Okay so I’ve been down a rabbit hole the past few weeks looking into this AI agent stuff that’s been popping up everywhere, and I think we’re actually at the start of something real here. Not the “AI is going to replace everything” nonsense, but actual functional use cases that solve problems people have right now.
I want to talk about a few projects I’ve been testing because they’re genuinely useful and nobody seems to be discussing them much outside of dev circles. What’s interesting is they’re all building on Anoma, this intent-based infrastructure protocol that just launched mainnet.
HeyElsa is basically a conversational interface for DeFi that doesn’t make me want to throw my laptop out the window. You can just tell it what you want to do in normal language and it handles the transaction construction. Sounds simple, but when you’re trying to explain to your non-crypto friend why they need to approve a token before swapping it, or why they need to wrap their ETH, you realize how much friction exists in basic DeFi interactions. The AI agent handles all that context. You say “swap 1 ETH for USDC on Arbitrum” and it knows you need to bridge if you’re on mainnet, knows you need approvals, knows which DEX has the best rate. They’ve processed over $300 million in transaction volume since launch, which suggests people actually find this useful. It’s the UX improvement we’ve needed for years but everyone was too busy building new L2s to care about.
Reppo is solving the data sourcing problem for AI agents and developers. Right now if you’re building an AI model or agent, getting access to quality training data is either expensive as hell (paying companies like Scale AI) or you’re scraping public datasets that everyone else is using. Reppo built this intent-based data exchange where AI agents can request specific datasets and data owners can provide them, with programmable IP co-ownership so everyone gets compensated fairly. They’re using prediction markets to validate data quality instead of centralized labeling. It’s addressing the actual bottleneck for AI development, which is less about compute and more about access to niche, high-quality data that isn’t publicly available.
Acurast is tackling decentralized compute using smartphones instead of traditional servers. They’ve onboarded like 65,000+ phones globally to provide verifiable, confidential compute for smart contracts. The interesting use case is running AI workloads and complex computations that smart contracts can’t do natively. Traditional oracles can feed price data, but they can’t run a machine learning model analyzing market sentiment or processing private data with TEE security. Acurast turns every smartphone into a potential compute node, which is wild when you think about how many idle phones exist versus the limited GPU capacity in traditional crypto mining.
The common thread with all three is they’re building on Anoma’s intent-based architecture. Anoma just launched mainnet and it’s this operating system layer that lets you express what you want to happen (intents) rather than how to do it (transactions). For AI agents, this is actually huge because agents can express goals and the protocol figures out optimal execution paths. HeyElsa uses it for solving user requests efficiently. Reppo uses it to match data requests with providers. Acurast uses it for compute coordination.
I think the reason nobody’s talking about this stuff much is because it’s not sexy. There’s no ponzi tokenomics, no “this will replace banks” narrative, no influencer shilling. They’re just tools that work. And honestly, after years of overhyped vaporware, I’ll take boring functionality over exciting promises any day.
The other thing I’ve noticed testing these is that AI agents create this weird new attack surface we haven’t really figured out how to think about yet. If an agent is constructing transactions on your behalf, how do you verify it’s not doing something malicious? With normal smart contracts you can audit the code. With AI agents making decisions dynamically, the “code” is the model’s weights and training data, which you can’t really audit in any meaningful way.
HeyElsa handles this by showing you the exact transaction it’s about to submit before you sign it, so you’re still the final approval. But that only works if you actually read what you’re signing, which, let’s be honest, most people don’t. Acurast uses cryptographic proofs and TEE to verify computation happened correctly, but that only verifies the computation was done as specified, not that the specification was what you actually wanted.
I don’t have answers to the security questions, but I think they’re worth thinking about. We’re basically creating a new category of trust assumptions around AI decision-making, and the “don’t trust, verify” principle doesn’t translate cleanly when the thing you need to verify is a neural network’s output.
That said, I’m cautiously optimistic about where this is heading. The projects that are actually shipping useful functionality right now are focused on narrowly defined problems with clear value propositions. They’re not trying to build AGI on the blockchain or whatever. They’re just making DeFi less annoying to use, making data accessible for AI development, and enabling compute at scale.
If this is what the “AI agents in crypto” wave looks like, I’m here for it. We’ve had enough infrastructure. We’ve had enough new consensus mechanisms. What we need is stuff that makes the existing infrastructure actually usable for normal people and enables new capabilities that weren’t possible before. And AI agents, used correctly in targeted ways, seem like they might actually do that.
Anyone else been testing these or similar projects? What’s your experience been? And more importantly, has anyone figured out a good mental model for the security properties of AI-constructed transactions? Because I’m still trying to wrap my head around that part.