The rapid shift from Generative AI to Agentic AI marks more than a technical milestone—it represents a philosophical change in how machines reason, collaborate, and solve problems. Instead of relying on a single, all-purpose model, Agentic AI introduces a dynamic ecosystem of specialized agents that work together like human teams, each offering a distinct capability or perspective.

One of the most transformative configurations in this space is Collaborate Mode, where multiple agents contribute—either asynchronously or in parallel—to achieve a unified outcome. This mode enables more nuanced problem-solving, especially in complex workflows where different types of reasoning, tools, or perspectives must come together seamlessly.

Inspired by my Medium post, this blog breaks down the architecture, purpose, and code implementation of this mode using the AGNO framework, making the power of distributed machine collaboration more approachable and actionable.

What is Collaborate Mode?

Collaborate Mode is an agent orchestration strategy where multiple intelligent agents receive the same task, operate independently, and deliver unique insights that are then synthesized by a coordinator. This design mirrors how effective human teams operate—through parallel expertise, independent judgment, and collaborative synthesis.

Ideal use cases:

• Brainstorming across different domains
• Aggregating cross-platform knowledge
• Speeding up research through parallelism
• Building consensus across diverse information sources

How it works visually

Imagine each agent as a researcher assigned to a unique platform:

• Reddit Agent gathers opinions from communities
• HackerNews Agent scans developer insights
• Twitter Agent captures trending conversations
• Academic Agent retrieves scholarly context

Each one returns findings from its ecosystem, which the coordinator blends into a single, meaningful response.

AGNO framework code implementation

1. Import modules & tools

from textwrap import dedent
from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.team.team import Team
from agno.tools.arxiv import ArxivTools
from agno.tools.duckduckgo import DuckDuckGoTools
from agno.tools.googlesearch import GoogleSearchTools
from agno.tools.hackernews import HackerNewsTools

2. Define specialized agents

Each agent is built for platform-specific intelligence gathering.

Reddit Agent

reddit_researcher = Agent(
name="Reddit Researcher",
role="Research a topic on Reddit",
model=OpenAIChat(id="gpt-4o"),
tools=\[DuckDuckGoTools()],
add_name_to_instructions=True,
instructions=dedent("""You are a Reddit researcher..."""),
)

HackerNews Agent

hackernews_researcher = Agent(
name="HackerNews Researcher",
model=OpenAIChat("gpt-4o"),
role="Research a topic on HackerNews.",
tools=\[HackerNewsTools()],
add_name_to_instructions=True,
instructions=dedent("""You are a HackerNews researcher..."""),
)

Academic Agent

academic_paper_researcher = Agent(
name="Academic Paper Researcher",
model=OpenAIChat("gpt-4o"),
role="Research academic papers...",
tools=\[GoogleSearchTools(), ArxivTools()],
add_name_to_instructions=True,
instructions=dedent("""You are an academic researcher..."""),
)

Twitter - X Agent

twitter_researcher = Agent(
name="Twitter Researcher",
model=OpenAIChat("gpt-4o"),
role="Research Twitter/X topics",
tools=[DuckDuckGoTools()],
add_name_to_instructions=True,
instructions=dedent("""You are a Twitter researcher..."""),

)

3. Define the team

agent_team = Team(
name="Discussion Team",
mode="collaborate",
model=OpenAIChat("gpt-4o"),
members=[
     reddit_researcher,
     hackernews_researcher,
     academic_paper_researcher,
     twitter_researcher,

],

instructions=[
     "You are a discussion master.",
     "You must conclude the discussion once consensus is reached.",
],

success_criteria="The team has reached a consensus.",
update_team_context=True,
send_team_context_to_members=True,
show_tool_calls=True,
markdown=True,
show_members_responses=True,
)

4. Running the discussion

if __name__ == "__main__":

asyncio.run(
     agent_team.print_response(
         message="Start the discussion on the topic: 'What is the best way to learn to code?'",
         stream=True,
         stream_intermediate_steps=True,
     )
)

Example output

* Reddit: Focus on project-building and freeCodeCamp
* HackerNews: Start with Python and open-source
* Academia: Reinforce with spaced repetition and mentorship
* Twitter/X: Emphasize consistency and public learning
* Team Consensus: Use beginner-friendly languages, build real-world projects, and immerse yourself in learning communities.

Pro tip: Run agents in parallel

asyncio.run(

agent_team.print_response(
     message="Start the discussion on the topic: 'How should we improve remote team collaboration?'",
     stream=True,
     stream_intermediate_steps=True,
))

Using asyncio ensures agents work simultaneously, which dramatically boosts speed and output quality—especially in research-heavy or time-sensitive use cases.

Final thoughts

Collaborate Mode is more than a clever orchestration pattern—it’s the embodiment of distributed intelligence. By mimicking the structure of human brainstorming, it allows AI to perform with greater breadth, depth, and creativity. With frameworks like AGNO making implementation seamless, the age of intelligent, agent-led collaboration is no longer speculative—it’s operational.

As we continue evolving from single-shot prompts to structured autonomy, Collaborate Mode stands out as a key innovation for scalable, multi-perspective problem-solving in AI systems.