AI agents seem to be everywhere, judging by the amount of mindshare they soak up. Yet despite all the talk about agentic bots that permeate crypto Twitter and Telegram, talk doesn’t automatically correlate with usage. Despite interest in AI agents being sky-high, you may be surprised to learn just how few traders have actually used them.
And while all the excitement around agents is understandable, with their promise to automate and enhance every element of onchain trading, much of the chat is inaccurate – because everyone’s talking about something they’ve never experienced. As a result, awareness of agents has never been higher yet knowledge of them remains depressingly low. Let’s see if we can fix that and dispel a few myths about AI agents in the process.
What’s in an agent?
Autonomous AI agents are software entities capable of executing tasks with minimal human oversight. At a practical level, these autonomous programs can assess risks, analyze onchain data, and optimize investment strategies. Think of them as self-driving cars navigating the blockchain networks where DeFi is done.
While the vehicles themselves – our AI agents – are impressive, it’s the underlying frameworks that serve as the engine and chassis, ensuring efficient journeys across the digital terrain. Frameworks like Virtuals.io, ai16zdao, and Arc provide developers with specialized tools, libraries, and pre-built components so they can focus on logic rather than reinventing the wheel.
These frameworks can be tailored to specific chain requirements or programming languages. For example, an agent might autonomously rebalance liquidity pools on one network, then switch to auditing smart contracts on another, all using the same underlying framework.
Read also: Best AI Agent Cryptos in 2025
What can an agent do?
The list of possible tasks that can be assigned to an agent grows by the day. Agents can analyze market trends, execute trades under strict parameters, optimize yield farming strategies, or even scour the onchain landscape for suspicious activity. They can manage communities, moderate forums, generate content, and create performance reports.
Because these frameworks handle the heavy lifting of code integration, they free developers to pursue innovative ways for AI agents to interact with the broader crypto ecosystem. The “right” framework depends entirely on a project’s specific needs, much like choosing the optimum engine for the vehicle being fitted out.
As for what these agents can do once deployed in the wild, here are just a few use cases:
- Assess Risks: Evaluate market conditions and mitigate financial or security threats.
- Analyze Onchain Data: Decode the vast stream of blockchain transactions and condense it into human-palatable bite-sized chunks. (This is what a lot of the agentic memecoins posting on X currently do.)
- Optimize Investment Strategies: Adjust portfolios to take advantage of emerging opportunities such as greater yield or new financial products.
All of these agents can be further enhanced through platforms like EVAL Engine for performance scores, holding AI agents accountable and fostering continuous improvement. This is an extremely useful tool for any aspiring agent operator, serving as an evaluation plugin for discerning the quality of agents. Developed by Chromia, EVAL Engine monitors an agent’s output over time, offering insights into long-term performance trends. This framework is likely to become widely adopted as adoption of agents proliferates.
And that’s about it, right? AI agent plus framework = autonomous bot that can do anything you ask of it? Not quite. There’s still one critical component we’ve yet to address. If the agent is the self-driving car making its way across blockchain highways and the developer framework is its engine, what’s fueling it?
The compute powering AI agents
The power needed to run these AI agents goes well beyond ordinary computing requirements. Training machine learning models or handling high-frequency transaction data requires enormous GPU resources because AI agent frameworks demand significant computational power to function effectively. Traditional centralized cloud providers can be both expensive and restrictive. This is where decentralized compute steps in.
DePINs such as io.net have emerged as the fuel that keeps all these agents ticking over. By offering access to a decentralized cloud of more than 300,000 verified GPUs across 130+ countries, io.net delivers the computational backbone these frameworks need. Decentralized compute not only cuts costs compared to large centralized providers but ensures that your AI agents run in a truly unstoppable, censorship-resistant manner, in keeping with blockchain’s core principles.
Where next for agents?
AI agent frameworks are on track to become the de facto operating system for autonomous, chain-agnostic innovation. We’re rapidly approaching a future where intelligent agents can move seamlessly across networks, each playing a specialized role in a single coordinated strategy.
AI agent frameworks are evolving into comprehensive Agent Operating Systems (AgentOS), providing a universal foundation for AI-driven automation across industries. These frameworks will allow AI agents to transcend blockchain-specific constraints, enabling seamless interaction across multiple networks.
At this stage, the vast majority of DeFi users are aware of AI agents and their capabilities. But few have actually experienced them in action, short of interacting with agentic twitterbots – and in some cases it’s debatable just how much AI there is in the mix. This year, however, will mark the first time that many traders break their agentic cherry, and deploy a bot to take care of the tasks that they are either unable or unwilling to do.
Much like the migration from web wallets to Telegram trading bots, they may find that once they’ve taken that decisive step, they can never go back. Last year, agents were the future of onchain trading. This year, that future is finally here.
Source: https://coincodex.com/article/62739/what-you-should-know-about-ai-agents/