Introduction to ARAL
Introduction to ARAL
Section titled “Introduction to ARAL”ARAL (Agent Reference Architecture Layers) is an open-source standard for building production-ready AI agents. It provides a comprehensive 7-layer architecture that ensures security, scalability, and interoperability across all agent implementations.
Quick Navigation
Section titled “Quick Navigation”Build your first ARAL-compliant agent in 5 minutes
Explore the complete architecture model
Step-by-step tutorials and examples
CORE, ORCH, and INTEROP certification
Why Choose ARAL?
Section titled “Why Choose ARAL?”Enterprise-grade architecture meets open-source philosophy. ARAL is built by teams who understand production systems at scale.
7 distinct layers from runtime to protocol. Each layer has clear responsibilities and well-defined interfaces. No ambiguity, no confusion.
60+ security requirements built-in. STRIDE threat modeling, OAuth 2.0, TLS 1.3+, comprehensive audit logging, and defense-in-depth at every layer.
MCP, REST, WebSocket, gRPC support. Your agents work seamlessly across platforms, tools, and ecosystems. No vendor lock-in.
Battle-tested patterns from real-world deployments. Built-in monitoring, observability, health checks, and reliability patterns.
Use what you need. Start with CORE profile for basics, add ORCH for multi-agent coordination, INTEROP for cross-system federation.
Complete specifications, implementation guides, and examples. Clear requirements with conformance test suites for every layer.
The 7-Layer Architecture
Section titled “The 7-Layer Architecture”ARAL defines a proven, layered approach to AI agent development. Each layer handles a specific concern, enabling modularity, testability, and security by design.
Interactive Layer Overview
Section titled “Interactive Layer Overview”Container/process lifecycle, resource limits, health checks, metrics, observability
Working memory, short-term, long-term storage, vector databases, state management, encryption
Tool/function registry, input validation, permission enforcement, MCP protocol support
LLM integration, decision-making, chain-of-thought, guardrails, prompt injection defense
Identity management, behavioral boundaries, constraints, cryptographic signing, persona switching
Multi-agent workflows, consensus, load distribution, retry policies, circuit breakers
Service discovery, authentication, inter-agent communication, CloudEvents, federation protocols
Key Features
Section titled “Key Features”🎯 Standards-Based Development
Section titled “🎯 Standards-Based Development”Build agents that conform to a universal standard. ARAL ensures your agents are:
- Portable - Run anywhere (cloud, edge, on-premise)
- Testable - 226 test vectors verify conformance
- Maintainable - Clear separation of concerns
- Upgradable - Versioned specifications with migration paths
🔐 Security & Privacy by Design
Section titled “🔐 Security & Privacy by Design”Security isn’t optional in ARAL:
- L1-L7 Security Requirements - 60+ mandatory controls
- GDPR/CCPA Compliance - 32 privacy requirements
- Threat Modeling - STRIDE analysis for each layer
- Audit Logging - Complete traceability
- Least Privilege - Granular permission model
🚀 From Prototype to Production
Section titled “🚀 From Prototype to Production”ARAL scales with your needs:
| Profile | Use Case | Layers | Complexity |
|---|---|---|---|
| CORE | Single agent, basic functionality | L1-L5 | ⭐⭐ Simple |
| ORCH | Multi-agent teams, coordination | L1-L6 | ⭐⭐⭐ Moderate |
| INTEROP | Cross-system federation | L1-L7 | ⭐⭐⭐⭐ Advanced |
Real-World Use Cases
Section titled “Real-World Use Cases”ARAL is used in production for:
- Customer Support Agents - Automated ticket handling with human escalation
- DevOps Automation - Infrastructure management and incident response
- Healthcare Assistants - HIPAA-compliant patient interaction
- Financial Advisors - Regulated investment guidance with audit trails
- Research Teams - Multi-agent scientific collaboration
- Enterprise Chatbots - Secure, compliant internal assistants
Getting Started
Section titled “Getting Started”Ready to build your first ARAL agent? Follow our quick start guide:
Next Steps
Section titled “Next Steps”- Understand the Architecture → Read about the 7-layer model
- Choose a Profile → Select CORE, ORCH, or INTEROP
- Build an Agent → Follow our step-by-step guide
- Test Conformance → Use our test vectors
- Deploy → Learn production best practices
Community & Support
Section titled “Community & Support”ARAL is a community-driven open standard:
- GitHub - aral-standard
- Specifications - All SPEC documents
- Examples - Reference implementations
- Discussions - GitHub Issues and Discussions
Contributing
Section titled “Contributing”We welcome contributions! The ARAL standard is:
- Open Source - Apache 2.0 (code), CC BY 4.0 (specs)
- Vendor Neutral - No single company controls ARAL
- Community Driven - RFCs and public review process
- Well Governed - Clear decision-making and change management
What Makes ARAL Different?
Section titled “What Makes ARAL Different?”Unlike framework-specific approaches (LangChain, AutoGen, CrewAI), ARAL is:
✅ A standard, not a framework - Define architecture, not implementation
✅ Language agnostic - Use Python, TypeScript, Go, Rust, or any language
✅ Security first - Not bolted on as an afterthought
✅ Production proven - Based on real-world deployments
✅ Interoperable - Agents from different vendors work together
✅ Comprehensive - Covers runtime to protocol, memory to orchestration