From prompts to production-grade supply chain intelligence.
Monday: 3 core prompts for risk detection, supplier analysis, and disruption forecasting. Tuesday: automated code. Wednesday: team workflows. Thursday: complete technical architecture. Multi-agent orchestration, ML pipelines, real-time monitoring, and enterprise-grade resilience.
Key Assumptions
System Requirements
Functional
- Ingest supplier data, shipment tracking, inventory levels, weather, news
- Multi-agent risk analysis: demand forecasting, supplier health, route optimization
- Real-time alerting for critical disruptions (port closures, supplier failures)
- Scenario planning: simulate disruptions, recommend mitigations
- Integration with ERP (SAP, Oracle), TMS (BluJay, MercuryGate), WMS (Manhattan)
- Dashboard for operations, procurement, and executive teams
- Audit trail for all risk decisions and agent actions
Non-Functional (SLOs)
💰 Cost Targets: {"per_supplier_per_month_usd":2.5,"per_operation_usd":0.08}
Agent Layer
planner
L4Decomposes complex supply chain queries into subtasks, selects appropriate executor agents
🔧 query_parser, task_decomposer, agent_selector
⚡ Recovery: If parsing fails → fallback to simple risk query, If no suitable executor → route to human operator
risk_executor
L3Analyzes supplier risk using ML models and historical data
🔧 ml_inference_api, supplier_db_query, news_sentiment_api
⚡ Recovery: If ML API down → use rule-based fallback, If confidence < 0.6 → flag for human review
forecasting_executor
L3Predicts demand and supply disruptions using time-series models
🔧 prophet_model, lstm_model, external_data_api (weather, events)
⚡ Recovery: If model fails → use moving average, If data incomplete → use regional averages
optimization_executor
L3Recommends route and inventory optimizations
🔧 route_optimizer (OR-Tools), inventory_optimizer, cost_calculator
⚡ Recovery: If optimizer times out → return top 3 heuristic solutions, If infeasible → relax constraints incrementally
evaluator
L2Validates outputs from executor agents for quality and consistency
🔧 consistency_checker, outlier_detector, cross_validator
⚡ Recovery: If validation fails → flag for human review, If quality_score < 0.7 → request re-execution
guardrail
L1Enforces policy checks, PII redaction, safety filters before external actions
🔧 pii_detector, policy_engine, audit_logger
⚡ Recovery: If PII detection fails → block output, If policy unclear → default to deny + escalate
ML Layer
Feature Store
Update: Batch (daily) + Streaming (critical features every 1h)
- • supplier_financial_health (updated daily)
- • shipment_delay_history (7d, 30d, 90d rolling)
- • geopolitical_risk_index (by region, updated hourly)
- • weather_disruption_score (updated 6h)
- • news_sentiment (supplier-specific, updated 1h)
- • inventory_turnover_rate (updated daily)
- • demand_volatility (30d rolling)
Model Registry
Strategy: Semantic versioning (major.minor.patch), immutable artifacts in S3
- • supplier_risk_classifier
- • demand_forecaster
- • route_optimizer
Observability Stack
Real-time monitoring, tracing & alerting
0 activeDeployment Variants
Startup Architecture
Fast to deploy, cost-efficient, scales to 100 competitors
Infrastructure
Risks & Mitigations
⚠️ LLM hallucinations lead to false risk alerts
Medium✓ Mitigation: 4-layer hallucination detection (confidence, cross-reference, logic, human review). Target: <1% false positive rate.
⚠️ ERP/TMS API rate limits block data ingestion
High✓ Mitigation: Implement exponential backoff, request rate limiting, cache aggressively. Negotiate higher API limits with vendors.
⚠️ Model drift: accuracy degrades over time
High✓ Mitigation: Automated drift detection (weekly), retraining pipeline (monthly), A/B testing new models. Alert if accuracy drops >5%.
⚠️ Multi-tenancy data leak between customers
Low✓ Mitigation: VPC isolation per tenant, row-level security in DB, audit all cross-tenant queries. Penetration testing quarterly.
⚠️ Agent autonomy causes unintended actions
Medium✓ Mitigation: Guardrail agent enforces policies, human-in-the-loop for critical actions (>$10K impact), audit trail for all agent decisions.
⚠️ Cost overrun from LLM API usage
Medium✓ Mitigation: Set per-operation cost limits ($0.08/op), use smaller models for simple tasks, cache responses, monitor spend daily.
⚠️ Regulatory compliance (GDPR, data residency)
Low✓ Mitigation: Multi-region architecture, data residency enforcement, PII redaction, annual compliance audits (SOC2, ISO 27001).
Evolution Roadmap
Progressive transformation from MVP to scale
Phase 1: MVP (0-3 months)
Phase 2: Scale (3-6 months)
Phase 3: Enterprise (6-12 months)
Complete Systems Architecture
9-layer architecture: Presentation → API Gateway → Agent Layer → ML Layer → Integration → Data → External → Observability → Security
Presentation
5 components
API Gateway
5 components
Agent Layer
7 components
ML Layer
7 components
Integration
6 components
Data
6 components
External
6 components
Observability
6 components
Security
6 components
Sequence Diagram - Risk Detection Flow
Automated data flow every hour
Data Flow - End-to-End
From supplier event to risk alert in 15 minutes
Key Integrations
ERP Integration (SAP/Oracle)
TMS Integration (BluJay/MercuryGate)
WMS Integration (Manhattan/HighJump)
Weather API (OpenWeather)
News Feed (NewsAPI/Bloomberg)
Security & Compliance
Failure Modes & Fallbacks
| Failure | Fallback | Impact | SLA |
|---|---|---|---|
| LLM API down (OpenAI/Anthropic outage) | Switch to backup LLM provider (Gemini) → If all down, use rule-based risk scoring | Degraded quality, not broken | 99.5% |
| ML model inference timeout (>5s) | Use cached risk score (if <24h old) → Else, use historical average | Slightly stale data | 99.0% |
| ERP API unavailable | Use cached supplier data → Queue updates for later sync | Eventual consistency (up to 1h delay) | 99.0% |
| Database connection pool exhausted | Reject new requests with 503 → Scale read replicas | Temporary unavailability | 99.5% |
| Agent executor returns low confidence (<0.6) | Flag for human review → Do not auto-alert | Reduced automation, quality maintained | 100% (no false alerts) |
| Feature store data stale (>1h) | Use last known good features → Alert data engineering | Slightly degraded accuracy | 99.0% |
RAG vs Fine-Tuning
Hallucination Detection
Evaluation Framework
Dataset Curation
Agentic RAG
Multi-Model Ensemble
Tech Stack Summary
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