Every few decades, a new technology emerges that changes everything: the personal computer in the ’80s, the Internet in the ’90s, the smartphone in the 2000s. And as AI agents ride a wave of excitement toward 2025 , and the tech world isn’t wondering if AI agents will similarly reshape our lives, it’s wondering when.
But despite all the enthusiasm, the promise of decentralized agents remains unfulfilled. Most so-called agents today are little more than chatbots or glorified co-pilots, incapable of true autonomy and handling complex tasks; not the autopilots that true AI agents should be. So 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 is there today. If you’ve been browsing X/Twitter, you’ve probably seen a lot of rumors about bots like Truth Terminal and Freysa. They are intelligent and very attractive thought experiments, but they are not decentralized agents. Not even close. What they really are are semi-scripted robots shrouded in mystique, incapable of making decisions and executing tasks autonomously. As a result, they cannot learn, adapt, or execute dynamically, at scale or otherwise.
Even the most serious players in the AI and blockchain space have struggled to deliver on the promise of truly decentralized agents. Because traditional blockchains have no “natural” way of processing AI, many projects end up taking shortcuts. Some focus strictly on verification, ensuring that AI results are credible, but do not provide any meaningful utility once those results are incorporated into the chain.
Others emphasize execution but skip the critical step of decentralizing the AI inference process. Often, these solutions work without validators or consensus mechanisms for AI results, effectively circumventing basic blockchain principles. These workarounds may create eye-catching headlines with a strong narrative and a sleek Minimum Viable Product (MVP), but they ultimately lack the substance necessary for real-world utility.
These challenges to 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, as the blockchain infrastructure, which is meant to operate silently in the background, is dragged into the front-end in the form of clunky user interfaces and manual cross-chain coordination requests. 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 and blockchain systems are built to interact.
What AI agents need to be successful
For decentralized agents to become a reality, the infrastructure that supports them needs a complete overhaul. The first, and most fundamental, challenge is to allow 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 transaction purpose and performance limitations. Closing this gap requires custom infrastructure, something I’ll 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-made transactions, but agents operate at machine speed. Process thousands (or millions) of interactions in real time? No chance. Therefore, a reimagined infrastructure must offer programmability for complex multi-chain tasks and scalability to process millions of agent interactions without throttling the network.
Then there is programmability. Current blockchains rely on rigid if-this-then-that smart contracts, which are great for simple tasks but inadequate for the complex, multi-step workflows that AI agents require. Consider a broker managing a DeFi trading strategy. You can’t just execute a buy or sell order: you need to analyze data, validate your model, execute trades across chains, and make adjustments based on real-time conditions. This goes far beyond the capabilities of traditional blockchain programming.
Finally, there is reliability. AI agents will eventually be tasked with performing high-risk operations, and mistakes will be at best inconvenient and at worst devastating. Current systems are prone to errors, especially when integrating results from large language models (LLM). One wrong prediction and a broker could wreak havoc, either by depleting a DeFi fund 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 needs to be combined into a robust development platform with durable primitives and on-chain infrastructure, so developers can create new products and experiences more efficiently and cost-effectively. Without this, AI will remain stagnant in 2024, relegated to co-pilots and toys that barely scratch the surface of what is possible.
A comprehensive 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 adopt a customized and comprehensive approach, where each layer of the infrastructure (from consensus mechanisms to development tools) is optimized for the specific demands of autonomous agents. .
In addition to being able to orchestrate multi-step workflows in real time, AI pipelines must first include a test system capable of handling a wide range of machine learning models, from simple algorithms to advanced AI. This level of fluidity demands an omnichain infrastructure that prioritizes speed, composability, and scalability to allow agents to navigate and operate within a fragmented blockchain ecosystem without any specialized adaptation.
AI-first chains must also address the unique risks posed by integrating LLM and other AI systems. To mitigate this, AI-first chains must build in 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 faulty or malicious data.
From storytelling to building solutions
2024 saw a big buzz around AI agents, and 2025 is when the Web3 industry will really steal the show. This all starts with a radical reinvention of traditional blockchains, where every layer (from on-chain execution to 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 changing the way we think about work and play.
It is increasingly clear that companies that prioritize genuine and powerful AI-blockchain integrations will dominate the scene, providing valuable services that would be impossible to implement on a traditional chain or Web2 platform. In this competitive context, the shift from human-centered systems to agent-centered systems is not optional; It’s inevitable.
