L1 - Runtime

The Runtime layer (L1) is the foundational layer of the ARAL architecture, responsible for managing the execution environment, resources, and lifecycle of AI agents.

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Overview

The Runtime layer provides the essential infrastructure that allows agents to operate reliably and efficiently. It handles:

• Resource Management: CPU, memory, and connection quotas
• Lifecycle Management: Startup, shutdown, and restart procedures
• Health Monitoring: Health checks and status reporting
• Metrics Collection: Performance and operational metrics

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Core Responsibilities

Instance Management

Provides unique agent instance identification and tracking

Resource Control

Implements resource quotas and fallback behaviors

Health Monitoring

Exposes health check endpoints for orchestration

Metrics & Logging

Provides observability through structured logs and metrics

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Key Requirements

Instance Management

Requirement	Description
Unique ID	Each agent instance must have a unique identifier
Lifecycle Events	Log all start, stop, and error events
Graceful Shutdown	Implement configurable timeout for clean termination

Resource Management

Requirement	Description
Resource Quotas	Implement limits for CPU, memory, and connections
Fallback Behavior	Define behavior when resources are exhausted
Execution Timeout	Enforce maximum execution time per request
Backpressure	Implement mechanisms to handle load spikes

Observability

Requirement	Description
Health Endpoint	Expose HTTP endpoint for health checks
Metrics Endpoint	Provide Prometheus-compatible metrics
Structured Logging	Use consistent, parseable log format

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Configuration

Manifest Schema

{
  "agent_id": "uuid",
  "version": "1.0.0",
  "runtime": {
    "max_memory_mb": 512,
    "max_cpu_percent": 80,
    "max_connections": 100,
    "shutdown_timeout_ms": 30000,
    "request_timeout_ms": 60000
  },
  "health_check": {
    "enabled": true,
    "port": 8080,
    "path": "/health"
  },
  "metrics": {
    "enabled": true,
    "port": 9090,
    "format": "prometheus"
  }
}

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Health Check Response

Success Response

{
  "status": "healthy",
  "timestamp": "2026-01-15T12:00:00Z",
  "agent_id": "agent-uuid",
  "uptime_seconds": 3600,
  "version": "1.0.0"
}

Failure Response

{
  "status": "unhealthy",
  "timestamp": "2026-01-15T12:00:00Z",
  "agent_id": "agent-uuid",
  "errors": [
    {
      "component": "memory",
      "message": "Memory usage exceeded 95%"
    }
  ]
}

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Best Practices

1. Set Appropriate Timeouts: Configure timeouts based on expected workload
2. Monitor Resource Usage: Regularly review metrics to optimize quotas
3. Implement Health Checks: Ensure orchestrators can detect unhealthy agents
4. Log Lifecycle Events: Maintain audit trail of agent operations
5. Test Graceful Shutdown: Verify clean termination under various conditions

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Related Layers

• L2 - Memory: Manages agent state and context
• L6 - Orchestration: Uses health checks for routing
• Specification: Full requirements for L1 Runtime

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