One of the most transformative patterns in Agentic AI is team-based orchestration — a collaborative approach where specialized agents work together to fulfill complex goals. In this edition, we explore coordinate mode using the AGNO framework — a design where a team manager delegates, supervises, and integrates the contributions of each agent.

Inspired by my Medium post.

What are agentic AI teams?

An agentic team is a structured collection of AI agents, each performing a specific role with autonomy and tool access. Teams can include roles like:

• Researcher: Finds and filters relevant data
• Writer: Synthesizes content with tone and structure
• Translator: Converts content across languages
• Planner: Organizes execution based on goals

In Coordinate Mode:

• A team manager Agent directs the flow of tasks
• Individual agents handle sub-tasks independently
• Final results are reviewed, refined, and unified by the manager

AGNO Framework: Coordinating a multi-agent content team

Let’s examine a professional-grade configuration of a New York Times-style editorial team, where search, writing, and editorial review are handled by distinct agents.

Imports

from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.team.team import Team
from agno.tools.search import DuckDuckGoTools
from agno.tools.read import Newspaper4kTools

Searcher agent

searcher = Agent(
    name="Searcher",
    role="Searches the top URLs for a topic",
    instructions=[
        "Generate 3 search terms for a topic.",
        "Search the web and return 10 high-quality, relevant URLs.",
        "Prioritize credible sources, suitable for the New York Times."
    ],
    tools=[DuckDuckGoTools()],
    add_datetime_to_instructions=True,
)

Writer agent

writer = Agent(
    name="Writer",
    role="Writes a high-quality article",
    description="Senior NYT writer tasked with long-form editorial content.",
    instructions=[
        "Read all articles using `read_article`.",
        "Write a structured, engaging article of at least 15 paragraphs.",
        "Support arguments with factual citations and ensure clarity.",
        "Never fabricate facts or plagiarize content."
    ],
    tools=[Newspaper4kTools()],
    add_datetime_to_instructions=True,
)

Editor team (Manager agent in Coordinate Mode)

editor = Team(
    name="Editor",
    mode="coordinate",
    model=OpenAIChat("gpt-4o"),
    members=[searcher, writer],
    description="You are a senior NYT editor coordinating the team.",
    instructions=[
        "Delegate research to the search agent.",
        "Delegate drafting to the writer.",
        "Review, proofread, and enhance the final article.",
        "Maintain NYT-level quality, structure, and tone."
    ],
    add_datetime_to_instructions=True,
    send_team_context_to_members=True,
    show_members_responses=True,
    markdown=True,
)

Running the team

Method 1: Print output directly
editor.print_response("Write an article about latest developments in AI.")

Method 2: Get raw result
response = editor.run("Write an article about latest developments in AI.")

Key parameters explained

Pro tip: Define success criteria

Adding success criteria helps agents align their efforts with measurable outcomes.

strategy_team = Team(
    members=[market_analyst, competitive_analyst, strategic_planner],
    mode="coordinate",
    name="Strategy Team",
    description="A team that develops strategic recommendations",
    success_criteria="Produce actionable strategic recommendations supported by market and competitive analysis",
)
response = strategy_team.run(
    "Develop a market entry strategy for our new AI-powered healthcare product"
)

This ensures agents not only act — but act with strategic purpose and direction.

Conclusion

Coordinate Mode in Agentic AI exemplifies intelligent task distribution, where specialized agents work under centralized leadership to deliver complex, high-quality outputs. The AGNO framework simplifies this orchestration through agent roles, tool integration, and goal alignment — enabling scalable, auditable AI workflows.

From editorial pipelines to business strategy engines, multi-agent coordination is redefining how work gets done — autonomously, intelligently, and collaboratively.