Introduction: From “Chat” to “Work”

Until 2024, we were “Chatting” with AI. However, in 2026, AI has become a colleague that does “Work”.

Agentic Workflow is a mechanism where, instead of asking AI for a single task, you give it a “Goal” and let it autonomously select procedures, tools, and self-correct to achieve it.

For example, instead of saying “Fix this bug”, you command: “Resolve Issue #123 in this repository, pass the tests, and create a PR”.

1. Why “Multi-Agent”?

It has been proven that coordinating multiple small specialized models is more accurate and cost-effective than making one super-performance model (like GPT-5) do everything.

Role Specialization

By narrowing down roles (Personas) such as 'Python Specialist', 'Legal Specialist', 'Translation Specialist', hallucinations are reduced.

Mutual Criticism

Create a loop where a 'Reviewer' agent inspects the artifacts of a 'Creator' agent and rejects them if they are not good enough.

Parallel Processing

Separate agents perform 'Market Research' and 'Competitor Analysis' simultaneously, and finally, a 'Strategy Planning' agent integrates them.

2. Comparison of the Big Three Frameworks

As of 2026, frameworks for building Agentic AI have consolidated into three major ones.

CrewAI: Build a Team Fastest

from crewai import Agent, Task, Crew

# 1. Define Agents
researcher = Agent(
 role='Tech Researcher',
 goal='Uncover cutting-edge AI trends',
 backstory='You are a silicon valley veteran...',
 tools=[search_tool]
)

writer = Agent(
 role='Tech Writer',
 goal='Write compelling blog posts',
 backstory='You clarify complex topics...'
)

# 2. Define Tasks
task1 = Task(description='Research AI Agents in 2026', agent=researcher)
task2 = Task(description='Write a blog post based on research', agent=writer)

# 3. Form Team & Execute
crew = Crew(agents=[researcher, writer], tasks=[task1, task2])
result = crew.kickoff()

With just this code, a workflow where “Researcher researches and passes the results to Writer to write an article” is completed.

LangGraph: Controlling Loops and Branches

In business contexts, complex logic like “Re-investigate if the results are not good” or “Ask for human approval under specific conditions” is required. LangGraph realizes this.

from langgraph.graph import StateGraph, END

# Define State
class AgentState(TypedDict):
 input: str
 output: str
 decision: str

# Conditional Logic
def check_quality(state):
 if state['decision'] == 'approve':
 return END
 else:
 return "rewrite"

workflow = StateGraph(AgentState)

# Add Nodes
workflow.add_node("draft", write_draft)
workflow.add_node("review", review_draft)
workflow.add_node("rewrite", revise_draft)

# Define Edges
workflow.add_edge("draft", "review")
workflow.add_conditional_edges(
 "review",
 check_quality,
 {END: END, "rewrite": "rewrite"}
)
workflow.add_edge("rewrite", "review") # Loop structure

app = workflow.compile()

The strength of LangGraph is that you can design agent movements based on Graph Theory.

3. Agent Design Patterns

There are established patterns not just for connecting agents, but for “how to make them think”.

Pattern 1: Reflection

A pattern where you instruct the agent to “Review your output and list the bad points”, and regenerate based on that feedback. Just including this dramatically improves the quality of code generation and writing.

Pattern 2: Tool Use (RAG vs Agentic RAG)

Traditional RAG was just “Search and Answer”. Agentic RAG has an autonomous loop: “Search, and if judged that information is insufficient, search again with a different keyword”. AI that answers “I don’t know” is a thing of the past.

Pattern 3: Human-in-the-loop

If full automation is scary, insert a “Human Approval Node” right before important decisions (sending emails, deploying code, etc.). In LangGraph, this can be achieved with a single setting interrupt_before=["approval_node"], making it wait until a human gives the OK.

4. Practice: Building a “Blog Writing Team”

Actually, this blog post was also written with the support of an AI agent team (though the final adjustment is human).

Conclusion: Are You a “Player” or a “Manager”?

AI that writes code, AI that draws pictures, AI that does research. Individual capabilities are already exceeding humans.

However, it is still only humans who can combine them to produce “meaningful results”. What is required of engineers in 2026 is “AI Team Management Skills” rather than coding skills.

Now, let’s hire your first Crew.

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