security/ops/legal)
π§ severity_classifier (fine-tuned BERT), category_classifier (GPT-4), sentiment_analyzer, anomaly_detector
β‘ Recovery: If confidence < 0.7: escalate to human review, If model API down: use rule-based fallback classifier
orchestrator
L4Routes alerts, manages escalation, tracks SLAs
π§ stakeholder_router, sla_tracker, escalation_engine, communication_dispatcher
β‘ Recovery: If routing fails: broadcast to all stakeholders for severity P0-P1, If SLA breach detected: auto-escalate to next tier
evaluator
L3Validates classification quality, checks for false positives
π§ confusion_matrix_calculator, feedback_aggregator, drift_detector
β‘ Recovery: If ground truth unavailable: use stakeholder feedback as proxy, If drift detected: trigger model retraining workflow
guardrail
L4PII redaction, policy checks, safety filters
π§ pii_detector (AWS Comprehend/Presidio), policy_engine, toxicity_classifier
β‘ Recovery: If PII detection fails: block processing, log error, If policy violation detected: halt alert, notify admin
escalation
L3Manages escalation tiers, retries, fallback communication
π§ notification_dispatcher (Slack/SMS/Email), pagerduty_api, retry_scheduler
β‘ 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
- π events_ingested_per_sec
- π classification_latency_p95_ms
- π alert_delivery_latency_sec
- π escalation_rate
- π false_positive_rate
- π sla_breach_count
- π llm_api_latency_ms
- π llm_cost_per_event_usd
- π agent_error_rate
- π kafka_lag_sec
Dashboards
- π ops_dashboard
- π ml_dashboard
- π cost_dashboard
- π compliance_dashboard
Traces
β Enabled
Deployment Variants
π 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
π’ 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
π 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
β οΈ False negatives (miss critical crisis)
Mediumβ Mitigation: Optimize for recall (98%+), human review for low-confidence P0/P1, post-incident analysis to tune model
β οΈ False positives (alert fatigue)
Highβ Mitigation: Precision tuning (93%+), tiered alerting (P4 = email only), stakeholder feedback loop to improve model
β οΈ LLM hallucinations (fake stakeholders, bad advice)
Mediumβ Mitigation: Guardrail Agent with 4-layer validation, cross-reference all entities, human review for P0/P1
β οΈ Data breach (PII exposure)
Lowβ Mitigation: PII redaction before LLM, encryption at rest/in transit, audit all access, SOC2 compliance
β οΈ Vendor lock-in (single LLM provider)
Mediumβ Mitigation: Multi-provider architecture (Claude + GPT + BERT), standardized prompt interface, easy model swapping
β οΈ Cost overrun (LLM API costs spike)
Highβ Mitigation: Cost guardrails ($500/day limit), tiered model routing (rules β BERT β GPT), caching, batch processing
β οΈ Scalability bottleneck (Kafka lag, DB overload)
Mediumβ Mitigation: Horizontal scaling (K8s auto-scale), read replicas, Kafka partitioning, load testing (10x expected volume)
Evolution Roadmap
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
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
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
Sequence Diagram - Crisis Detection Flow
Crisis Response System - Agent Orchestration
7 ComponentsCrisis Response System - External Integrations
9 ComponentsData Flow - Crisis Detection to Response
End-to-end flow from social post to stakeholder alert
Scaling Patterns
Key Integrations
Social Listening (Twitter, Reddit)
News APIs (NewsAPI, Google News)
Internal Logs (ELK, Splunk)
Communication Hub (Slack, Teams, Email, SMS)
Incident Management (PagerDuty, Opsgenie)
Security & Compliance
Failure Modes & Fallbacks
Failure | Fallback | Impact | SLA |
---|---|---|---|
LLM API down (Claude/GPT) | Route to rule-based classifier (regex + keyword matching), flag for human review | Degraded accuracy (80% vs 94%), but no downtime | 99.5% |
Classification confidence < 0.7 | Route to human review queue, send low-confidence alert to analysts | Delayed response (manual review takes 5-15 min) | 99.9% |
Kafka broker down | Buffer events in Redis, replay when Kafka recovers | Increased latency (up to 5 min), no data loss | 99.9% |
Database unavailable | Read from replica, write to queue for later replay | Read-only mode, delayed writes | 99.5% |
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 down | Block all processing, log error, alert admin | System unavailable until service recovers | 100% (safety first) |
SLA breach (no ACK after 5 min) | Auto-escalate to next tier (e.g., P1 β CEO), broadcast to all stakeholders | Increased noise, but ensures critical events handled | 99.9% |
Advanced ML/AI Patterns
Production ML engineering beyond basic API calls