Integration Scenarios

ARAL Integration Scenarios

Real-World Use Cases and Implementation Patterns

Version: 1.1.0
Status: Informative
Date: 2026-01-15

Overview

This document provides real-world integration scenarios demonstrating how ARAL-conformant agents can be deployed across various domains and use cases. Each scenario includes architecture patterns, security considerations, and implementation guidance.

Enterprise Knowledge Assistant
Multi-Agent Customer Service
Healthcare Clinical Decision Support
Financial Trading Agent
IoT Smart Home Orchestrator
DevOps Autonomous Operations
Legal Document Analysis
Educational Tutoring System
Supply Chain Optimization
Cross-Organization Agent Collaboration

1. Enterprise Knowledge Assistant

1.1 Scenario Overview

Domain: Enterprise knowledge management
Conformance Profile: ARAL-INTEROP (L1-L7)
Key Requirements: Security, privacy, multi-source integration

1.2 Architecture

┌─────────────────────────────────────────────────────────┐
│ L7: Protocol       REST API, MCP, Slack integration    │
├─────────────────────────────────────────────────────────┤
│ L6: Orchestration  Query routing, load balancing       │
├─────────────────────────────────────────────────────────┤
│ L5: Persona        "Enterprise Knowledge Agent"        │
│                    - Role: Information retrieval        │
│                    - Constraints: Internal docs only    │
├─────────────────────────────────────────────────────────┤
│ L4: Reasoning      RAG (Retrieval-Augmented Gen)       │
│                    - LLM: Claude 3.5 Sonnet            │
│                    - Embeddings: OpenAI text-embed-3    │
├─────────────────────────────────────────────────────────┤
│ L3: Capabilities   - Search knowledge base             │
│                    - Query SQL databases                │
│                    - Access Confluence/SharePoint       │
├─────────────────────────────────────────────────────────┤
│ L2: Memory         - Vector DB: Pinecone               │
│                    - Conversation history: PostgreSQL   │
│                    - Cache: Redis                       │
├─────────────────────────────────────────────────────────┤
│ L1: Runtime        Kubernetes cluster (Azure/AWS)      │
└─────────────────────────────────────────────────────────┘

1.3 Security Considerations

Authentication:

SSO integration (OIDC/SAML)
Service accounts for backend systems
JWT token validation

Authorization:

RBAC based on AD/LDAP groups
Document-level access control
Query result filtering by permissions

Data Protection:

TLS 1.3 for all communications
Encryption at rest (AES-256)
PII detection and redaction
Audit logging of all queries

1.4 Privacy Requirements

GDPR Compliance: Employee data anonymization
Retention: 90-day conversation history
Right to Erasure: Automated deletion workflows
Consent: Opt-in for analytics

1.5 Implementation Example

Persona Manifest:

{
  "persona": {
    "id": "enterprise-knowledge-assistant",
    "name": "Enterprise Knowledge Assistant",
    "version": "1.2.0",
    "role": "information_retrieval",
    "constraints": {
      "data_sources": ["internal_only"],
      "pii_handling": "redact",
      "max_response_time": "5s"
    },
    "capabilities": [
      {
        "id": "search_confluence",
        "type": "tool",
        "provider": "atlassian_api"
      },
      {
        "id": "query_database",
        "type": "tool",
        "provider": "sql_connector",
        "permissions": ["read_only"]
      }
    ],
    "memory": {
      "short_term": {
        "type": "redis",
        "ttl": "24h"
      },
      "long_term": {
        "type": "vector_db",
        "provider": "pinecone",
        "dimensions": 1536
      }
    },
    "security": {
      "authentication": "oauth2",
      "authorization": "rbac",
      "encryption": ["tls_1.3", "aes_256"]
    }
  }
}

1.6 Integration Pattern

1.7 Deployment Checklist

Deploy agent runtime on Kubernetes
Configure SSO integration
Set up vector database with company documents
Configure access control policies
Implement audit logging
Set up monitoring and alerting
Conduct security audit
Train employees on usage
Establish feedback loop

2. Multi-Agent Customer Service

2.1 Scenario Overview

Domain: Customer support automation
Conformance Profile: ARAL-ORCH (L1-L6)
Key Requirements: Scalability, multi-agent coordination, handoff

2.2 Multi-Agent Architecture

                    ┌─────────────────────┐
                    │  Orchestrator Agent │
                    │  (Router/Supervisor)│
                    └──────────┬──────────┘
                               │
          ┌────────────────────┼────────────────────┐
          │                    │                    │
    ┌─────▼─────┐        ┌────▼────┐        ┌─────▼─────┐
    │  Triage   │        │ Technical│        │ Billing   │
    │  Agent    │        │  Agent   │        │  Agent    │
    └───────────┘        └──────────┘        └───────────┘
         │                     │                    │
         └─────────────────────┴────────────────────┘
                               │
                        ┌──────▼───────┐
                        │ Human Handoff│
                        │   (Zendesk)  │
                        └──────────────┘

2.3 Agent Specializations

Triage Agent:

Persona: First-line support, intent classification
Capabilities: Language detection, sentiment analysis, routing
Decision Logic: Route to specialist or human

Technical Agent:

Persona: Technical troubleshooting
Capabilities: Knowledge base search, diagnostic flows, log analysis
Integrations: JIRA, GitHub, monitoring systems

Billing Agent:

Persona: Billing inquiries, subscription management
Capabilities: CRM lookup, payment processing, refund workflows
Integrations: Stripe, Salesforce

2.4 Orchestration Protocol

Message Envelope:

{
  "envelope": {
    "id": "msg-12345",
    "from": "triage-agent",
    "to": "technical-agent",
    "timestamp": "2026-01-15T14:30:00Z",
    "type": "handoff",
    "priority": "high",
    "context": {
      "customer_id": "cust-789",
      "conversation_id": "conv-456",
      "intent": "technical_issue",
      "sentiment": "frustrated",
      "history": [ ... ]
    },
    "payload": {
      "issue": "API returning 500 errors",
      "attempted_solutions": ["restart", "clear_cache"],
      "customer_tier": "enterprise"
    }
  }
}

2.5 Human Handoff Criteria

Automatic Escalation:

Confidence score < 0.7
Sentiment: angry/extremely negative
Request for human
Legal/compliance matters
VIP customer
Unresolved after 3 agent interactions

2.6 Metrics & Monitoring

KPIs:

First contact resolution rate
Average handling time
Customer satisfaction score (CSAT)
Agent confidence scores
Escalation rate
Cost per interaction

Observability:

Distributed tracing (OpenTelemetry)
Agent performance dashboards
Conversation flow analytics
Error rate monitoring

3. Healthcare Clinical Decision Support

3.1 Scenario Overview

Domain: Healthcare
Conformance Profile: ARAL-CORE (L1-L5)
Key Requirements: HIPAA compliance, safety, explainability

3.2 Regulatory Considerations

HIPAA Requirements:

✅ Encryption at rest and in transit
✅ Access controls and audit logs
✅ Business Associate Agreement (BAA)
✅ Breach notification procedures
✅ Minimum necessary principle

FDA Software as Medical Device (SaMD):

Clinical validation required
Risk classification
Quality management system (ISO 13485)
Post-market surveillance

3.3 Safety Architecture

┌─────────────────────────────────────────────────────────┐
│ Safety Layer:                                           │
│ - Human-in-the-loop (mandatory review)                 │
│ - Confidence thresholds                                 │
│ - Contraindication checks                               │
│ - Drug interaction database                             │
└─────────────────────────────────────────────────────────┘
         ▲
         │
┌────────┴────────────────────────────────────────────────┐
│ L5: Persona        "Clinical Decision Support Agent"    │
│                    - Role: Recommendation (not decision) │
│                    - Constraints: Evidence-based only    │
│                    - Disclaimers: Not a substitute       │
├─────────────────────────────────────────────────────────┤
│ L4: Reasoning      Medical knowledge reasoning          │
│                    - Guidelines: UpToDate, Cochrane     │
│                    - Evidence grading (A/B/C)           │
│                    - Differential diagnosis              │
├─────────────────────────────────────────────────────────┤
│ L3: Capabilities   - EHR integration (HL7 FHIR)        │
│                    - Medical literature search           │
│                    - Drug database lookup                │
└─────────────────────────────────────────────────────────┘

3.4 Explainability Requirements

Mandatory Explanations:

Evidence sources (studies, guidelines)
Confidence levels
Reasoning chain
Limitations and caveats
Alternative diagnoses considered

Example Output:

Recommendation: Consider prescribing metformin as first-line therapy for Type 2 Diabetes.

Evidence:
- ADA Guidelines 2023 (Grade A recommendation)
- UKPDS Study (N Engl J Med. 1998;339:229-234)
- Cochrane Review (2020): RR 0.87 for cardiovascular outcomes

Confidence: High (92%)

Contraindications to check:
- eGFR < 30 mL/min/1.73m² (renal impairment)
- Severe hepatic impairment
- Lactic acidosis history

Alternative considerations:
- GLP-1 agonist if weight loss desired
- SGLT2 inhibitor if heart failure present

⚠️ This is a clinical decision support tool. Final treatment decision must be made by licensed healthcare provider based on full clinical context.

3.5 Data Privacy

PHI Protection:

De-identification per HIPAA Safe Harbor method
Minimum necessary access
Audit logs for all PHI access
Patient consent for data use
Right to access and amendment

4. Financial Trading Agent

4.1 Scenario Overview

Domain: Financial services
Conformance Profile: ARAL-CORE (L1-L5)
Key Requirements: Low latency, audit trails, risk management

4.2 Compliance Requirements

Regulations:

MiFID II (EU Markets in Financial Instruments Directive)
SEC Rule 15c3-5 (US Market Access Rule)
ESMA guidelines on algorithmic trading
FCA Handbook (UK)

Mandatory Controls:

Pre-trade risk controls
Kill switch mechanism
Order throttling
Market maker obligations
Best execution requirements
Transaction reporting (within 1 minute)

4.3 Risk Management Architecture

┌─────────────────────────────────────────────────────────┐
│ Risk Controls (Hardware/Software Circuit Breakers)      │
│ - Max order size                                        │
│ - Max position limits                                   │
│ - Loss limits (daily/weekly)                            │
│ - Volatility filters                                    │
│ - Fat finger protection                                 │
└─────────────────────────────────────────────────────────┘
         ▲
         │
┌────────┴────────────────────────────────────────────────┐
│ L5: Persona        "Trading Agent"                      │
│                    - Strategy: Market making            │
│                    - Risk tolerance: Low                │
│                    - Regulatory: MiFID II compliant     │
├─────────────────────────────────────────────────────────┤
│ L4: Reasoning      Reinforcement learning model        │
│                    - Market microstructure analysis     │
│                    - Order book dynamics                │
│                    - Latency: <100μs inference          │
├─────────────────────────────────────────────────────────┤
│ L3: Capabilities   - FIX protocol integration          │
│                    - Market data feeds                  │
│                    - Order placement/cancellation       │
├─────────────────────────────────────────────────────────┤
│ L2: Memory         - Order book state (in-memory)      │
│                    - Historical trades (TimescaleDB)    │
│                    - Strategy parameters (Redis)        │
├─────────────────────────────────────────────────────────┤
│ L1: Runtime        Bare metal servers (co-location)    │
│                    - OS: Linux RT kernel                │
│                    - Network: DPDK, RDMA                │
└─────────────────────────────────────────────────────────┘

4.4 Audit Requirements

Trade Reconstruction:

Full audit trail of all decisions
Model version and parameters
Market data snapshot at decision time
Reasoning for each trade
Retention: 7 years (MiFID II)

Audit Log Example:

{
  "event_id": "trade-12345",
  "timestamp": "2026-01-15T14:30:00.123456Z",
  "agent_id": "trading-agent-001",
  "model_version": "v2.3.1",
  "action": "buy",
  "instrument": "AAPL",
  "quantity": 100,
  "price": 150.25,
  "reasoning": {
    "signal_strength": 0.82,
    "features": {
      "bid_ask_spread": 0.01,
      "order_imbalance": 0.65,
      "volatility": 0.015
    },
    "risk_checks": {
      "position_limit": "pass",
      "daily_loss_limit": "pass",
      "volatility_filter": "pass"
    }
  },
  "market_data": { ... },
  "result": {
    "order_id": "ord-67890",
    "execution_price": 150.26,
    "slippage": 0.01
  }
}

5. IoT Smart Home Orchestrator

5.1 Scenario Overview

Domain: Smart home automation
Conformance Profile: ARAL-ORCH (L1-L6)
Key Requirements: Low power, local execution, privacy

5.2 Edge Architecture

┌───────────────────────────────────────┐
│ Cloud Layer (Optional)                │
│ - Voice assistant integration         │
│ - Remote access                       │
│ - OTA updates                         │
└─────────────┬─────────────────────────┘
              │
┌─────────────▼─────────────────────────┐
│ Edge Gateway (ARAL Agent)             │
│ - Raspberry Pi / NVIDIA Jetson        │
│ - Local inference (TFLite/ONNX)       │
│ - Matter/Thread protocol support      │
└─────────────┬─────────────────────────┘
              │
    ┌─────────┼─────────┬─────────┐
    │         │         │         │
┌───▼───┐ ┌──▼──┐  ┌──▼──┐   ┌──▼──┐
│Lights │ │Locks│  │HVAC │   │Camera│
└───────┘ └─────┘  └─────┘   └─────┘

5.3 Privacy-First Design

Local Processing:

Voice commands processed on-device
Video analysis at edge (no cloud upload)
Encrypted local storage
Network-isolated operation mode

Data Minimization:

No personal data sent to cloud
Aggregated telemetry only (opt-in)
Automatic deletion of video after 24h
Anonymous usage statistics

5.4 Persona Configuration

{
  "persona": {
    "id": "smart-home-orchestrator",
    "name": "Home Automation Agent",
    "constraints": {
      "privacy_mode": "maximum",
      "cloud_access": false,
      "voice_retention": "none",
      "video_retention": "24h"
    },
    "capabilities": [
      {
        "id": "control_lights",
        "protocol": "matter",
        "devices": ["living_room", "bedroom", "kitchen"]
      },
      {
        "id": "manage_hvac",
        "protocol": "zigbee",
        "learning_enabled": true
      }
    ],
    "reasoning": {
      "model": "tflite",
      "inference_location": "edge",
      "latency_requirement": "<200ms"
    }
  }
}

6. DevOps Autonomous Operations

6.1 Scenario Overview

Domain: IT operations
Conformance Profile: ARAL-ORCH (L1-L6)
Key Requirements: Reliability, observability, human oversight

6.2 AIOps Architecture

┌─────────────────────────────────────────────────────────┐
│ L6: Orchestration  Multi-agent coordination             │
│                    - Incident Commander Agent            │
│                    - Diagnostics Agent                   │
│                    - Remediation Agent                   │
│                    - Communication Agent                 │
├─────────────────────────────────────────────────────────┤
│ L4: Reasoning      - Anomaly detection (ML)             │
│                    - Root cause analysis (LLM)          │
│                    - Runbook selection                   │
│                    - Impact assessment                   │
├─────────────────────────────────────────────────────────┤
│ L3: Capabilities   - Kubernetes API                     │
│                    - Cloud provider APIs                 │
│                    - Terraform/Ansible                   │
│                    - PagerDuty/Slack                     │
├─────────────────────────────────────────────────────────┤
│ L2: Memory         - Metrics: Prometheus                │
│                    - Logs: Elasticsearch                 │
│                    - Traces: Jaeger                      │
│                    - Incidents: PostgreSQL               │
└─────────────────────────────────────────────────────────┘

6.3 Runbook Automation

Incident Detection → Analysis → Remediation

Example Flow:

Detection: High error rate on API service
Analysis:
- Check recent deployments
- Analyze error logs
- Query metrics (CPU, memory, network)
- Review traces for slow requests
Diagnosis: New deployment causing memory leak
Remediation Options:
- Option A: Rollback to previous version (low risk)
- Option B: Scale up pods (temporary mitigation)
- Option C: Apply hotfix (requires testing)
Decision: Agent recommends rollback
Human Approval: Required for production changes
Execution: Automated rollback via CI/CD
Verification: Monitor metrics for 15 minutes
Communication: Update Slack channel, close PagerDuty incident

6.4 Safety Guardrails

Change Approval Matrix:

Action Type	Dev	Staging	Production
Read-only queries	Auto	Auto	Auto
Config changes	Auto	Auto	Approval
Scaling (within limits)	Auto	Auto	Auto
Deployments	Auto	Approval	Approval
Database changes	Approval	Approval	Manual
Service restarts	Auto	Auto	Approval

Blast Radius Limits:

Max 20% of fleet at once
Canary deployments first
Automatic rollback on error spike
Rate limiting on API calls

7. Legal Document Analysis

7.1 Scenario Overview

Domain: Legal tech
Conformance Profile: ARAL-CORE (L1-L5)
Key Requirements: Accuracy, confidentiality, explainability

7.2 Use Cases

Contract review and risk analysis
Legal research and case law search
Due diligence document processing
Regulatory compliance checking
E-discovery and document classification

7.3 Security Architecture

Confidentiality Controls:

Attorney-client privilege protection
End-to-end encryption
Zero-knowledge architecture
No training on client data
Isolated tenant environments

Access Controls:

Multi-factor authentication
Client matter codes
Need-to-know access
Time-limited document access
Watermarking and DRM

7.4 Contract Analysis Workflow

Risk Categories:

🔴 High: Unlimited liability, broad indemnification
🟡 Medium: Unfavorable payment terms, restrictive IP clauses
🟢 Low: Standard confidentiality, typical termination clauses

7.5 Explainability

Clause-Level Annotations:

Clause: "Vendor shall indemnify Client for any and all claims..."

⚠️ RISK: High
ISSUE: Unlimited indemnification obligation
PRECEDENT: Similar clause upheld in Smith v. Acme Corp (2023)
RECOMMENDATION: Negotiate cap at 2x contract value
ALTERNATIVE LANGUAGE: "...up to a maximum of two times the total contract value..."

8. Educational Tutoring System

8.1 Scenario Overview

Domain: Education technology
Conformance Profile: ARAL-CORE (L1-L5)
Key Requirements: Personalization, safety, COPPA compliance

8.2 Student Protection

COPPA Compliance (Children’s Online Privacy Protection Act):

Parental consent for users under 13
No behavioral advertising
Data minimization
Right to delete child’s data
Clear privacy notice

Content Safety:

Content filtering (profanity, violence)
Age-appropriate materials
Moderation of generated content
Anti-cheating measures
Cyberbullying detection

8.3 Adaptive Learning Architecture

┌─────────────────────────────────────────────────────────┐
│ L5: Persona        "Math Tutor Agent"                   │
│                    - Subject: Algebra                   │
│                    - Grade level: 8th                   │
│                    - Teaching style: Socratic          │
├─────────────────────────────────────────────────────────┤
│ L4: Reasoning      - Learning science principles       │
│                    - Zone of proximal development       │
│                    - Spaced repetition                  │
│                    - Retrieval practice                 │
├─────────────────────────────────────────────────────────┤
│ L3: Capabilities   - Problem generation                │
│                    - Step-by-step hints                 │
│                    - Mistake analysis                   │
│                    - Progress tracking                  │
├─────────────────────────────────────────────────────────┤
│ L2: Memory         Student Model:                      │
│                    - Mastery levels per topic           │
│                    - Common mistakes                    │
│                    - Learning pace                      │
│                    - Engagement patterns                │
└─────────────────────────────────────────────────────────┘

8.4 Personalization

Student Model Attributes:

Current knowledge state (Bayesian Knowledge Tracing)
Learning preferences (visual/verbal/kinesthetic)
Optimal difficulty level
Session duration preferences
Time of day patterns

Adaptive Strategies:

Increase difficulty when mastery demonstrated
Provide scaffolding for struggling concepts
Interleave topics for better retention
Adjust pace based on engagement signals

9. Supply Chain Optimization

9.1 Scenario Overview

Domain: Logistics and supply chain
Conformance Profile: ARAL-ORCH (L1-L6)
Key Requirements: Multi-party coordination, real-time optimization

9.2 Multi-Organization Architecture

┌────────────────┐     ┌────────────────┐     ┌────────────────┐
│  Manufacturer  │     │   Distributor  │     │    Retailer    │
│     Agent      │◄───►│     Agent      │◄───►│     Agent      │
└────────┬───────┘     └────────┬───────┘     └────────┬───────┘
         │                      │                      │
         └──────────────────────┼──────────────────────┘
                                │
                    ┌───────────▼───────────┐
                    │  Coordination Agent   │
                    │  (Neutral Third Party)│
                    └───────────────────────┘

9.3 Coordination Protocol

Shared Information (via L7 Protocol):

Inventory levels (aggregated)
Demand forecasts
Capacity constraints
Lead times
Pricing (where permitted)

Private Information (not shared):

Supplier relationships
Cost structures
Strategic plans
Customer lists

9.4 Optimization Objectives

Multi-Objective Optimization:

Minimize total cost
Maximize service level (fill rate)
Minimize carbon footprint
Balance inventory across network
Reduce stockouts and overstocks

Example Decision:

Scenario: Increased demand for Product X in Region A

Manufacturer Agent:
- Increase production by 20%
- Allocate extra capacity

Distributor Agent:
- Reroute shipments from low-demand Region B
- Expedite delivery to Region A

Retailer Agent:
- Accept higher shipment frequency
- Optimize shelf space

Coordination Agent:
- Validates no conflicts
- Optimizes truck routes
- Minimizes total network cost

10. Cross-Organization Agent Collaboration

10.1 Scenario Overview

Domain: Inter-enterprise collaboration
Conformance Profile: ARAL-INTEROP (L1-L7)
Key Requirements: Standardized protocols, trust, federation

10.2 Federation Architecture

┌─────────────────────────────────────────────────────────┐
│                    Agent Directory                      │
│              (Decentralized Registry)                   │
│  - Agent discovery                                      │
│  - Capability advertisement                             │
│  - Trust anchors                                        │
└────────────────┬────────────────────────────────────────┘
                 │
     ┌───────────┼───────────┬───────────┐
     │           │           │           │
┌────▼────┐ ┌───▼────┐ ┌────▼────┐ ┌───▼────┐
│ Org A   │ │ Org B  │ │ Org C   │ │ Org D  │
│ Agent   │ │ Agent  │ │ Agent   │ │ Agent  │
└─────────┘ └────────┘ └─────────┘ └────────┘

10.3 Trust Model

Identity & Authentication:

X.509 certificates (PKI)
Mutual TLS (mTLS)
DID (Decentralized Identifiers)
OAuth 2.0 for delegation

Authorization:

Capability-based access control
Signed capability tokens
Time-limited permissions
Audit trail requirements

10.4 Interoperability Protocol (L7)

Agent-to-Agent Protocol:

{
  "protocol": "aral-a2a",
  "version": "1.0",
  "message": {
    "id": "msg-abc123",
    "from": {
      "agent_id": "org-a-agent-001",
      "organization": "company-a.com",
      "certificate": "-----BEGIN CERTIFICATE-----..."
    },
    "to": {
      "agent_id": "org-b-agent-005",
      "organization": "company-b.com"
    },
    "timestamp": "2026-01-15T14:30:00Z",
    "ttl": 3600,
    "type": "request",
    "action": "query_inventory",
    "payload": {
      "product_id": "SKU-12345",
      "quantity": 1000,
      "delivery_date": "2026-02-01"
    },
    "signature": "-----BEGIN SIGNATURE-----..."
  }
}

10.5 Use Case: Healthcare Information Exchange

Scenario: Patient transfers between hospitals

Agents Involved:

Hospital A: Discharge planning agent
Hospital B: Admission coordination agent
Payer: Authorization agent
Patient: Personal health agent (PHR)

Workflow:

Hospital A agent initiates transfer request
Patient agent verifies consent
Hospital B agent checks bed availability
Payer agent pre-authorizes admission
Secure health record transfer (HL7 FHIR)
Medication reconciliation agent prevents conflicts
Audit trail recorded by all parties

Benefits:

Reduced delays (48h → 4h average)
Fewer medical errors
Better patient experience
Automated compliance documentation

Conclusion

These integration scenarios demonstrate the versatility and power of ARAL-conformant agents across diverse domains. Key success factors include:

Clear Architecture: Layer separation enables modularity
Security First: Built-in security controls at each layer
Privacy by Design: GDPR compliance from the start
Interoperability: Standard protocols enable collaboration
Human Oversight: Critical decisions require human approval
Auditability: Full traceability for compliance and debugging

Organizations implementing ARAL agents should:

Start with a clear conformance profile (CORE, ORCH, or INTEROP)
Conduct thorough security and privacy assessments
Implement comprehensive monitoring and observability
Plan for human-in-the-loop workflows
Establish clear governance and accountability

11. End-to-End Integration Scenarios

11.1 Customer Support Automation

Domain: Enterprise customer service
Conformance Profile: ARAL-INTEROP (L1-L7)
Technology Stack: Modern cloud-native architecture

┌─────────────────────────────────────────────────────────────────┐
│                    CUSTOMER SUPPORT AGENT                       │
├─────────┬─────────┬─────────┬─────────┬─────────┬─────────┬────┤
│   L7    │   L6    │   L5    │   L4    │   L3    │   L2    │ L1 │
├─────────┼─────────┼─────────┼─────────┼─────────┼─────────┼────┤
│ Cloud   │Temporal │Keycloak │LangChain│ Zendesk │ Redis + │ K8s│
│ Events  │         │  + OPA  │  + DMN  │   API   │ Qdrant  │    │
└─────────┴─────────┴─────────┴─────────┴─────────┴─────────┴────┘

Layer Implementations:

L7 (Protocol): CloudEvents for event-driven architecture, async messaging
L6 (Orchestration): Temporal for workflow orchestration, durable execution
L5 (Persona): Keycloak for identity, OPA (Open Policy Agent) for authorization
L4 (Reasoning): LangChain for LLM orchestration, DMN (Decision Model Notation)
L3 (Capabilities): Zendesk API integration for ticket management
L2 (Memory): Redis for caching, Qdrant for vector similarity search
L1 (Runtime): Kubernetes cluster for container orchestration

Key Features:

Multi-tenant architecture with isolated namespaces
Real-time sentiment analysis and escalation
Automated ticket routing and triage
Context-aware conversation history
SLA-driven prioritization

11.2 Financial Document Processing

Domain: Banking and financial services
Conformance Profile: ARAL-ORCH (L1-L6)
Technology Stack: AWS-native with compliance focus

┌─────────────────────────────────────────────────────────────────┐
│                 DOCUMENT PROCESSING PIPELINE                    │
├─────────┬─────────┬─────────┬─────────┬─────────┬─────────┬────┤
│   L7    │   L6    │   L5    │   L4    │   L3    │   L2    │ L1 │
├─────────┼─────────┼─────────┼─────────┼─────────┼─────────┼────┤
│ AsyncAPI│  Argo   │ SPIFFE  │  GPT-4  │ AWS     │Postgres │EKS │
│ + Kafka │Workflows│ + Cedar │+ Drools │Textract │+ S3     │    │
└─────────┴─────────┴─────────┴─────────┴─────────┴─────────┴────┘

Layer Implementations:

L7 (Protocol): AsyncAPI for event schemas, Kafka for message streaming
L6 (Orchestration): Argo Workflows for Kubernetes-native pipelines
L5 (Persona): SPIFFE/SPIRE for service identity, Cedar for policy language
L4 (Reasoning): GPT-4 for document understanding, Drools for business rules
L3 (Capabilities): AWS Textract for OCR and document extraction
L2 (Memory): PostgreSQL for structured data, S3 for document storage
L1 (Runtime): Amazon EKS (Elastic Kubernetes Service)

Key Features:

PII detection and redaction
Regulatory compliance checks (KYC, AML)
Multi-language document support
Audit trail for all processing steps
SOC 2 Type II compliant architecture

11.3 Multi-Agent Research System

Domain: Academic research and knowledge synthesis
Conformance Profile: ARAL-INTEROP (L1-L7)
Technology Stack: Advanced AI agent collaboration

┌─────────────────────────────────────────────────────────────────┐
│                   RESEARCH AGENT COLLECTIVE                     │
├─────────┬─────────┬─────────┬─────────┬─────────┬─────────┬────┤
│   L7    │   L6    │   L5    │   L4    │   L3    │   L2    │ L1 │
├─────────┼─────────┼─────────┼─────────┼─────────┼─────────┼────┤
│  gRPC   │ CrewAI  │  Auth0  │LangGraph│   MCP   │ Neo4j + │GKE │
│+ Events │+ Tempora│ + OPA   │+ Claude │ Servers │ Weaviate│    │
└─────────┴─────────┴─────────┴─────────┴─────────┴─────────┴────┘

Layer Implementations:

L7 (Protocol): gRPC for efficient RPC, CloudEvents for async communication
L6 (Orchestration): CrewAI for multi-agent coordination, Temporal for workflows
L5 (Persona): Auth0 for authentication, OPA for fine-grained authorization
L4 (Reasoning): LangGraph for agent state machines, Claude for deep analysis
L3 (Capabilities): MCP (Model Context Protocol) servers for tool integration
L2 (Memory): Neo4j for knowledge graphs, Weaviate for semantic search
L1 (Runtime): Google Kubernetes Engine (GKE)

Agent Roles:

Literature Review Agent: Searches academic databases, extracts key findings
Synthesis Agent: Combines insights, identifies patterns and gaps
Critique Agent: Evaluates methodology, identifies biases
Citation Agent: Manages references, formats bibliographies
Writing Agent: Generates research summaries and reports

Coordination Modes:

Debate Mode: Agents argue different interpretations of findings
Consensus Mode: Voting on confidence levels for claims
Chain Mode: Sequential analysis pipeline (search → analyze → critique → write)
Parallel Mode: Concurrent literature searches across databases

12. Conformance Matrix by Layer

This matrix defines the conformance requirements for each layer across the three ARAL profiles.

L1-L3: Runtime, Memory & Capabilities

Layer	MUST (Obligatoire)	SHOULD (Recommandé)	MAY (Optionnel)
L1: Runtime	• Container isolation • Resource limits • Health checks • Graceful shutdown	• Metrics endpoint • Hot reload • Backpressure	• GPU acceleration • Custom schedulers • Multi-region
L2: Memory	• Durable state store • TTL-based expiration • Namespace isolation	• Event sourcing • CQRS pattern • Encryption at rest	• Vector memory • Distributed cache • Time-travel debugging
L3: Capabilities	• OpenAPI contract • Permission checks • Input validation	• Service discovery • Circuit breaker • Rate limiting	• MCP support • Plugin system • Capability marketplace

L4-L5: Reasoning & Persona

Layer	MUST (Obligatoire)	SHOULD (Recommandé)	MAY (Optionnel)
L4: Reasoning	• Traceable decisions • Timeout enforcement • Error handling	• Guardrails • Prompt injection defense • Token limits	• Hybrid reasoning • Model ensemble • Reinforcement learning
L5: Persona	• Agent identity • Immutable at runtime • Validation at startup	• Policy-as-code • Cryptographic signing • Constraint enforcement	• Verifiable credentials • DID support • Persona marketplace

L6-L7: Orchestration & Protocol

Layer	MUST (Obligatoire)	SHOULD (Recommandé)	MAY (Optionnel)
L6: Orchestration	• Retry policies • Load balancing • Circuit breaker	• Saga support • Graceful degradation • Priority queues	• Multi-agent choreography • Agent discovery • Consensus protocols
L7: Protocol	• CloudEvents envelope • Trace propagation • Standard auth	• Schema versioning • Content negotiation • Compression	• Federation protocol • P2P communication • Blockchain integration

12.1 Profile Requirements

ARAL-CORE (L1-L5)

Target: Standalone autonomous agents

Required Layers: L1, L2, L3, L4, L5
Optional Layers: None
Compliance Level: All MUST requirements for L1-L5

Use Cases:

Personal AI assistants
Single-purpose automation
Edge computing agents
Embedded systems

ARAL-ORCH (L1-L6)

Target: Multi-agent orchestration

Required Layers: L1, L2, L3, L4, L5, L6
Optional Layers: L7 (for enhanced interop)
Compliance Level: All MUST requirements for L1-L6

Use Cases:

Enterprise multi-agent systems
Swarm intelligence
Distributed problem solving
Collaborative agents

ARAL-INTEROP (L1-L7)

Target: Cross-system interoperability

Required Layers: L1, L2, L3, L4, L5, L6, L7
Optional Layers: None
Compliance Level: All MUST requirements for L1-L7

Use Cases:

Inter-enterprise collaboration
Federated agent networks
Industry consortiums
Public agent services

12.2 Implementation Checklists

For L1 (Runtime) - MUST

Unique agent instance ID
Graceful shutdown (configurable timeout)
Health check endpoint (HTTP/gRPC)
Resource quotas (CPU, memory, connections)
Lifecycle event logging
Request timeout enforcement
Metrics endpoint (Prometheus format)

For L2 (Memory) - MUST

For L3 (Capabilities) - MUST

Capability registry
Permission-based access control
Input/output validation (JSON Schema)
Capability not found error handling
Execution timeout enforcement
Result validation

For L4 (Reasoning) - MUST

For L5 (Persona) - MUST

For L6 (Orchestration) - MUST

For L7 (Protocol) - MUST

13. Technology Stack Examples

13.1 Cloud-Native Stack (AWS)

Layer	Technology	Notes
L7	API Gateway + EventBridge	REST + async events
L6	Step Functions	Workflow orchestration
L5	IAM + Cognito	Identity & access
L4	Bedrock (Claude/Titan)	Managed LLM service
L3	Lambda + API Gateway	Serverless capabilities
L2	DynamoDB + ElastiCache	NoSQL + caching
L1	ECS Fargate / EKS	Container runtime

Cost Model: Pay-per-use, serverless-first

13.2 Enterprise On-Premises Stack

Layer	Technology	Notes
L7	Kong API Gateway	Enterprise API management
L6	Apache Airflow	Workflow orchestration
L5	Keycloak + OPA	Open source IAM
L4	Ollama (local LLM)	On-prem model serving
L3	Custom Python services	Business logic
L2	PostgreSQL + Redis	RDBMS + cache
L1	Kubernetes (on-prem)	Container orchestration

Cost Model: Fixed infrastructure costs, full control

13.3 Edge Computing Stack (IoT)

Layer	Technology	Notes
L7	MQTT + CoAP	Lightweight protocols
L6	EdgeX Foundry	IoT edge framework
L5	Lightweight JWT	Token-based auth
L4	TensorFlow Lite	On-device inference
L3	gRPC services	Efficient RPC
L2	SQLite + LevelDB	Embedded database
L1	Docker (ARM)	Containerized edge

Cost Model: Resource-constrained, offline-capable

Conclusion

These end-to-end scenarios demonstrate ARAL’s flexibility across diverse domains, scales, and technology stacks. The conformance matrix provides clear guidance on implementation requirements, ensuring interoperability while allowing technology choice flexibility.

Key Takeaways:

Layered Architecture: Each layer has distinct responsibilities and can be implemented independently
Technology Agnostic: ARAL defines interfaces, not implementations
Scalability: From edge devices to enterprise multi-agent systems
Compliance: Built-in support for regulatory requirements (GDPR, HIPAA, etc.)
Interoperability: Standard protocols enable cross-organization collaboration

References:

ARAL-CORE-1.0
ARAL-SECURITY-1.0
ARAL-PRIVACY-1.0
ARAL-PROTOCOL-1.0
ARAL-CONFORMANCE-1.0

Integration Scenarios

ARAL Integration Scenarios

Overview

Table of Contents

1. Enterprise Knowledge Assistant

1.1 Scenario Overview

1.2 Architecture

1.3 Security Considerations

1.4 Privacy Requirements

1.5 Implementation Example

1.6 Integration Pattern

1.7 Deployment Checklist

2. Multi-Agent Customer Service

2.1 Scenario Overview

2.2 Multi-Agent Architecture

2.3 Agent Specializations

2.4 Orchestration Protocol

2.5 Human Handoff Criteria

2.6 Metrics & Monitoring

3. Healthcare Clinical Decision Support

3.1 Scenario Overview

3.2 Regulatory Considerations

3.3 Safety Architecture

3.4 Explainability Requirements

3.5 Data Privacy

4. Financial Trading Agent

4.1 Scenario Overview

4.2 Compliance Requirements

4.3 Risk Management Architecture

4.4 Audit Requirements

5. IoT Smart Home Orchestrator

5.1 Scenario Overview

5.2 Edge Architecture

5.3 Privacy-First Design

5.4 Persona Configuration

6. DevOps Autonomous Operations

6.1 Scenario Overview

6.2 AIOps Architecture

6.3 Runbook Automation

6.4 Safety Guardrails

7. Legal Document Analysis

7.1 Scenario Overview

7.2 Use Cases

7.3 Security Architecture

7.4 Contract Analysis Workflow

7.5 Explainability

8. Educational Tutoring System

8.1 Scenario Overview

8.2 Student Protection

8.3 Adaptive Learning Architecture

8.4 Personalization

9. Supply Chain Optimization

9.1 Scenario Overview

9.2 Multi-Organization Architecture

9.3 Coordination Protocol

9.4 Optimization Objectives

10. Cross-Organization Agent Collaboration

10.1 Scenario Overview

10.2 Federation Architecture

10.3 Trust Model

10.4 Interoperability Protocol (L7)

10.5 Use Case: Healthcare Information Exchange

Conclusion

11. End-to-End Integration Scenarios

11.1 Customer Support Automation

11.2 Financial Document Processing

11.3 Multi-Agent Research System

12. Conformance Matrix by Layer

L1-L3: Runtime, Memory & Capabilities

L4-L5: Reasoning & Persona

L6-L7: Orchestration & Protocol

12.1 Profile Requirements

ARAL-CORE (L1-L5)

ARAL-ORCH (L1-L6)

ARAL-INTEROP (L1-L7)

12.2 Implementation Checklists

For L1 (Runtime) - MUST

For L2 (Memory) - MUST

For L3 (Capabilities) - MUST

For L4 (Reasoning) - MUST