Getting Started with SLM Agents

Step 1: Set Up Microsoft Agent Framework Environment

Install Dependencies:

# Install Microsoft Agent Framework
pip install microsoft-agent-framework

# Install Foundry Local SDK for edge deployment
pip install foundry-local-sdk

# Install additional dependencies for edge agents
pip install openai asyncio

Initialize Foundry Local:

# Start Foundry Local service
foundry service start

# Load default model for agent development
foundry model run phi-4-mini

Step 2: Choose Your SLM for Agent Applications

Popular options for Microsoft Agent Framework:

• Microsoft Phi-4 Mini (3.8B): Excellent for general agent tasks with balanced performance
• Qwen2.5-0.5B (0.5B): Ultra-efficient for simple routing and classification agents
• Qwen2.5-Coder-0.5B (0.5B): Specialized for code-related agent tasks
• Phi-4 (7B): Advanced reasoning for complex edge scenarios when resources allow

Step 3: Create Your First Agent with Microsoft Agent Framework

Basic Agent Setup:

from microsoft_agent_framework import Agent, Config
from foundry_local import FoundryLocalManager

# Initialize Foundry Local connection
foundry = FoundryLocalManager("phi-4-mini")

# Create agent configuration
config = Config(
    name="my-first-agent",
    model_provider="foundry-local",
    model_alias="phi-4-mini",
    offline_mode=True
)

# Create and configure agent
agent = Agent(
    config=config,
    model_endpoint=foundry.endpoint,
    api_key=foundry.api_key
)

# Define a simple tool
@agent.tool
def get_current_time() -> str:
    """Get the current time."""
    from datetime import datetime
    return datetime.now().strftime("%Y-%m-%d %H:%M:%S")

# Test the agent
response = agent.chat("What time is it?")
print(response)

Step 4: Define Agent Scope and Requirements

Start with focused, well-defined agent applications using Microsoft Agent Framework:

• Single domain agents: Customer service OR scheduling OR research
• Clear agent objectives: Specific, measurable goals for agent performance
• Limited tool integration: 3-5 tools maximum for initial agent deployment
• Defined agent boundaries: Clear escalation paths for complex scenarios
• Edge-first design: Prioritize offline functionality and local processing

Step 5: Implement Edge Deployment with Microsoft Agent Framework

Resource Configuration:

from microsoft_agent_framework import ResourceConfig

# Configure for edge deployment
resource_config = ResourceConfig(
    max_memory_usage="2GB",
    max_concurrent_agents=2,
    model_cache_size="1GB",
    auto_unload_idle_models=True,
    power_management=True
)

agent = Agent(
    config=config,
    resource_limits=resource_config
)

Deploy Safety Measures for Edge Agents:

• Local input validation: Check requests without cloud dependencies
• Offline output filtering: Ensure responses meet quality standards locally
• Edge security controls: Implement security without requiring internet connectivity
• Local monitoring: Track performance and flag issues using edge telemetry

Step 6: Measure and Optimize Edge Agent Performance

• Agent task completion rates: Monitor success rates in offline scenarios
• Agent response times: Ensure sub-second response times for edge deployment
• Resource utilization: Track memory, CPU, and battery usage on edge devices
• Cost efficiency: Compare edge deployment costs to cloud-based alternatives
• Offline reliability: Measure agent performance during network outages