Multi-Agent Orchestration with Strands

Why Multi-Agent Systems?

Single agents hit a ceiling when tasks require diverse expertise. Multi-agent systems decompose complex problems across specialized agents that collaborate to achieve outcomes no single agent could.

In strands there are multiple ways to build multi agent systems:

1. Agent as a Tool - This the simplest way to develop a multi agent system in strands. In this specialized agents are given to orchestrator agent as a tool. Which decide when to call which agent.

2. Graph - Graph gives a determinitic approach to multi agent system execution. In this approach specialized agents fucntion as nodes and data flow is defined by edges connecting them.

3. Swarm - This is the most advanced way to develop a multi agent system in strands. In this agents are given a common goal and they collaborate to achieve it.

Defining Multi Agents

import logging
from strands import Agent
from strands.multiagent import Swarm

# Enable debug logs and print them to stderr
logging.getLogger("strands.multiagent").setLevel(logging.DEBUG)
logging.basicConfig(
    format="%(levelname)s | %(name)s | %(message)s",
    handlers=[logging.StreamHandler()]
)

# Create specialized agents
researcher = Agent(name="researcher", system_prompt="You are a research specialist...")
coder = Agent(name="coder", system_prompt="You are a coding specialist...")
reviewer = Agent(name="reviewer", system_prompt="You are a code review specialist...")
architect = Agent(name="architect", system_prompt="You are a system architecture specialist...")

# Create a swarm with these agents, starting with the researcher
swarm = Swarm(
    [coder, researcher, reviewer, architect],
    entry_point=researcher,  # Start with the researcher
    max_handoffs=20,
    max_iterations=20,
    execution_timeout=900.0,  # 15 minutes
    node_timeout=300.0,       # 5 minutes per agent
    repetitive_handoff_detection_window=8,  # There must be >= 3 unique agents in the last 8 handoffs
    repetitive_handoff_min_unique_agents=3
)

Running the agents

# Execute the swarm on a task
result = swarm("Design and implement a simple REST API for a todo app")
# Or use invoke_async for async execution: result = await swarm.invoke_async(...)

# Access the final result
print(f"Status: {result.status}")
print(f"Node history: {[node.node_id for node in result.node_history]}")

Conclusion

Multi-agent orchestration is the future of complex AI workflows. Strands provides a clean abstraction for designing these systems. Strands provides various ways to develope multi agent systems that can achieve different agentic pattern.