When AI Agents Moved From Talking to Doing

Honestly, watching AI agents evolve in the crypto world has been a bit like watching a side project accidentally turn into a real product. The early attempts were pretty simple. Most of them were just automated chat layers built into places like X, simple sentiment scrapers or shallow on-chain scanners that made everything sound smarter than it actually was. It worked well enough to call it the first wave, and it gave teams something to build on, but the real action was happening off-chain, where all the inference and decision-making actually lived. Still, those first tools shaped how people imagined what an AI agent could be, and that mattered more than the features themselves.

As the landscape matured, the second wave began to feel different. Teams started pushing beyond friendly chatbots and moved into areas that actually interact with money. Frameworks like ELIZA, ARC, Virtuals and Coinbase’s AgentKit showed that you could build an agent with a personality but also plug it into on-chain systems. Most of the heavy lifting stayed off-chain, of course, but the idea of connecting AI reasoning to blockchain execution became a lot more legitimate. Some repos exploded in popularity before cooling off, tokens surged and crashed, and developers treated these frameworks like playgrounds for whatever they wanted to automate next.

Then DeFAI entered the picture. This is where things got more serious. Projects like HeyAnon, Wayfinder, Giza’s ARMA agent and Almanak started treating AI as something that could actually move assets, rebalance positions or suggest complex DeFi paths without making the user learn the mechanics underneath. The irony is that none of this worksunless the models remain off-chain, since blockchains are too rigid and deterministic to run AI directly. So everything important happens out of sight and only the final signal touches the chain. But even with safeguards, the idea that an LLM has to interpret smart contracts before anything executes introduces its own brand of risk, especially if something gets misread or manipulated.

ARMA is a good example. It tries to optimize multi-protocol yields by constantly reallocating and auto-compounding based on real-time rates, which sounds great but also depends entirely on trustworthy data inputs. Almanak takes it even further by modeling strategies through a blockchain state machine and using LLMs to turn brainstorming, simulation and deployment into a single workflow. And then there’s Story, which went in a completely different direction by trying to make on-chain IP programmable and licenseable with agent-driven negotiation logic. It feels like a reminder that AI agents don’t always need to manage money to matter.

The bigger trend underneath these projects is hard to miss. The first wave focused on personality, community and clever conversation loops. They were fun, and they proved that AI could plug into crypto culture pretty naturally, but they didn’t move real economic weight. The second wave is all about closing that gap. These agents are now stepping directly into transactional territory, bridging chains, executing strategies and handling tasks that previously required specialized knowledge. It’s progress, but it also opens a new set of concerns. Off-chain processing introduces new attack surfaces. Smart contract misunderstandings can trigger unintended outcomes. Data feeds can be corrupted. And private-key management becomes even more sensitive when an automated agent can act faster than a human ever could.

Still, the trajectory feels inevitable. If DeFAI delivers enough convenience, people will use it even if they worry about the risks, just like they did with early trading bots. A portion of users will eventually get burned by bad security or a malicious exploit, but the broader movement will continue forward because the benefits are simply too appealing. The whole space is slowly shifting from AI as a novelty to AI as real infrastructure. And if developers manage to build strong fail-safes and test every edge case before pushing things live, we might end up with agents that make DeFi feel less like a puzzle and more like a normal part of everyday digital finance.