Sub-Agents

Sub-Agents

Intelligent task delegation with specialized agents working in coordination

Overview

Sub-Agents enable sophisticated multi-agent coordination where a main agent intelligently delegates tasks to specialized sub-agents. Each sub-agent has its own expertise, capabilities, and role, working together to complete complex workflows that would be challenging for a single agent.

New: Sub-Agents now integrate seamlessly with Graph workflows, enabling hierarchical task distribution across complex workflow orchestration systems.

Creating Sub-Agents

Sub-agents are created independently and then attached to a main coordinator agent:

import { Agent } from '@astreus-ai/astreus';

// Create specialized sub-agents
const researcher = await Agent.create({
  name: 'ResearcherBot',
  model: 'gpt-4o',
  systemPrompt: 'You are an expert researcher who gathers and analyzes information thoroughly.',
  memory: true,
  knowledge: true
});

const writer = await Agent.create({
  name: 'WriterBot',
  model: 'gpt-4o',
  systemPrompt: 'You are a skilled content writer who creates engaging, well-structured content.',
  vision: true
});

const analyst = await Agent.create({
  name: 'AnalystBot', 
  model: 'gpt-4o',
  systemPrompt: 'You are a data analyst who provides insights and recommendations.',
  useTools: true
});

// Create main agent with sub-agents
const mainAgent = await Agent.create({
  name: 'CoordinatorAgent',
  model: 'gpt-4o',
  systemPrompt: 'You coordinate complex tasks between specialized sub-agents.',
  subAgents: [researcher, writer, analyst]
});

Delegation Strategies

Auto Delegation

The main agent uses LLM intelligence to analyze tasks and assign them optimally:

const result = await mainAgent.ask(
  'Research AI market trends, analyze the data, and write an executive summary',
  {
    useSubAgents: true,
    delegation: 'auto'  // AI-powered task distribution
  }
);

Task Analysis

Main agent analyzes the complex task using LLM reasoning.

Agent Matching

Evaluates each sub-agent's capabilities and specializations.

Optimal Assignment

Creates specific subtasks for the most appropriate agents.

Coordinated Execution

Manages execution flow and result aggregation.

Manual Delegation

Explicitly assign specific tasks to specific agents using their IDs:

const result = await mainAgent.ask(
  'Complex multi-step project',
  {
    useSubAgents: true,
    delegation: 'manual',
    taskAssignment: {
      [researcher.id]: 'Research market opportunities in healthcare AI',
      [analyst.id]: 'Analyze market size and growth potential',
      [writer.id]: 'Create executive summary with recommendations'
    }
  }
);

Sequential Delegation

Sub-agents work in sequence, building on previous results:

const result = await mainAgent.ask(
  'Create a comprehensive business plan for an AI startup',
  {
    useSubAgents: true,
    delegation: 'sequential'  // Each agent builds on the previous work
  }
);

Coordination Patterns

Parallel Execution

Sub-agents work simultaneously for maximum efficiency:

const result = await mainAgent.ask(
  'Multi-faceted analysis task',
  {
    useSubAgents: true,
    delegation: 'auto',
    coordination: 'parallel'  // All agents work concurrently
  }
);

Sequential Execution

Sub-agents work in order with context passing:

const result = await mainAgent.ask(
  'Research → Analyze → Report workflow',
  {
    useSubAgents: true,
    delegation: 'auto', 
    coordination: 'sequential'  // Agents work in dependency order
  }
);

Sub-Agent Configuration

Specialized Roles

Configure sub-agents for specific expertise areas:

// Research Specialist
const researcher = await Agent.create({
  name: 'ResearchSpecialist',
  systemPrompt: 'You conduct thorough research using multiple sources and methodologies.',
  knowledge: true,  // Access to knowledge base
  memory: true,     // Remember research context
  useTools: true    // Use research tools
});

// Content Creator
const creator = await Agent.create({
  name: 'ContentCreator', 
  systemPrompt: 'You create compelling content across different formats and audiences.',
  vision: true,     // Process visual content
  useTools: true    // Use content creation tools
});

// Technical Analyst
const analyst = await Agent.create({
  name: 'TechnicalAnalyst',
  systemPrompt: 'You analyze technical data and provide actionable insights.',
  useTools: true    // Use analysis tools
});

Graph Integration

Sub-Agents work seamlessly with Graph workflows for complex orchestration:

import { Agent, Graph } from '@astreus-ai/astreus';

// Create specialized sub-agents
const researcher = await Agent.create({
  name: 'ResearchBot',
  systemPrompt: 'You conduct thorough research and analysis.',
  knowledge: true
});

const writer = await Agent.create({
  name: 'WriterBot',
  systemPrompt: 'You create compelling content and reports.',
  vision: true
});

// Main coordinator with sub-agents
const coordinator = await Agent.create({
  name: 'ProjectCoordinator',
  systemPrompt: 'You coordinate complex projects using specialized teams.',
  subAgents: [researcher, writer]
});

// Create sub-agent aware graph
const projectGraph = new Graph({
  name: 'Market Analysis Project',
  defaultAgentId: coordinator.id,
  subAgentAware: true,
  optimizeSubAgentUsage: true
}, coordinator);

// Add tasks with intelligent sub-agent delegation
const researchTask = projectGraph.addTaskNode({
  name: 'Market Research',
  prompt: 'Research AI healthcare market trends and opportunities',
  useSubAgents: true,
  subAgentDelegation: 'auto'
});

const reportTask = projectGraph.addTaskNode({
  name: 'Executive Report',
  prompt: 'Create comprehensive executive report based on research',
  dependencies: [researchTask],
  useSubAgents: true,
  subAgentCoordination: 'sequential'
});

// Execute with performance monitoring
const result = await projectGraph.run();
console.log('Performance:', projectGraph.generateSubAgentPerformanceReport());

Graph Sub-Agent Features

Automatic Detection: Graph nodes automatically use sub-agents when beneficial
Context Passing: Workflow context flows to sub-agents for better coordination
Performance Optimization: Real-time monitoring and automatic strategy adjustment
Flexible Configuration: Per-node sub-agent settings with inheritance from graph config

Advanced Examples

Content Production Pipeline

const contentPipeline = await Agent.create({
  name: 'ContentPipeline',
  model: 'gpt-4o',
  subAgents: [researcher, writer, analyst]
});

const blogPost = await contentPipeline.ask(
  'Create a comprehensive blog post about quantum computing applications in finance',
  {
    useSubAgents: true,
    delegation: 'auto',
    coordination: 'sequential'
  }
);

Market Research Workflow

const marketResearch = await Agent.create({
  name: 'MarketResearchTeam',
  model: 'gpt-4o',
  subAgents: [researcher, analyst, writer]
});

const report = await marketResearch.ask(
  'Analyze the fintech market and create investor presentation',
  {
    useSubAgents: true,
    delegation: 'manual',
    coordination: 'parallel',
    taskAssignment: {
      [researcher.id]: 'Research fintech market trends and competitors',
      [analyst.id]: 'Analyze market data and financial projections',
      [writer.id]: 'Create compelling investor presentation'
    }
  }
);

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