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Tutorial

Building a Multi-Agent System with AKIOS

Multi-agent orchestration is complex, but with the right infrastructure, it becomes manageable. In this tutorial, we'll build a research team consisting of 5 specialized agents that collaborate on technical analysis tasks.

The Research Team Architecture

Our system consists of:

  • Project Manager Agent: Coordinates the research workflow and assigns tasks
  • Research Analyst Agent: Gathers and analyzes technical data
  • Code Reviewer Agent: Examines code quality and security
  • Documentation Agent: Creates clear technical documentation
  • Quality Assurance Agent: Validates results and ensures completeness

Setting Up the AKIOS Environment

First, install the AKIOS SDK and set up your environment:

pip install akios-sdk
export AKIOS_API_KEY="your-api-key-here"

Defining Agent Policies

Each agent needs a clear policy manifest that defines its capabilities and constraints:

apiVersion: akios/v1
kind: AgentPolicy
metadata:
  name: research-analyst
spec:
  governance:
    allowed_tools:
      - web_search
      - data_analysis
    network_access:
      allowlist:
        - host: "api.github.com"
        - host: "scholar.google.com"
    budget:
      max_tokens_per_hour: 10000
      max_cost_per_session: 2.50

  observability:
    log_level: "detailed"
    alert_on:
      - pattern: "security_vulnerability"
        action: "escalate"

Implementing the Orchestration Logic

Using the AKIOS SDK, we can create a coordinator that manages the agent interactions:

from akios import Agent, Policy, Swarm

# Load agent policies
policies = {
    'project_manager': Policy.from_file('policies/project-manager.yaml'),
    'research_analyst': Policy.from_file('policies/research-analyst.yaml'),
    'code_reviewer': Policy.from_file('policies/code-reviewer.yaml'),
    'documentation': Policy.from_file('policies/documentation.yaml'),
    'qa': Policy.from_file('policies/qa.yaml')
}

# Create the agent swarm
swarm = Swarm(
    agents={
        name: Agent(model="gpt-4-turbo", policy=policy)
        for name, policy in policies.items()
    },
    coordinator_policy=Policy.from_file('policies/coordinator.yaml')
)

# Define the research workflow
workflow = {
    'initial_analysis': ['project_manager', 'research_analyst'],
    'code_review': ['code_reviewer'],
    'documentation': ['documentation'],
    'validation': ['qa']
}

# Execute the research task
result = swarm.run(
    task="Analyze the security implications of the new authentication system",
    workflow=workflow
)

Monitoring and Observability

AKIOS provides comprehensive monitoring of multi-agent interactions:

# Enable detailed tracing
swarm.enable_tracing(
    destination="akios-radar",
    include_agent_states=True,
    include_message_flow=True
)

# Set up alerts for coordination issues
swarm.add_alert(
    condition="agent_timeout",
    action="notify_engineering_team"
)

Best Practices

  • Clear Role Separation: Each agent should have a well-defined scope
  • Policy Enforcement: Use AKIOS policies to prevent agents from overstepping boundaries
  • State Synchronization: Ensure all agents have access to shared context
  • Error Handling: Implement robust error recovery mechanisms
  • Cost Monitoring: Track token usage across the entire swarm

Scaling Considerations

As your multi-agent system grows, consider:

  • Hierarchical coordination patterns
  • Load balancing across agent instances
  • Caching shared knowledge bases
  • Progressive disclosure of context

This architecture provides a solid foundation for building complex, collaborative AI systems while maintaining security, observability, and cost control through the AKIOS platform.