How to Build a Simple Multi-Agent Workflow: A Step-by-Step Beginner’s Guide

1. What Is a Multi-Agent Workflow?

A multi-agent workflow is a process where multiple “agents” (small, focused programs) work together to complete a larger task. Each agent has a clear role—such as planning, researching, or writing—and the workflow controls how information flows between them. Here we will discuss how to prepare simple multi-agent workflow for beginners:

Think of it like a small team:

• Agent 1: Planner (decides what to do)
• Agent 2: Researcher (gathers information)
• Agent 3: Writer (creates the final answer)

The workflow defines who talks to whom, in what order, and under what conditions.

Key Terms You Need to Know

Before we go further, it’s helpful to understand some basic concepts that appear in tools like LangGraph and other workflow frameworks.

1.1 State

State is the shared memory of your workflow. It stores everything the agents need to know so far.

Examples of state:

• User input (e.g., “Explain multi-agent workflows to a beginner”)
• Agent outputs (e.g., “Draft outline created”, “Research notes collected”)
• Flags or status (e.g., needs_more_research = true)

You can think of state as a single object or dictionary that all agents can read from and write to.

Simple example in pseudocode:

state = {
    "user_question": "How to build a multi-agent workflow?",
    "outline": None,
    "research_notes": None,
    "draft": None,
    "needs_more_research": False
}

1.2 Nodes

A node is a step in your workflow—usually representing one agent or one specific action.

Examples of nodes:

• PlanAgent node: Creates an outline.
• ResearchAgent node: Calls tools or APIs to gather information.
• WriterAgent node: Generates a final article draft.

Each node takes the current state, does something, and returns an updated state.

def PlanAgent(state):
    # read state
    question = state["user_question"]
    # create a simple outline
    outline = ["Introduction", "Key concepts", "Example workflow", "Conclusion"]
    # update state
    state["outline"] = outline
    return state

1.3 Edges

Edges define how you move from one node to the next. They represent connections in the workflow graph.

• Edge from PlanAgent → ResearchAgent
• Edge from ResearchAgent → WriterAgent

In code or configuration, this is often defined as a graph or mapping:

edges = {
    "PlanAgent": "ResearchAgent",
    "ResearchAgent": "WriterAgent"
}

1.4 Conditional Edges

Conditional edges are edges that depend on some condition in the state. They answer the question: “What should happen next, given the current situation?”

Example:

• If needs_more_research == True → go back to ResearchAgent.
• Else → go to WriterAgent.

Pseudocode:

def next_step_after_research(state):
    if state["needs_more_research"]:
        return "ResearchAgent"  # loop back
    else:
        return "WriterAgent"

1.5 Tool Execution

Tool execution is when an agent calls an external function or service to get something done. Tools can be:

• Web search APIs
• Database queries
• Calculators
• Internal business APIs

For example, your ResearchAgent may call a search_web() tool:

def search_web(query):
    # pretend this calls an external API
    return ["Result 1", "Result 2"]

def ResearchAgent(state):
    question = state["user_question"]
    notes = search_web(question)
    state["research_notes"] = notes
    return state

Understanding state, nodes, edges, and conditional edges gives you the mental model for how multi-agent workflows are structured. Tools like LangGraph use these exact ideas: you define nodes (agents), connect them with edges, and manage flow based on the shared state.

2. Benefits of Multi-Agent Workflows for Beginners

You do NOT have to be an advanced AI engineer to gain value from multi-agent workflows. For beginners, the advantages are:

• Clear separation of responsibilities
  Each agent does one thing well, which is easier to design and debug.
• Step-by-step control
  You can see and adjust each step instead of dealing with a single, monolithic prompt.
• Easier to extend over time
  Start with 2 agents; later add more (e.g., a “Fact Checker” or “Formatter” agent).
• Better reliability
  You can insert checks, loops, and conditions to avoid obvious errors.

If you’re new to AI workflows, knowing the practical benefits helps you justify the extra setup. Instead of a single black-box prompt, you get a transparent, modular system that’s easier to improve gradually—exactly the philosophy behind frameworks like LangGraph. Learn more An Introduction to Multi-Agent Workflows

3. Designing a Simple 3-Agent Workflow

Let’s build a concrete example you can adapt:

Scenario:
User asks: “Explain how to build a simple multi-agent workflow.”
Our workflow:

1. Planner Agent – Understands the question and creates a structured outline.
2. Research Agent – Gathers key points or facts (simulated).
3. Writer Agent – Produces the final answer using the outline and research.

3.1 Defining the State

We’ll start by defining a basic state structure.

state = {
    "user_question": "",
    "outline": [],
    "research_notes": [],
    "draft": "",
    "needs_more_research": False
}

When a user sends a question:

state["user_question"] = "How do I build a simple multi-agent workflow?"

3.2 Planner Agent (Node 1)

Goal: Turn the question into a clear outline.

def PlannerAgent(state):
    question = state["user_question"]
    
    # Very simple "planning"
    outline = [
        "Introduction to multi-agent workflows",
        "Key concepts: state, nodes, edges",
        "Example 3-agent workflow",
        "Practical tips and best practices",
        "Conclusion"
    ]
    
    state["outline"] = outline
    return state

3.3 Research Agent (Node 2)

Goal: Collect supporting points or facts for each outline item.

def ResearchAgent(state):
    outline = state["outline"]
    notes = []
    
    for item in outline:
        # In a real system, you might call a search tool here.
        # We'll simulate it.
        notes.append(f"Key points for section: {item}")
    
    state["research_notes"] = notes
    
    # For illustration, we decide we have enough research
    state["needs_more_research"] = False
    return state

3.4 Writer Agent (Node 3)

Goal: Use the outline and research notes to generate a final draft.

def WriterAgent(state):
    outline = state["outline"]
    notes = state["research_notes"]
    
    draft_parts = []
    for section, note in zip(outline, notes):
        draft_parts.append(f"{section}\n{note}\n")
    
    full_draft = "\n".join(draft_parts)
    state["draft"] = full_draft
    return state

3.5 Connecting the Nodes with Edges

We define the basic flow:

# Simple graph-like routing
edges = {
    "PlannerAgent": "ResearchAgent",
    "ResearchAgent": "WriterAgent"  # default
}

But now let’s add a conditional edge after ResearchAgent.

def next_after_research(state):
    if state["needs_more_research"]:
        return "ResearchAgent"  # loop again
    else:
        return "WriterAgent"

3.6 Running the Workflow (Simple Orchestrator)

Finally, we need a small orchestrator that:

1. Starts at PlannerAgent
2. Follows the edges
3. Stops after WriterAgent

def run_workflow(initial_state):
    current_node = "PlannerAgent"
    
    while True:
        if current_node == "PlannerAgent":
            initial_state = PlannerAgent(initial_state)
            current_node = "ResearchAgent"
        
        elif current_node == "ResearchAgent":
            initial_state = ResearchAgent(initial_state)
            # conditional edge
            current_node = next_after_research(initial_state)
        
        elif current_node == "WriterAgent":
            initial_state = WriterAgent(initial_state)
            break  # end of workflow
        
        else:
            raise ValueError(f"Unknown node: {current_node}")
    
    return initial_state

# Example usage:
state["user_question"] = "How do I build a simple multi-agent workflow?"
final_state = run_workflow(state)
print(final_state["draft"])

This is a minimal multi-agent workflow with:

• 3 nodes (agents)
• Shared state
• A simple conditional edge
• Simulated tool execution (could be extended later)

Seeing a complete but simple example makes the idea concrete. This is similar to how LangGraph would wire up LLM agents and tool nodes in a graph, but here it is simplified to pseudocode beginners can understand.

4. Connecting the Example to LangGraph (Beginner-Friendly View)

LangGraph is a framework that lets you declare workflows like the one above using graph concepts:

• Each agent or tool is a node.
• Transitions between agents are edges.
• Decisions are implemented through conditional edges.
• All data flows through a centralized state.

For beginners, the key ideas to take from our example when approaching LangGraph are:

• You break your task into several clear roles (Planner, Researcher, Writer).
• You define how data moves through those roles.
• You can easily add conditions (loops, branching decisions) without rewriting everything.

You don’t need to learn every LangGraph feature at once. Start by mapping your simple Python-style pseudocode into:

• Nodes = small Python functions or LLM calls
• State = a shared dictionary or typed object
• Edges = routing rules based on state values

Many beginners are intimidated by frameworks. By seeing that LangGraph is “just” a structured way to express the same concepts we coded manually, you reduce the learning curve. You can experiment with simple graphs first, then gradually adopt more advanced features like streaming, tool invocation, and retries.

5. Real-World Use Case: Automated Blog Assistant

To make it more tangible, let’s look at a common real-world scenario: an automated blog assistant that helps you draft SEO articles.

5.1 Goal

You want to:

• Input a topic (e.g., “remote work productivity tips”).
• Get back:
  • A proposed outline
  • Key research points
  • A first draft you can manually edit

5.2 Basic Multi-Agent Setup

Agents:

1. SEO Planner Agent
   • Analyzes the topic
   • Suggests headings (H2/H3)
   • Identifies primary and secondary keywords
2. Research Agent
   • Gathers background info (from tools, internal docs, or knowledge base)
3. Drafting Agent
   • Produces a long-form article using outline + research

State example:

state = {
    "topic": "remote work productivity tips",
    "target_keywords": [],
    "outline": [],
    "research_notes": [],
    "draft": "",
    "needs_more_research": False
}

You can reuse the same workflow pattern:

• SEOPlannerAgent → ResearchAgent → (conditional loop) → DraftingAgent

Where the conditional edge might decide to:

• Loop back to ResearchAgent if:
  • Research notes are too short
  • Some sections have missing information
• Move to DraftingAgent once all sections are covered

Real-world use cases help you see direct business value: content generation, customer support workflows, data analysis pipelines, and more. The same core ideas—state, nodes, edges, and conditional edges—apply across domains, and frameworks like LangGraph are built to standardize this pattern.

6. Best Practices for Beginners

When you’re first building multi-agent workflows, keep things simple and follow these guidelines.

6.1 Start Small and Incremental

• Begin with 2 agents (e.g., Planner + Writer).
• Once that works, add a Research Agent as a third node.
• Don’t introduce too many conditional edges in the first version.

6.2 Keep Each Agent Focused

A clear division of responsibility makes systems easier to debug:

• One agent = one main responsibility
• Avoid “do everything” mega-agents
• Examples:
  • Plan / Outline
  • Research / Retrieve
  • Write / Summarize
  • Review / Critique

6.3 Design Your State Carefully

• Use clear, descriptive keys: research_notes, draft, user_question.
• Prefer simple structures first (lists, strings, dicts).
• Add metadata as needed (e.g., timestamps, confidence scores).

6.4 Plan for Error Handling

• What happens if a tool fails?
• What if research returns nothing?
• Where should the workflow stop or ask for human input?

You can add status flags in state:

state["error"] = None
state["tool_status"] = "ok"  # or "failed"

And then use conditional edges to:

• Retry a node
• Route to a fallback node
• Ask for human intervention

6.5 Log Intermediate Outputs

For debugging and learning:

• Print or log outputs after each node.
• Store versions of the state.
• Compare workflows when you make changes.

Best practices prevent small experiments from becoming unmaintainable as they grow. The same principles are used in production-ready workflow systems, including LangGraph-based applications.

7. Common Mistakes to Avoid

Beginners often run into avoidable problems:

7.1 Overloading a Single Agent

• Problem: One agent is responsible for planning, researching, and writing.
• Impact: Hard to debug, less reusable, more brittle prompts.
• Fix: Split into at least 2–3 simple agents.

7.2 Vague or Unstructured State

• Problem: Throwing everything into one big “text blob”.
• Impact: Conditional logic becomes harder; later agents can’t easily find what they need.
• Fix: Use structured keys like outline, section_summaries, final_answer.

7.3 No Clear Stopping Condition

• Problem: Loops that never terminate or workflows that keep adding text.
• Impact: Wasted tokens, confused behavior.
• Fix: Define explicit end nodes and stopping rules.

7.4 Skipping Validation

• Problem: Trusting the final answer without checks.
• Impact: Errors or hallucinations go unnoticed.
• Fix: Add a simple Review Agent later that checks for:
  • Missing sections
  • Obvious contradictions
  • Lack of sources (if required)

Avoiding these common mistakes lets you scale your workflow from a toy example to something you can rely on in practical projects, whether you use plain Python, LangGraph, or other orchestration tools.

8. Summary: Your First Multi-Agent Workflow

To recap, building a simple multi-agent workflow involves:

• Defining a shared state that holds all information.
• Creating nodes (agents) with clear roles.
• Connecting them with edges that define the order of execution.
• Using conditional edges for loops and decisions.
• Adding tool execution where agents call external services.

You can start with a 2–3 agent pipeline (Planner → Researcher → Writer) and extend it over time. Frameworks like LangGraph provide a structured, graph-based way to express exactly what we’ve done in pseudocode, making your workflows more maintainable and scalable.

Published by Kamrun Analytics Inc., Last update: November 22, 2025