Edge Agent Development

This document provides guidance for developing and deploying agents on edge devices using the Meta Agent Platform.

Overview

Edge agents run on resource-constrained devices close to data sources, enabling low-latency processing, privacy preservation, and offline operation. The platform supports lightweight runtimes, synchronization, and monitoring for edge deployments.

Edge Agent Characteristics

• Lightweight: Optimized for CPU, memory, and storage constraints.
• Offline Capable: Operate without constant connectivity; sync when online.
• Efficient Models: Use quantized, pruned, or distilled models.
• Local Storage: Use SQLite or similar for local persistence.
• Resource Monitoring: Track CPU, memory, battery, and network usage.
• Security: Secure boot, encrypted storage, secure updates.

Edge Architecture

The edge agent architecture consists of several key components:

1. Edge Runtime: Lightweight execution environment
2. Local Storage: Database for offline operation
3. Sync Manager: Handles data synchronization
4. Resource Monitor: Tracks device resources
5. Security Module: Manages encryption and secure updates

Note: This is a placeholder for an edge agent architecture diagram. The actual diagram should be created and added to the project.

Development Patterns

Model Optimization Techniques

Quantization

Reduces model precision to decrease size and improve inference speed:

# Example of quantizing a TensorFlow model
import tensorflow as tf

def quantize_model(model_path, output_path):
    # Load the model
    model = tf.keras.models.load_model(model_path)

    # Convert to TensorFlow Lite model
    converter = tf.lite.TFLiteConverter.from_keras_model(model)

    # Enable quantization
    converter.optimizations = [tf.lite.Optimize.DEFAULT]

    # Convert the model
    tflite_model = converter.convert()

    # Save the quantized model
    with open(output_path, 'wb') as f:
        f.write(tflite_model)

    print(f"Original model size: {os.path.getsize(model_path) / 1024:.2f} KB")
    print(f"Quantized model size: {os.path.getsize(output_path) / 1024:.2f} KB")

Pruning

Removes unnecessary connections in neural networks:

# Example of pruning a TensorFlow model
import tensorflow as tf
import tensorflow_model_optimization as tfmot

def prune_model(model, target_sparsity=0.8):
    # Define pruning parameters
    pruning_params = {
        'pruning_schedule': tfmot.sparsity.keras.PolynomialDecay(
            initial_sparsity=0.0,
            final_sparsity=target_sparsity,
            begin_step=0,
            end_step=1000
        )
    }

    # Apply pruning to all layers
    model_pruned = tfmot.sparsity.keras.prune_low_magnitude(model, **pruning_params)

    # Compile the pruned model
    model_pruned.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])

    return model_pruned

Knowledge Distillation

Trains a smaller model to mimic a larger one:

# Example of knowledge distillation
import tensorflow as tf

def distill_knowledge(teacher_model, student_model, x_train, temperature=5.0):
    # Define distillation loss function
    def distillation_loss(y_true, y_pred):
        # Get soft targets from teacher model
        teacher_preds = teacher_model(x_train, training=False)
        teacher_preds = tf.nn.softmax(teacher_preds / temperature)

        # Apply temperature scaling to student predictions
        student_preds = y_pred / temperature
        student_preds = tf.nn.softmax(student_preds)

        # Calculate KL divergence loss
        kl_loss = tf.keras.losses.KLDivergence()(teacher_preds, student_preds)

        # Combine with standard categorical crossentropy
        ce_loss = tf.keras.losses.CategoricalCrossentropy()(y_true, y_pred)

        return ce_loss * 0.5 + kl_loss * 0.5 * (temperature ** 2)

    # Compile student model with distillation loss
    student_model.compile(optimizer='adam', loss=distillation_loss, metrics=['accuracy'])

    return student_model

Efficient Code Patterns

• Minimize Dependencies: Use lightweight libraries or implement minimal versions.
• Lazy Loading: Load resources only when needed.
• Memory Management: Implement proper cleanup and resource release.
• Asynchronous Processing: Use non-blocking operations where possible.

Synchronization Strategies

Delta Sync

Only transmit changes since last synchronization:

# Example of delta synchronization
def delta_sync(local_db, server_endpoint, last_sync_timestamp):
    # Get changes since last sync
    changes = local_db.get_changes_since(last_sync_timestamp)

    # Send changes to server
    response = requests.post(
        f"{server_endpoint}/sync",
        json={
            'device_id': get_device_id(),
            'changes': changes,
            'last_sync': last_sync_timestamp
        }
    )

    # Process server response
    if response.status_code == 200:
        server_changes = response.json().get('changes', [])
        new_timestamp = response.json().get('timestamp')

        # Apply server changes to local DB
        local_db.apply_changes(server_changes)

        # Update last sync timestamp
        local_db.update_sync_timestamp(new_timestamp)

        return True, new_timestamp
    else:
        return False, last_sync_timestamp

Conflict Resolution

Handles conflicts between local and server changes:

# Example of conflict resolution
def resolve_conflicts(local_changes, server_changes):
    resolved_changes = []
    conflicts = []

    # Identify conflicts
    for local_change in local_changes:
        for server_change in server_changes:
            if is_conflict(local_change, server_change):
                conflicts.append((local_change, server_change))
                break
        else:
            # No conflict found, add to resolved changes
            resolved_changes.append(local_change)

    # Add non-conflicting server changes
    for server_change in server_changes:
        if not any(is_conflict(local_change, server_change) for local_change in local_changes):
            resolved_changes.append(server_change)

    # Apply resolution strategy for conflicts
    for local_change, server_change in conflicts:
        resolution = apply_resolution_strategy(local_change, server_change)
        resolved_changes.append(resolution)

    return resolved_changes

Deployment Workflow

Note: This is a placeholder for an edge deployment workflow diagram. The actual diagram should be created and added to the project.

1. Profile Requirements: Assess device capabilities and agent needs.
2. Optimize Models: Prepare models for edge execution (ONNX, TFLite, etc.).
3. Package Agent: Bundle code, models, and dependencies.
4. Provision Device: Install runtime and dependencies.
5. Deploy & Validate: Transfer package, run agent, verify operation.
6. Monitor: Collect and sync telemetry data.

Edge Agent Configuration

# edge-agent-config.yaml
name: text-classifier-edge
version: 1.0.0
type: edge

resources:
  max_memory: 128MB
  max_cpu: 1.0
  max_storage: 100MB

model:
  type: quantized
  format: tflite
  path: /models/text-classifier-lite.tflite
  precision: int8

runtime:
  executor: tflite
  threads: 2
  acceleration: cpu  # cpu, gpu, npu

input:
  format: text
  max_length: 512

output:
  format: json
  schema: /schemas/classification-output.json

storage:
  type: sqlite
  path: /data/agent-data.db
  max_size: 50MB

sync:
  strategy: delta
  interval: 3600  # seconds
  retry_policy:
    max_retries: 5
    backoff: exponential

security:
  encryption: aes-256
  secure_boot: true
  integrity_check: true

Testing and Debugging

Resource Constraint Testing

# Example of resource constraint testing
import resource
import threading

def test_with_memory_constraint(agent, input_data, memory_limit_mb):
    # Set memory limit
    resource.setrlimit(
        resource.RLIMIT_AS,
        (memory_limit_mb * 1024 * 1024, resource.RLIM_INFINITY)
    )

    # Run agent with memory constraint
    try:
        result = agent.process(input_data)
        return True, result
    except MemoryError:
        return False, "Memory limit exceeded"

def test_with_cpu_constraint(agent, input_data, cpu_limit_percent):
    # Create CPU limiter thread
    stop_event = threading.Event()

    def cpu_consumer():
        while not stop_event.is_set():
            # Consume CPU cycles
            pass

    # Start CPU consumer threads based on limit
    num_threads = int((100 - cpu_limit_percent) / 100 * os.cpu_count())
    threads = []

    for _ in range(num_threads):
        t = threading.Thread(target=cpu_consumer)
        t.daemon = True
        t.start()
        threads.append(t)

    # Run agent with CPU constraint
    try:
        result = agent.process(input_data)
        success = True
    except Exception as e:
        result = str(e)
        success = False

    # Stop CPU consumer threads
    stop_event.set()
    for t in threads:
        t.join()

    return success, result

Network Simulation

# Example of network condition simulation
import socket
import time

class NetworkSimulator:
    def __init__(self, latency_ms=0, packet_loss_percent=0, bandwidth_kbps=None):
        self.latency_ms = latency_ms
        self.packet_loss_percent = packet_loss_percent
        self.bandwidth_kbps = bandwidth_kbps
        self._original_socket = socket.socket

    def __enter__(self):
        # Replace socket with simulated version
        socket.socket = self._create_simulated_socket
        return self

    def __exit__(self, exc_type, exc_val, exc_tb):
        # Restore original socket
        socket.socket = self._original_socket

    def _create_simulated_socket(self, *args, **kwargs):
        # Create a real socket
        sock = self._original_socket(*args, **kwargs)

        # Replace send/recv methods with simulated versions
        original_send = sock.send
        original_recv = sock.recv

        def simulated_send(data, *args, **kwargs):
            # Simulate latency
            if self.latency_ms > 0:
                time.sleep(self.latency_ms / 1000)

            # Simulate packet loss
            if self.packet_loss_percent > 0:
                if random.random() < (self.packet_loss_percent / 100):
                    # Simulate packet loss by returning sent data size without sending
                    return len(data)

            # Simulate bandwidth limitation
            if self.bandwidth_kbps is not None:
                bytes_per_second = self.bandwidth_kbps * 125  # Convert kbps to B/s
                time_needed = len(data) / bytes_per_second
                time.sleep(time_needed)

            # Actually send the data
            return original_send(data, *args, **kwargs)

        def simulated_recv(bufsize, *args, **kwargs):
            # Simulate latency
            if self.latency_ms > 0:
                time.sleep(self.latency_ms / 1000)

            # Simulate packet loss
            if self.packet_loss_percent > 0:
                if random.random() < (self.packet_loss_percent / 100):
                    # Simulate packet loss by returning empty data
                    return b''

            # Actually receive the data
            data = original_recv(bufsize, *args, **kwargs)

            # Simulate bandwidth limitation
            if self.bandwidth_kbps is not None and data:
                bytes_per_second = self.bandwidth_kbps * 125  # Convert kbps to B/s
                time_needed = len(data) / bytes_per_second
                time.sleep(time_needed)

            return data

        sock.send = simulated_send
        sock.recv = simulated_recv
        return sock

# Usage example
def test_agent_with_network_conditions(agent, input_data):
    # Test with perfect network
    with NetworkSimulator() as _:
        perfect_result = agent.process(input_data)

    # Test with high latency
    with NetworkSimulator(latency_ms=200) as _:
        high_latency_result = agent.process(input_data)

    # Test with packet loss
    with NetworkSimulator(packet_loss_percent=5) as _:
        packet_loss_result = agent.process(input_data)

    # Test with low bandwidth
    with NetworkSimulator(bandwidth_kbps=64) as _:
        low_bandwidth_result = agent.process(input_data)

    # Test with offline condition (100% packet loss)
    with NetworkSimulator(packet_loss_percent=100) as _:
        try:
            offline_result = agent.process(input_data)
            offline_capable = True
        except Exception:
            offline_result = None
            offline_capable = False

    return {
        'perfect': perfect_result,
        'high_latency': high_latency_result,
        'packet_loss': packet_loss_result,
        'low_bandwidth': low_bandwidth_result,
        'offline_capable': offline_capable,
        'offline_result': offline_result
    }

Best Practices

• Test Under Constraints: Simulate limited CPU, memory, and network.
• Offline Testing: Ensure correct operation without connectivity.
• Security: Encrypt data at rest and in transit; use secure update mechanisms.
• Resource Awareness: Adapt behavior based on available resources.
• Progressive Enhancement: Provide basic functionality with minimal resources, enhance with more.
• Graceful Degradation: Maintain core functionality when resources are constrained.
• Battery Optimization: Minimize wake cycles and background processing.
• Update Strategy: Plan for efficient and secure over-the-air updates.

Troubleshooting

Issue	Possible Cause	Solution
High memory usage	Inefficient data processing	Use streaming processing, reduce batch size
Slow startup time	Large model loading	Use lazy loading, optimize model size
Battery drain	Frequent wake cycles	Implement batching, optimize sync intervals
Sync failures	Network instability	Implement robust retry with exponential backoff
Storage exhaustion	Unbounded data growth	Implement data retention policies, compression
Security breach	Insufficient encryption	Use strong encryption, secure key management

References

• Edge Deployment Guide
• Component Design: Edge Computing Framework
• Data Model: Edge Devices
• Edge Infrastructure
• TensorFlow Lite
• ONNX Runtime
• PyTorch Mobile
• SQLite

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Last updated: 2025-04-18