Agent Workflows: Building Multi-Agent Systems with Strands Agents SDK

Understanding Workflows

What is an Agent Workflow?

An agent workflow coordinates tasks across multiple agents, where each agent performs a specialized function in a defined sequence. You break a complex problem into components, assign each to the agent best suited to it, and control the order, dependencies, and information flow between them. Use a workflow when a process needs a specific, repeatable execution pattern.

Components of a Workflow Architecture

A workflow architecture consists of three key components:

1. Task Definition and Distribution

• Task Specification: Clear description of what each agent needs to accomplish
• Agent Assignment: Matching tasks to agents with appropriate capabilities
• Priority Levels: Determining which tasks should execute first when possible

2. Dependency Management

• Sequential Dependencies: Tasks that must execute in a specific order
• Parallel Execution: Independent tasks that can run simultaneously
• Join Points: Where multiple parallel paths converge before continuing

3. Information Flow

• Input/Output Mapping: Connecting one agent’s output to another’s input
• Context Preservation: Maintaining relevant information throughout the workflow
• State Management: Tracking the overall workflow progress

When to Use a Workflow

Workflows excel in scenarios requiring structured execution and clear dependencies:

• Complex Multi-Step Processes: Tasks with distinct sequential stages
• Specialized Agent Expertise: Processes requiring different capabilities at each stage
• Dependency-Heavy Tasks: When certain tasks must wait for others to complete
• Resource Optimization: Running independent tasks in parallel while managing dependencies
• Error Recovery: Retrying specific failed steps without restarting the entire process
• Long-Running Processes: Tasks requiring monitoring, pausing, or resuming capabilities
• Audit Requirements: When detailed tracking of each step is necessary

Consider other approaches (swarms or graphs) for simple tasks, highly collaborative problems, or situations requiring extensive agent-to-agent communication.

Implementing Workflow Architectures

Creating Workflows with Strands Agents

Strands Agents SDK allows you to create workflows using existing Agent objects, even when they use different model providers or have different configurations.

Sequential Workflow Architecture

graph LR
    Agent1[Research Agent] --> Agent2[Analysis Agent] --> Agent3[Report Agent]

In a sequential workflow, agents process tasks in a defined order, with each agent’s output becoming the input for the next:

from strands import Agent

# Create specialized agents
researcher = Agent(system_prompt="You are a research specialist. Find key information.", callback_handler=None)
analyst = Agent(system_prompt="You analyze research data and extract insights.", callback_handler=None)
writer = Agent(system_prompt="You create polished reports based on analysis.")

# Sequential workflow processing
def process_workflow(topic):
    # Step 1: Research
    research_results = researcher(f"Research the latest developments in {topic}")

    # Step 2: Analysis
    analysis = analyst(f"Analyze these research findings: {research_results}")

    # Step 3: Report writing
    final_report = writer(f"Create a report based on this analysis: {analysis}")

    return final_report

This sequential workflow creates a pipeline where each agent’s output becomes the input for the next agent, allowing for specialized processing at each stage. For a functional example of sequential workflow implementation, see the agents_workflow.py example in the Strands Agents SDK documentation.

Conclusion

Workflows give you explicit control over multi-step processes: a defined execution order, managed dependencies, and context passed between stages. Use the built-in workflow tool when you want that orchestration handled for you, or implement the coordination yourself when a step needs custom behavior.

Tags

multi-agent8