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EdgeMind: 5G-MEC Intelligence Orchestration

EdgeMind

Real-time AI orchestration at telecom edge with Strands agent swarms. Brings intelligence to 5G Multi-access Edge Computing sites for sub-100ms decision making.

Live Demo

Interactive Dashboard

Project Overview

Built a 5G-MEC (Multi-access Edge Computing) orchestration system that deploys Strands-based multi-agent swarms directly at telecom edge sites. The system monitors local metrics, detects performance degradation, and self-orchestrates routing and resource decisions without cloud dependence.

Problem Statement

Today's AI systems trade speed for intelligence. Edge devices process fast but lack complexity; the cloud processes deeply but adds latency. For real-time applications—autonomous vehicles, industrial control, or competitive gaming—milliseconds matter.

Solution

EdgeMind brings intelligence to the edge through threshold-based orchestration, MEC-native intelligence, and swarm coordination—all achieving sub-100ms routing decisions.

Key Innovation

Threshold-Based Orchestration

  • Monitors latency, CPU/GPU load, and queue depth
  • Triggers intelligent swarm responses automatically
  • Adapts to network conditions in real-time
  • No cloud dependency for critical decisions

MEC-Native Intelligence

  • Strands agents deployed directly at telecom edge sites
  • Located near RAN (Radio Access Network) controllers
  • Complete agent set at each MEC site
  • Local MCP tools for metrics and operations

Swarm Coordination

  • Agents collaborate across MEC sites
  • Balance load without cloud involvement
  • Autonomous failover between sites
  • Consensus-based decision making

Real-Time Performance

  • Sub-100ms routing decisions
  • 99.9% availability through redundancy
  • Autonomous load balancing
  • Intelligent adaptation to conditions

Architecture & Technical Approach

System Flow

User Devices (5G)
  → MEC Site A (Primary)
  → Swarm Coordination
  → MEC Sites B & C (Fallback)
  → AWS Cloud (Passive Observer)

MEC Site Components

Each MEC site contains:

Complete Strands Agent Set:

  • Orchestrator Agent
  • Load Balancer Agent
  • Resource Monitor Agent
  • Decision Coordinator Agent
  • Cache Manager Agent

Local MCP Tools:

  • metrics_monitor
  • container_ops
  • inference_engine
  • telemetry_logger
  • memory_sync

Agent Architecture

Agent Role Deployment
Orchestrator Agent Threshold monitoring & swarm triggering MEC Site Controller
Load Balancer Agent Distribute workload across MEC sites Strands Swarm Member
Resource Monitor Agent Track CPU/GPU/latency metrics Strands Swarm Member
Decision Coordinator Agent Coordinate swarm consensus Strands Swarm Member
Cache Manager Agent Local model and data caching Strands Swarm Member

Business Use Cases

Gaming & Esports

  • Real-time NPC dialogue: Device SLM for instant responses
  • Game state analysis: MEC swarm coordination for regional multiplayer
  • Performance analytics: Cloud observability (passive)
  • High GPU usage: 85-95% utilization for rendering and AI

Autonomous Vehicles

  • Collision detection: Device SLM for ultra-low latency safety (<30ms)
  • Traffic coordination: MEC orchestrator manages regional traffic flow
  • Fleet analytics: Cloud monitoring and long-term insights
  • V2X communication: Vehicle-to-everything coordination

Smart Cities & IoT

  • Sensor processing: Device SLM for immediate responses
  • City-wide coordination: MEC swarm balances infrastructure load
  • Urban planning: Cloud analytics from aggregated MEC data

Healthcare

  • Patient monitoring: 50-200 patients per MEC site
  • HIPAA compliance: Local processing for privacy
  • Medical alerts: Real-time critical event detection

Technology Stack

  • Edge Agents: Strands framework with Claude 3.5 Sonnet integration
  • AI Model: Claude API for real agent coordination
  • MEC Infrastructure: Docker/Kubernetes on edge compute nodes
  • Dashboard: Streamlit with real-time simulation and dual-mode operation
  • Orchestration: Threshold-based swarm coordination with MCP tools
  • AWS Integration: AgentCore Memory + Orchestration only (passive)
  • Communication: Direct MEC-to-MEC networking

Live Dashboard Features

Dual-Mode Operation

  • Mock Data Mode: No API key required, simulated agents
  • Real Strands Agents Mode: Full Claude API integration

Real-Time Metrics

  • Latency (ms) — target <100ms
  • CPU Usage — trigger >80%
  • GPU Usage — monitoring utilization
  • Queue Depth — request backlog

Swarm Visualization

  • Green: Healthy MEC sites
  • Red: Overloaded sites
  • Gray: Failed sites
  • Lines: MEC interconnections

Agent Activity Stream

  • Info: Normal operations
  • Success: Consensus achieved
  • Warning: Threshold breach
  • Error: System failure

Enhanced Demo Scenarios

Gaming: High GPU usage (85-95%), multiplayer synchronization, NPC AI processing

Automotive: Ultra-low latency (<30ms), safety-critical systems, V2X communication

Healthcare: Patient monitoring (50-200 patients), HIPAA compliance, medical alerts

Normal: Balanced resource utilization and standard MEC operations

Automated Demo Mode

  • Cycles through all scenarios every 15 seconds
  • Scenario-specific metrics and thresholds
  • Enhanced visualizations with context-aware indicators
  • Start/Stop controls for presentation mode

Skills Demonstrated

5G/MEC Architecture: Multi-access edge computing, RAN integration, telecom infrastructure, edge deployment

Multi-Agent Systems: Strands framework, swarm coordination, consensus algorithms, agent orchestration

Real-Time Systems: Sub-100ms latency targets, threshold-based triggering, performance optimization

Distributed Systems: MEC-to-MEC networking, failover mechanisms, load balancing, redundancy

Cloud Integration: AWS AgentCore, passive observability, hybrid edge-cloud architecture

Dashboard Development: Streamlit, real-time visualization, dual-mode operation, automated demos

DevOps: Docker/Kubernetes, edge deployment, monitoring, telemetry

MCP Protocol: Tool design, metrics monitoring, container operations, inference engines

Expected Outcomes

  • Sub-100ms decision making for real-time applications
  • Autonomous load balancing without cloud dependency
  • 99.9% availability through MEC site redundancy
  • Intelligent swarm coordination adapting to network conditions

Future Work: ICEO Framework

The next phase extends toward ICEO (Intelligence-Centric Edge Orchestration), where each MEC site acts as a learning agent within a distributed intelligence fabric.

Planned Research:

  • Multi-MEC simulation for latency and consensus testing
  • Reinforcement-based learning between edge and cloud layers
  • Formalize and publish ICEO as a framework for autonomous 5G orchestration

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