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 now AI agents are heading for a wave of excitement in 2025, and the tech world isn’t wondering whether AI agents will reshape our lives in the same way, it’s wondering how quickly.
But despite all the excitement, the promise of decentralized agents remains unfulfilled. Most of today’s so-called agents are little more than glorified chatbots or copilots, incapable of true autonomy and complex task handling – not the autopilots that true AI agents should be. What is holding back this revolution, and how do we move from theory to reality?
The current reality: true decentralized agents do not yet exist
Let’s start with what’s available today. If you’ve scrolled through X/Twitter, you’ve probably seen a lot of buzz surrounding bots like Truth Terminal and Freysa. They are clever, very compelling thought experiments, but they are not decentralized agents. Not even close. What they really are are semi-scripted bots shrouded in mystique, incapable of autonomous decision-making and task execution. As a result, they cannot learn, adapt, or execute dynamically, at scale or otherwise.
Even more serious AI blockchain players have struggled to deliver on the promise of truly decentralized agents. Because traditional blockchains have no ‘natural’ way to handle AI, many projects end up taking a shortcut. Some focus solely on verification, which makes the output of AI credible but provides no meaningful utility once that output is brought into the chain.
Others emphasize execution, but skip the crucial step of decentralizing the AI inference process itself. Often these solutions operate without validators or consensus mechanisms for AI outputs, effectively bypassing the core principles of blockchain. These stopgap solutions may produce flashy headlines with a strong story and a sleek Minimum Viable Product (MVP), but ultimately they lack the substance needed for practicality.
These challenges in integrating AI with blockchain boil down to the fact that today’s internet was designed with human users in mind, not AI. This is especially true when it comes to Web3, as the blockchain infrastructure, which is meant to work silently in the background, is instead dragged to the front end in the form of clunky user interfaces and manual coordination requests across the chain. AI agents do not adapt well to these chaotic data structures and UI patterns, and what the industry needs is a radical rethinking of the way AI and blockchain systems are built to communicate with each other.
What AI agents need to succeed
For decentralized agents to become a reality, the infrastructure that underpins them must undergo a complete overhaul. The first and most fundamental challenge is to enable blockchain and AI to ‘talk’ to each other seamlessly. AI generates probabilistic results and relies on real-time processing, while blockchains require deterministic results and are limited by the finality of transactions and throughput limitations. Bridging this gap requires a customized infrastructure, which I will discuss further in the next section.
The next step is scalability. Most traditional blockchains are prohibitively slow. Sure, they work fine for human-driven transactions, but agents operate at machine speed. Process thousands (or 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 throttling the network.
Then there is programmability. Today’s blockchains are based on rigid, if-this-then-that smart contracts, which are great for simple tasks but ill-suited for the complex, multi-step workflows that AI agents need. Think of an agent managing a DeFi trading strategy. It cannot simply execute a buy or sell order; it must analyze data, validate its model, transact across chains, and adapt 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 inconvenient at best and devastating at worst. Current systems are prone to errors, especially when integrating the output of large language models (LLMs). One wrong prediction and an agent can wreak havoc, whether that means draining a DeFi pool or executing a flawed financial strategy. To prevent this, the infrastructure must include automated guardrails, real-time validation, and error correction baked into the system itself.
All of this should be combined into a robust developer platform with sustainable primitives and on-chain infrastructure, so that developers can build new products and experiences more efficiently and cost-effectively. Without this, AI will remain stuck in 2024 – relegated to co-pilots and toys that barely scratch the surface of what is possible.
A full-stack approach for a complex challenge
What does this agent-centric infrastructure look like? Given the technical complexity of integrating AI with blockchain, the best solution is to take a customized, full-stack approach, where every layer of the infrastructure – from consensus mechanisms to developer tools – is optimized for the specific requirements of autonomous agents .
In addition to the ability to orchestrate real-time, multi-step workflows, AI-first chains must include a proof system capable of processing a wide range of machine learning models, from simple algorithms to advanced AIs. This level of fluidity requires an omnichain infrastructure that prioritizes speed, composability, and scalability so that agents can navigate and operate within a fragmented blockchain ecosystem without any specialized customizations.
AI-first chains must also address the unique risks associated with integrating LLMs and other AI systems. To mitigate this, AI-first chains must build safeguards at every layer, from validating inferences to ensuring alignment with user-defined goals. Priority capabilities include real-time error detection, decision validation, and mechanisms to prevent agents from acting on erroneous or malicious data.
From storytelling to solution building
There was a lot of early hype around AI agents in 2024, and in 2025 the Web3 industry will actually start to earn it. This all starts with a radical reinterpretation of traditional blockchains, where every layer – from on-chain execution to the application layer – is designed with AI agents in mind. Only then will AI agents be able to evolve from entertaining bots to indispensable operators and collaborators, redefining entire industries and upending the way we think about work and play.
It’s becoming increasingly clear that companies that prioritize real, powerful AI blockchain integrations will dominate the scene and provide valuable services that would be impossible to deploy on a traditional chain or Web2 platform. Within this competitive context, the shift from human-centered systems to agent-centered systems is not optional; it’s inevitable.