Can Agentic AI Collaborate with Each Other?

• Michael Willson
• April 21, 2025

The short answer is, yes, they can. Agentic AI or automatic AI systems are now teaming up in real situations. These systems aren’t stuck working alone anymore. They share tasks, make decisions, and talk to each other. And they do it without needing people to guide every step. That’s what makes agentic AI so different from older systems.

When people first learn agentic AI, one big question is whether these systems can actually work as a team. And the answer is already playing out in real businesses. From cars to healthcare, this tech is taking on more team-based roles.

How Does Agentic AI Collaborate with Each Other?

Agentic AI collaborating works through structure. It needs memory systems, smart frameworks, and agents designed for teamwork. Let’s go through how this happens.

What’s a multi-agent system?

In many setups, agentic AI runs as a group. You might hear this called a multi-agent system. Here’s how it works: there’s often one agent at the top, giving out tasks. The rest follow instructions, but they still act on their own.

Each agent in the group has its own job. Some focus on planning. Others handle writing code or checking for mistakes. What makes this interesting is the way they all stay connected. They use shared memory, so no one gets left out of the loop. That’s how they stay on track, even if tasks change.

What are Generative AI Networks?

These are another way to get agents working together. Think of each AI as having a role. One may act like a coder, another like a tester, and another like a designer. Together, they work on the same task. This setup leads to smarter results than a single AI could manage on its own.

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How do frameworks help?

If you want to understand how to learn agentic AI, look at tools like LangGraph, AutoGen, and CrewAI. These are software kits that help developers build agent teams. They include features like memory sharing, agent chat, and task planning.

AutoGen is especially good for conversational teamwork. It lets agents go back and forth, asking each other questions, adjusting tasks, and sharing updates. It’s this back-and-forth that makes the work dynamic and productive.

What’s chaining in agentic AI?

One agent can kick off a chain of actions. It starts something, hands part of it off, and the next agent keeps going. Each step can include feedback. That way, agents don’t just pass work forward, they help improve it too.

This loop system means agents can learn from their past behavior. Over time, this leads to better and faster results.

Real-Life Examples of Agentic AI Collaboration?

You don’t need to guess, the examples are already out there. Big companies are already using these systems. Let’s look at a few.

Industry	Company/Example	How Agentic AI Is Used
Automotive	Ford	AI agents create 3D models and simulate part failures. They collaborate to speed up testing.
Marketing	Adobe	Agents manage content, personalize targeting, and optimize websites through teamwork.
Healthcare	Hospitals	Agents handle calls, reminders, and data collection—freeing up medical staff.
Entertainment	Disney + Nvidia + DeepMind	AI agents power robot characters with lifelike movement for theme park interactions.

How is the auto industry using agentic AI?

Ford is a good example. Their engineering team uses AI agents from places like OpenAI and Nvidia. These agents build 3D models and check how parts might fail. What used to take hours now takes seconds. The agents talk to each other, sharing results and fixing problems fast. That’s agentic AI collaborating in action.

What about in marketing?

Adobe uses AI agents in its tools. They have two: Agent Orchestrator and Brand Concierge. These agents help marketers by managing websites, tweaking content, and choosing who to target. The agents work together to figure out what works best, and then they act on it. This cuts down time and boosts results.

Can agentic AI help in healthcare?

Yes. In hospitals, agentic AI systems take care of the boring stuff. They call patients, send reminders, and gather basic recovery info. This teamwork between agents helps doctors and nurses focus on serious cases. It shows one of the biggest benefits: taking care of smaller tasks without needing people to step in.

What’s happening in entertainment?

Disney is teaming up with Nvidia and Google DeepMind. They’re making robot characters that act more naturally. These robots, like one called Blue, are run by agentic AI systems. The agents inside them work together to make the robot’s movements look smooth and real. This tech makes park experiences more engaging for visitors.

What tech makes this teamwork possible?

Several tools and tech pieces are helping this grow fast. Without these, agentic AI systems wouldn’t be able to function in groups.

What role do AI chips play?

Nvidia’s new AI chips, like Blackwell Ultra and Vera Rubin, are big upgrades. They help agents run faster and handle harder jobs. This lets agents work in real time and still talk to each other clearly.

How are integration platforms helping?

Some companies are joining forces. Nvidia and Oracle have built systems where AI agents can plug in and start working. Businesses can launch agent teams without building everything from scratch. This helps more companies start using agentic AI.

What about tools to build your own AI agents?

Accenture has a tool called AI Refinery. With it, people can make custom agents, even without coding skills. This opens the door for smaller teams or non-tech workers to join in. It supports teamwork among agents that are built for one specific task.

What problems still need fixing?

Agentic AI isn’t perfect yet. Several challenges still need attention.

What’s the issue with compatibility?

Agents built by different people don’t always get along. That’s because there’s no set way to make them talk to each other. Until that’s solved, full teamwork between systems from different developers will stay tricky.

Are there ethical issues?

Yes, and they matter. When agents make choices on their own, we need to know those choices match real-world values. Setting rules for how agents act is something companies are still working on.

Is security a problem?

It can be. AI agents that talk across networks might be at risk. If someone hacks in, they could mess with how the agents behave. That’s why strong protection systems are just as necessary as smart AI tools.

Where is all this going next?

Let’s look at where agentic AI collaboration is heading.

Are more companies adding AI agents?

Yes. Cisco, for example, is building more services using agentic AI. Their Webex AI Agent is now widely available. It’s made to improve customer service by working independently but still staying helpful.

What’s happening in communication?

Some researchers are designing systems where agents learn from one another. One new project, called AgentNet, is all about helping agents share skills and knowledge over time.

Can agentic AI work with humans?

Yes. In AR spaces, AI agents now help teams work better together. They generate whiteboards or notes automatically. This helps team members stay on the same page without doing it all themselves.

As collaborative dynamics between Agentic AI systems become more prominent, understanding the principles behind them becomes essential. Earning a Certified Agentic AI Expert™ certification can help professionals grasp these evolving interactions.

Conclusion

If you’re trying to learn agentic AI or wondering how to learn agentic AI properly, looking at these examples and getting certified are the best places to begin. Real-life use cases show how far this tech has come. From cars to hospitals to robots in theme parks, agentic AI collaborating is already happening.

As new tech rolls out, and problems like security and ethics are tackled, we’ll see even stronger teamwork among AI agents. Learning agentic AI now means stepping into the future early, where machines don’t just run tasks, but actually work as a team.