User Query
    ↓
Orchestrator Agent (Router)
    ↓
┌─────────────────┬─────────────────┬─────────────────┐
│ Research Agent  │ Product Agent   │ Travel Agent    │
│ (Factual Info)  │ (Recommendations)│ (Itineraries)   │
└─────────────────┴─────────────────┴─────────────────┘
    ↓
Combined Response

from strands.tools import tool

@tool
def research_assistant(query: str) -> str:
    """A specialized agent for research-related queries."""
    research_agent = Agent(
        model=LLM,
        system_prompt="You are a specialized research assistant.",
        tools=[retrieve, http_request],
    )
    return str(research_agent(query))

# Orchestrator uses these agents as tools
orchestrator = Agent(
    model=model,
    tools=[research_assistant, product_assistant, travel_assistant],
    system_prompt="Route queries to the appropriate specialist."
)

    Agent A
      ↕️
    Agent B ⇄ Agent C
      ↕️      ↕️
    Agent D ⇄ Agent E
    
(Agents dynamically hand off tasks)

[Start] → [Agent A] → [Agent B]
              ↓          ↓
         [Agent C] → [Agent D] → [End]

import os
from strands import Agent
from strands.models.litellm import LiteLLMModel
from strands.tools import tool

# Define specialized worker agents as tools
@tool
def math_expert(query: str) -> str:
    """Expert in mathematical calculations and problem-solving."""
    model = LiteLLMModel(
        client_args={"api_key": os.getenv("NEBIUS_API_KEY")},
        model_id="nebius/deepseek-ai/DeepSeek-V3-0324",
    )
    agent = Agent(
        model=model,
        system_prompt="You are a math expert. Solve problems step-by-step.",
    )
    return str(agent(query))

@tool
def science_expert(query: str) -> str:
    """Expert in scientific concepts and explanations."""
    model = LiteLLMModel(
        client_args={"api_key": os.getenv("NEBIUS_API_KEY")},
        model_id="nebius/deepseek-ai/DeepSeek-V3-0324",
    )
    agent = Agent(
        model=model,
        system_prompt="You are a science expert. Explain concepts clearly.",
    )
    return str(agent(query))

# Create orchestrator that routes to specialists
orchestrator = Agent(
    model=LiteLLMModel(
        client_args={"api_key": os.getenv("NEBIUS_API_KEY")},
        model_id="nebius/deepseek-ai/DeepSeek-V3-0324",
    ),
    tools=[math_expert, science_expert],
    system_prompt="""You are a master assistant. Route queries:
    - Math problems → math_expert
    - Science questions → science_expert
    - Simple questions → answer directly
    """,
)

# Use the orchestrator
response = orchestrator("Calculate the speed of light and explain its significance")
print(response)
# The orchestrator will call both math_expert and science_expert!