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AI agents are everywhere, powering the way we brainstorm, plan, write, and create. They’re in our workflow, our apps, and in the websites we visit. This invisible army of agents has diverse tasks to carry out, but the vast majority of them share this much in common: they’re lone wolves, operating independently.
It’s simpler to code a single agent, but there are drawbacks to having one brain – no matter how smart – making all the decisions. From a reasoning perspective, not to mention reach – one agent can’t connect to all systems – there are clear benefits to favoring a multi-agent framework. Not only does this mitigate bottlenecks, but it connects the hive mind, enabling multiple AIs to collaborate, each levering their own expertise and ecosystem reach.
It’s clear that the future of artificial intelligence lies in multi-agent systems: networks of autonomous agents that can problem-solve together. This is particularly true of blockchain networks, where collaborative agents deployed across multiple chains can deliver smarter, more feature-rich outcomes.
The digital world we inhabit comprises an array of databases, websites, and infrastructure layers, some closed-source, some open, but all operating independently of one another. Making sense of all this information calls for connecting the dots – a task for which AI agents are infinitely more suited than humans. But to achieve that, they need to be able to reach the dots by having the ability to interact with a range of on- and offchain systems.
An agent deployed on Ethereum, no matter how powerful, can only react to events and data that is fed to it on Ethereum; it can’t take action on Solana, such as executing a swap, because its system access doesn’t extend that far. But the limitations of single agent systems are about more than mere interoperability: they’re also about specialization.
Different agents excel at different things, based on the model they’ve been fed and the parameters they’ve been set. Just as no human knows all human knowledge, but collectively we know the entire sum, no agent can outperform multiple agents working in parallel. Blockchain businesses and traditional enterprises alike are aware that connecting specialized AI agents is the way to optimize outcomes.
As Deloitte explains, “Because agents are often domain-specific, they thrive when they can easily pull exactly the data they need. A well-designed architecture might allow an AI agent to query a customer transactions database, another to retrieve market prices from a data lake and another to fetch a document from a content repository, all through standardized interfaces.”
Gartner predicts AI agent ecosystems playing a dominant role in user experience by 2028, collaborating “across multiple applications and multiple business functions, allowing users to achieve goals without interacting with each application individually.” For an idea of how this might look, the blockchain industry is the obvious place to start, where a combination of open-source software; composability; and multiple networks and infra layers necessitates multi-agent solutions to connect the industry.
To gain an idea of what a multi-agent system looks like in practice, consider Anemoi. Developed by Coral Protocol, it provides a good idea of what the next wave of multi-agent solutions will be capable of achieving. Agents can communicate directly, while scalability is taken care of thanks to support for worker agents paired with a small LLM planner, enabling large-scale deployment under tighter resource budgets.
In terms of operation, Anemoi’s semi-centralized planner agent proposes an initial plan before assigning it to domain-specialized worker agents. These communicate directly, monitor progress, assess results, and propose refinements – all undertaken in real time. Its impressive GAIA benchmark (accuracy of 52.73% working with OWL worker configurations, GPT-4.1-mini as planner, and GPT-4o as workers) shows real promise.
What solutions such as Anemoi demonstrate is that when it comes to models, bigger doesn’t always equal better. When properly architected, mini models can get the job done efficiently without running into scalability or resource challenges. Just as Bitcoin has demonstrated that individual computers can become an unstoppable global network of hashing power, agents running mini-models can outperform powerful single agent models.
Just as the search engine helped mainstream the internet, providing a universal portal to discover everything that the web holds, the emergence of the agentic web will be the moment when AI is able to realize its full potential. This will make everything we’ve experienced with AI so far look like child’s play.
It’s one thing to order an agent to send you a daily weather update or explain how DEX aggregators work. It’s quite another to be able to order agents to coordinate your entire week based upon weather patterns, social events, trading opportunities, and market forces. But that’s the future that’s being quietly constructed as agents are connected together, just as search engines have connected and indexed all of the internet’s web pages.
Achieving this calls for further optimizations in such realms as data provision, scaling, and model efficiency. The ability to access data streams from a broad range of sources is integral to this, since this allows agents “to work with real-time information instead of static, stale datasets,” notes Deloitte. “For example, a trading agent can subscribe to live market feeds in a streaming architecture, or a compliance agent can automatically get alerts from a transaction monitoring system enabling near real-time responses to events rather than after-the-fact analysis.”
That’s why multi-agents aren’t just a better way to structure AI workloads – they’re the inevitable way in which it will be done. The evolution of agentic AI will entail the development and integration of standardized multi-agent protocols and frameworks. This will allow agents, accessed through a single interface, to do anything asked of them, operating across disparate ecosystems, networks, and ecosystems. Then, and only then, will we truly see what AI is capable of.
