When AI Agents Start Sharing Skills: The Beginning of a New Kind of Collective Intelligence?

One of the more interesting developments in artificial intelligence is not necessarily how much more capable individual agents are becoming. It is how their capabilities can be shared.

Think about how humans develop expertise.

Someone learns how to solve a problem. They document the process, teach someone else, share their experience or develop a better way of doing something. Over time, that knowledge becomes available to a wider group.

We don't all have to discover the same solution independently.

What if AI agents could work in a similar way?

I think there are two interesting developments emerging here, and although they are related, they represent quite different possibilities.

1. Making skills reusable across agents

The first is already becoming part of the AI ecosystem.

Platforms such as Claude support Agent Skills: reusable packages of instructions, workflows, resources and sometimes executable scripts that give an agent the ability to perform a particular task more effectively.

Instead of repeatedly explaining how something should be done, you create a skill once and make it available to agents that need it.

For example, a business could develop a skill for handling customer complaints, preparing marketing content or processing booking enquiries.

Different agents can then use that same capability when the relevant task arises.

The important shift is that expertise is no longer necessarily embedded in one particular agent. It can be packaged, reused and distributed.

This is already valuable. It means we can build libraries of capabilities that can be shared across different agents and workflows.

But there is another possibility.

2. What if agents could develop and share skills themselves?

Imagine an AI agent working for a small business.

It encounters a recurring problem. It tries different approaches, receives feedback, identifies what works and gradually develops a more effective way of completing the task.

Now imagine that the resulting skill could be made available to other agents operating in similar contexts.

An agent supporting another business could discover that skill, evaluate whether it is relevant and apply or adapt it to its own environment.

Over time, agents could contribute to a growing library of capabilities.

The knowledge would not only flow from humans to agents. It could also flow between agents, with agents contributing improvements based on their own experiences.

This is a different proposition from simply sharing a document or a set of instructions.

It introduces the possibility of an evolving ecosystem of skills, where capabilities are continuously developed, tested, improved and reused.

Of course, this would require mechanisms for validating skills, assessing their reliability, managing versions and controlling what agents are allowed to share. An agent should not automatically publish every solution it discovers, particularly where it involves confidential information or potentially unsafe actions.

But the underlying idea is compelling.

The networking analogy

Think about a group of small business owners attending a networking event.

One business owner shares how they have improved customer retention. Another explains how they manage cancellations. Someone else shares a more effective way of organising their bookings.

The participants take away ideas they can apply in their own businesses.

Now imagine a network of AI agents supporting those businesses.

Rather than simply exchanging information, the agents could share the actual skills and workflows they have developed.

One agent might contribute a better booking-management workflow. Another might improve it. A third might adapt it for a different type of business.

The result could be a collective capability that is more useful than any individual agent's original skill set.

The networking event becomes a useful analogy, but there is an important difference: human participants exchange experiences, while agents could potentially turn those experiences into reusable, operational capabilities.

From individual agents to a shared skills economy

This leads to an interesting question about how we might organise AI ecosystems in the future.

Today, we tend to think about agents as individual digital workers, each configured to perform a particular role.

But perhaps we should start thinking about them as participants in a wider skills economy.

In this model:

• Skills can be created and contributed by humans or agents.
• Agents can discover and access relevant skills.
• Skills can be tested, evaluated and improved.
• Successful capabilities can be shared across participating agents.
• The ecosystem can develop a growing library of reusable expertise.

The more agents participate, the greater the potential for new capabilities to emerge.

This does not mean that more agents automatically produce better intelligence. The quality of the shared skills, the mechanisms for learning and the governance around them will determine whether the network becomes genuinely more capable.

But it creates the possibility of a different kind of network effect.

What could this mean for small businesses?

I find this particularly interesting in the context of small businesses.

An independent salon, personal trainer, tutor or tradesperson cannot realistically afford to build and continuously improve a sophisticated collection of AI agents.

They don't have dedicated AI development teams or the resources to experiment with every new technology.

But what if they could access an environment where agents already have access to a growing collection of skills developed through the experiences of other businesses?

A salon could benefit from a skill developed by another salon's agent. A personal trainer could access capabilities developed within the fitness sector. A business could use specialist workflows without having to build them from scratch.

Each business would retain control over its own data, customers and permissions. What is shared is the reusable capability, not another business's confidential information.

The value of the platform would increasingly lie in the skills ecosystem it provides, rather than simply the individual agents it deploys.

Who owns the intelligence that emerges?

There is also a bigger question here.

If an agent develops a useful skill through its work, who owns that skill? The business that paid for the agent? The platform that provided the environment? The developer who created the underlying agent? Or could it become part of a shared library, subject to agreed permissions and licensing?

And how do we distinguish between a genuinely useful skill and one that simply appears to work in a particular situation?

These questions will become increasingly important as agent ecosystems mature.

Sharing capabilities creates opportunities, but it also introduces questions around intellectual property, accountability, security, quality control and trust.

The infrastructure supporting these exchanges will matter just as much as the intelligence of the agents themselves.

The next step in the evolution of AI?

We have moved from asking what AI can do to asking what AI agents can do.

Perhaps the next question is what happens when agents can build on each other's capabilities.

The first model allows humans to package expertise and distribute it across agents.

The second opens the possibility of agents contributing to the development and evolution of shared expertise themselves.

Together, they point towards something much bigger than a collection of independent digital assistants.

They suggest the emergence of connected environments where skills, knowledge and capabilities can evolve through participation.

Perhaps the future of AI will not be defined by how many agents we build, but by how effectively they can share, develop and build upon each other's skills.