Every few decades, a new technology emerges that changes everything: the personal computer in the 1980s, the Internet in the 1990s, the smartphone in the 2000s. And as AI agents ride a wave of enthusiasm until 2025, and the tech world isn’t wondering if AI agents will reshape our lives in the same way, it’s wondering how soon.
But despite all this enthusiasm, the promise of decentralized agents has still not been kept. Today, most so-called agents are little more than chatbots or glorified co-pilots, incapable of true autonomy and managing complex tasks – not the autopilots that real AI agents should be. So, what is holding back this revolution and how can we move from theory to reality?
The current reality: true decentralized agents do not yet exist
Let’s start with what exists today. If you’ve browsed X/Twitter, you’ve probably seen a lot of buzz around bots like Truth Terminal and Freysa. They are intelligent and highly engaging thought experiments, but they are not decentralized agents. Not even close. What they actually are are semi-scripted robots shrouded in mystique, incapable of making decisions and carrying out tasks autonomously. As a result, they cannot learn, adapt, or execute dynamically, at scale or otherwise.
Even more serious players in the AI-blockchain space have struggled to deliver on the promise of truly decentralized agents. Because traditional blockchains don’t have a “natural” way to deal with AI, many projects end up taking shortcuts. Some focus narrowly on verification, ensuring AI results are credible, but fail to provide meaningful utility once those results are put on-chain.
Others emphasize execution but skip the critical step of decentralizing the AI inference process itself. Often, these solutions operate without validators or consensus mechanisms for AI results, thereby bypassing the fundamental principles of blockchain. These stopgap solutions may create headlines with a strong narrative and a sleek minimum viable product (MVP), but they ultimately lack the substance needed for real-world utility.
These challenges of integrating AI with blockchain boil down to the fact that today’s internet is designed with human users in mind, not AI. This is especially true when it comes to Web3, since blockchain infrastructure, meant to operate silently in the background, is instead moved to the front end in the form of clunky user interfaces and manual requests for coordination between chains. AI agents do not adapt well to these chaotic data structures and user interface patterns, and what the industry needs is a radical rethinking of how AI systems and blockchains are designed to interact.
What AI Agents Need to Succeed
For decentralized agents to become a reality, the infrastructure behind them needs to be completely overhauled. The first and most fundamental challenge is to enable blockchain and AI to “communicate” seamlessly. AI generates probabilistic results and relies on real-time processing, while blockchains require deterministic results and are limited by transaction finality and throughput limitations. Bridging this gap requires tailor-made infrastructure, which I will discuss in more detail in the next section.
The next step is scalability. Most traditional blockchains are prohibitively slow. Sure, they work well for human transactions, but agents operate at machine speed. Process thousands, even millions, of interactions in real time? No chance. Therefore, a redesigned infrastructure must provide programmability for complex multi-chain tasks and scalability to handle millions of agent interactions without limiting the network.
Then there’s programmability. Today’s blockchains rely on rigid smart contracts, ideal for simple tasks, but inadequate for the complex, multi-step workflows that AI agents require. Think of an agent managing a DeFi trading strategy. It can’t just execute a buy or sell order: it must analyze data, validate its model, execute trades across multiple chains, and adjust based on real-time conditions. This goes far beyond the capabilities of traditional blockchain programming.
Finally, there is reliability. AI agents will ultimately be tasked with high-stakes operations, and mistakes will be embarrassing at best and devastating at worst. Current systems are prone to errors, especially when integrating results from large language models (LLMs). One wrong prediction and one agent could wreak havoc, whether it’s emptying a DeFi pool or executing a flawed financial strategy. To avoid this, the infrastructure must include automated guardrails, real-time validation, and error correction built into the system itself.
All of this must be combined into a robust development platform with sustainable primitives and on-chain infrastructure, so that developers can create new products and experiences more efficiently and cost-effectively. Without it, AI will remain stuck in 2024 – relegated to co-pilots and toys that barely scratch the surface of what’s possible.
A full-stack approach to a complex challenge
So what does this agent-centric infrastructure look like? Given the technical complexity of integrating AI with blockchain, the best solution is to take a personalized and comprehensive approach, in which each layer of the infrastructure – from consensus mechanisms to development tools – is optimized for the specific requests of autonomous agents.
In addition to being able to orchestrate real-time, multi-step workflows, AI-driven chains must include a proof system capable of handling a diverse range of machine learning models, from simple algorithms to advanced AI. This level of fluidity requires an omnichain infrastructure that prioritizes speed, composability, and scalability to allow agents to navigate and operate within a fragmented blockchain ecosystem without any specialized adaptations.
AI-driven channels must also address the unique risks posed by integrating LLMs and other AI systems. To mitigate this problem, AI-driven chains must build in protections at every level, from validating inferences to ensuring alignment with user-defined goals. Priority features include real-time error detection, decision validation, and mechanisms to prevent agents from acting on erroneous or malicious data.
From storytelling to solution creation
The year 2024 was marked by a lot of hype around AI agents, and it is in 2025 that the Web3 industry will really benefit from it. It all starts with a radical reinvention of traditional blockchains where every layer – from on-chain execution to the application layer – is designed with AI agents in mind. Only then can AI agents evolve from entertaining robots to indispensable operators and collaborators, redefining entire industries and disrupting the way we think about work and play.
It is increasingly clear that companies that prioritize true, powerful AI-blockchain integrations will dominate the scene, providing valuable services that would be impossible to deploy on a traditional chain or Web2 platform. In this competitive context, the shift from human-centered to agent-centered systems is not optional; it is inevitable.
