‍TL;DR: Last year’s frenzy around “autonomous AI agents” in crypto promised a Web3 revolution. Think bots handling your DeFi portfolio, running DAOs, executing real-world asset deals 24/7 with zero human input. Projects like Autonolas, Fetch.ai, AgentLayer and a swarm of others pitched AI agents as the fix for everything from clunky DeFi UX to DAO coordination failures. Millions in funding poured in and tokens like $VIRTUAL and $FET hit billion-dollar valuations at peak hype . But fast forward a few months: much of that narrative disappeared. Outside of a few demo products and frameworks, most “AI agents” remained slideware and whitepapers. Why? The infrastructure isn’t there, dev tooling is raw, user experience went from bad to weird, and no one really found product-market fit beyond speculative trading. Still, not all is lost – some use cases (pre-trade strategy bots, compliance automation, on-chain asset discovery) do have legs if we fill in critical gaps. Below we break down the rise and stall of Web3 AI agents, then sketch how real builders can cut through the vapor and actually BUIDL something useful.

AI Agent Hype

The AI agent narrative in Web3 didn’t come out of nowhere. It was sparked by a perfect storm of tech trends and crypto imagination. The late-2022 boom of generative AI (hello ChatGPT) made everyone and their dog wonder: what if we blend this with blockchain? Around the same time, a handful of projects began positioning themselves at this intersection:

> Autonolas (OLAS): A platform focused on open-source autonomous agent services, allowing devs to deploy off-chain AI agents that coordinate together and interface with blockchain . Autonolas pitched itself as the infrastructure for AI agent economies – complete with a token (OLAS) to incentivize development and an “Agent App Store” for discovering agent services.

> Fetch.ai (FET): An older project (dates back to 2018) that suddenly found renewed relevance. Fetch.ai had long been building an agent framework on its own chain, but in 2023 it doubled down – launching a new uAgents framework to make agent development easier , rolling out an “Agentverse” IDE for devs, and even integrating an AI agent (“Fetchbot”) into its wallet to automate tasks like auto-paying or auto-compounding yields . A $40M funding boost from DWF Labs in early 2023  didn’t hurt the hype.

> AgentLayer (AGENT): A newer entrant billing itself as “the first decentralized AI agent public chain.” Essentially, AgentLayer is building a specialized Layer-2 blockchain tailored for autonomous agents – complete with its own BFT consensus and a registry for AI agents . The vision: give AI agents their own playground (and economy) on-chain, with a native token to match. They launched a testnet and demo agents (like a security “Sentinel” bot) in late 2023 .

> The Memes & Copycats: With AI fever running hot, it wasn’t just serious infrastructure projects getting attention. Meme-y projects launched tokens like $GOAT, $VIRTUAL, and $AI16Z, each wrapping an AI agent narrative in viral marketing. One coin ($ai16z) even claimed to be an “AI hedge fund” run by a GPT version of Marc Andreessen  . Another ($VIRTUAL) created a virtual anime idol agent “Luna” that live-streamed and tweeted, pumping the token’s market cap to ~$75M at one point . At the height of the craze, AI agent tokens dominated crypto market cap rankings – by Jan 2025 $VIRTUAL hit a $3.8B fully-diluted value, with Fetch.ai not far behind at $3.4B . In short, AI agents captured the crypto zeitgeist.

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So why all the hype? Partly it was cultural – AI agents made for a sexy story that blended sci-fi autonomy with crypto decentralization. Memecoin culture played a role, with projects like $GOAT tapping humor and virality to make AI relatable . But there were also real technological threads fueling excitement. The success of ChatGPT showed AIs could handle complex instructions, so people imagined these AIs directly driving on-chain activity . Big investors jumped in, validating the space (e.g. a16z’s high-profile AI bets ). Even Nvidia’s CEO declared a “$1 trillion AI agent economy” is on the horizon , adding legit FOMO.

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In crypto circles, influential voices suggested AI agents would be the new interface for Web3 – finally abstracting away today’s painful UX. As one commentator put it, “AI agents are emerging as the new interface layer, doing for Web3 what Google did for the early internet”, i.e. simplifying a maze of wallets and smart contracts into something anyone could use . These weren’t envisioned as simple chatbots, but autonomous programs executing complex DeFi strategies, managing portfolios, even voting in DAO governance on your behalf . In other words, AI agents were hyped as the remedy to all of Web3’s adoption woes in one stroke.

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Agents to Fix DeFi, DAOs, and Everything Else

> DeFi is powerful but notoriously user-unfriendly – juggling wallets, LP positions, yields, and bridges can make a degenerate cry. AI agents were pitched as the ultimate fix for this brutal UX  . Instead of manually clicking through 10 dApps for a yield strategy, you’d simply tell an AI agent your goal (“maximize my ETH yield with low risk”) and it would automatically compose and execute all the necessary transactions across protocols . No more understanding AMMs or gas fees – the agent handles staking, harvesting, rebalancing, etc., like a “crypto butler.” “No need to even know how DeFi works,” as one article gushed . Some even quipped that blockchains were made for bots, not for humans , and AI agents would finally let humans step back from the complexity. Projects like Aperture Finance showcased AI-driven portfolio management (user sets goal, AI optimizes returns), and Autonolas built a demo agent “BabyDegen” to trade on your behalf 24/7 . The DefAI narrative promised set-and-forget investing: “AI agents act as financial assistants, handling all the complexity in the background… making decisions, rebalancing portfolios, optimizing returns – all without the user lifting a finger.”  

‍AI agents were pitched as the ultimate Web3 multi-tool – “better, faster, more autonomous” than any human or static code at tasks across DeFi, DAO, RWA, and beyond . Every crypto problem, it seemed, had an AI agent-shaped solution if you believed the whitepapers. By mid-2024, a slide from any project looked incomplete without an “AI agents” angle, and panels at conferences breathlessly debated the coming age of “agentic DeFi” or “AI DAOs.”

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What Got Built (and what didn’t)

So, with all that hype, did anyone actually ship working AI agents in Web3? The answer: a few teams delivered early products and frameworks – but much stayed vaporware or experimental. Here’s the scorecard:

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> Autonolas/Olas: The Autonolas team did release a suite of tools and example agents. They open-sourced their framework for multi-agent systems (MAS) integration with blockchain, including an agent communication network (ACN) and on-chain registry for services . In late 2023, they launched “Pearl,” an agent app store, and showcased BabyDegen, an autonomous DeFi trading agent . BabyDegen comes with variants like Modius and Optimus agents – basically personal portfolio managers that plug into protocols like Balancer, Aave, etc., to farm yields or execute strategies for you  . Users can run these agents locally or via the Pearl network, and the agents will manage your funds (with your permission) according to some predefined AI models/strategies. It’s cutting-edge stuff, but essentially these are glorified trading bots with AI decision logic. Reality: Autonolas delivered a solid developer infrastructure and some cool demos, but we haven’t seen DAOs handing the keys to AI agents yet. The platform is there for devs to use, but actual adoption is sparse so far – mostly tech-savvy users tinkering with BabyDegen rather than the masses delegating their 401k to it.

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> Fetch.ai: Fetch went hard on tooling. In 2023 they rolled out uAgents, a Python-based framework for building agents that can interface with Fetch’s Cosmos-based blockchain (and others) . They built Agentverse, a cloud IDE and sandbox for agent devs . And notably, they updated their Fetch Wallet to be an “AI Super Wallet” – integrating FetchBot, an AI agent that can execute simple on-chain tasks for the user . For example, FetchBot can automatically claim your staking rewards or send a payment when triggered, and it has a ChatGPT plugin to help users with wallet questions. Fetch also demoed agents for things like decentralized Uber (agents representing riders and drivers negotiating rides). Reality: Fetch provided lots of infrastructure and examples, but again, usage seems limited. The wallet’s AI features are niche, and developers still face a learning curve to make meaningful agents. No killer app emerged, despite the fancy framework. To Fetch’s credit, they have active developer engagement (their repo of sample agents, hackathons, etc.), but the ecosystem feels in “build mode” rather than widespread deployment.

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> AgentLayer and other L1s: AgentLayer launched its testnet and showed off an example agent (“Sentinel” security bot) . But as a whole, these AI-focused chains are in very early stages. Another example is HyperCycle, a proposed high-speed chain for AI agents in partnership with SingularityNET  – still under development. There’s also Aioz Network and others claiming “AI-ready blockchains.” So far, these efforts haven’t gone beyond test environments and tokens. It’s one thing to declare “we built a blockchain for AI agents!”, it’s another to have actual agents living there doing something useful. Reality: New blockchains need a strong reason to exist; at present, it’s unclear why an agent would need its own chain vs. using existing ones with off-chain compute. These projects are mostly promises (with perhaps fancy consensus algorithms) and their success depends on attracting both AI developers and users – a tall order when Ethereum and friends already exist.

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> Quite a few DeFi protocols flirted with AI agent features. Some launched simple “AI advisors” in their UI (e.g. a chatbot that answers questions about the dApp). Others, like Aperture and Kaito, built AI-driven analytics and strategy recommendation tools . But in terms of execution agents integrated into protocols, we haven’t seen much beyond prototypes. One notable attempt: Yearn Finance experimented with an AI that suggests vault strategies to users based on risk preference (though it doesn’t execute trades autonomously). There was also talk of Compound exploring AI risk management agents, but nothing concrete deployed on mainnet. Reality: DeFi protocols realize handing full control to an AI is risky; instead they leverage AI for decision support rather than autonomous execution. The closest thing to “agents running wild” in DeFi are the existing arbitrage and liquidation bots – which aren’t new, they just got a rebrand as “AI agents” if they use a bit of machine learning. 😅

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> ...(and) memecoins . Those meme projects ($GOAT, $VIRTUAL, $AI16Z, etc.) mostly delivered entertainment, not tech breakthroughs. $VIRTUAL’s Luna agent did tweet and interact as a sort of AI persona, which is cool from a community engagement perspective (like an AI VTuber with a token) . $AI16Z’s “AI hedge fund” essentially let people bet on an AI-managed portfolio, but it was more narrative than proven strategy. Many of these saw pumps and then dumps; a few are still around iterating on the concept (the Eliza framework behind AI16Z got a lot of GitHub traction  , indicating dev interest in social agents). Reality: Fun experiments, but little to no impact on real Web3 problems. However, they did succeed in getting people excited, which arguably pushed the narrative forward (for better or worse).

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Overall, the gap between vision and reality was stark. While a handful of functioning AI agent products emerged (mostly around trading and simple automation), many promises stayed on slides. For example, we heard about AI agents managing DAO treasuries, but no major DAO actually installed an AI “treasurer”. We saw whitepapers about multi-agent insurance and cross-chain AI bridges, but those remain theoretical or at best closed tests. Even in DeFi UX – the area with the most tangible demos – the actual userbase of AI “DeFi butlers” is minuscule. Instead of one agent to rule them all, we got a dozen siloed bots each doing a little thing.

In fact, users who tried the early AI agents often ended up juggling more complexity: one agent for swaps, another for yield, another for bridging, each on different platforms. The experience became fragmented . As one analysis noted, “each agent solves a particular execution, but they don’t work together… leaving users stuck with multiple platforms and interoperability woes.”  So much for simplifying UX – it ironically mirrored the very problem it set out to fix!

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From Hype to Hangover

By early 2025, the AI agent buzz in Web3 had noticeably cooled. The conversation moved on to other shiny things (real-world assets, LSDs, friend.tech, whatever’s next), and AI agents stopped dominating headlines. What caused the momentum to fade? A few reality checks:

1. Lack of Mature Infrastructure

‍Under the hood, a true autonomous agent network needs a lot of plumbing that Web3 doesn’t yet have. You can’t just spin up a MetaGPT and let it loose on Ethereum mainnet – the environment is too constrained. Secure, reliable agent execution environments are missing. There’s no widespread solution for an AI agent to safely hold private keys or custody assets on behalf of users in a decentralized way. (Today it’s usually “run this agent on your own machine and give it your wallet keys” – not exactly plug-and-play.) Also, connectivity infrastructure is immature: agents need to fetch off-chain data, trigger on-chain txs, and possibly talk to other agents. Apart from using centralized APIs or custom relayers, there isn’t a standardized “agent railway.” In short, the rails for agents aren’t as developed as the rails for regular dApps. Even something as simple as scheduling an agent to wake up when gas is cheap or an oracle update comes in is non-trivial without building a whole backend. This lack of infra meant many teams ended up hand-waving the “autonomy” part (their agents were basically cloud services in disguise), undermining the decentralization story.

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2. Unclear Developer Tooling & Standards‍

If a developer wanted to build an AI agent for Web3 today, where would they start? There’s no equivalent of, say, Truffle/Hardhat but for agent development. Each project introduced its own framework (Autonolas SDK, Fetch uAgents, etc.), none of which achieved critical mass. The tooling remains fragmented and immature, making the learning curve steep. Want your agent to work across Ethereum, Solana, and Cosmos? Good luck – you’ll be stitching together libraries and RPC calls manually. There’s also a lack of standards: how should agents authenticate on-chain? How to format agent-to-agent communication? Without common protocols, agents from different ecosystems can’t easily cooperate (one reason users ended up with multiple single-purpose agents ). The developer experience thus far has been rough, slowing down the creation of compelling applications. Until there are robust, easy SDKs and perhaps interoperability standards for agents, it’s hard for this space to grow like the early web or mobile app ecosystems did.

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3. UX and Trust Mismatch

‍Handing over control to an AI agent sounds good in theory (“set it and forget it!”), but in practice users balked at the trust leap. Even crypto veterans – used to smart contracts – were uneasy about a black-box AI making moves with their money. Did the agent test all edge cases? Could it get exploited or stuck in a loop? There’s a reason automation in finance is gradual: people want guarantees. Most AI agents could not offer guarantees of success (remember, these are probabilistic programs, often with <50% success on complex tasks in testing ). Early adopters reported agents making dumb mistakes or needing frequent supervision. As one AI dev put it bluntly, “after seeing many attempts at AI agents, I believe it’s too early, too expensive, too slow, too unreliable” to trust them fully . On top of that, the UX of using these agents was often worse than using the dApps directly! Many agent UIs were clunky or required technical setup (run a Docker container, etc.). Instead of simplifying, it added another layer one had to learn. This UX disappointment meant few users stuck with agent-based tools after trying them. It turns out people don’t want to chat with an AI to do a Uniswap trade that they could do in 3 clicks on a familiar interface. The AI might save time on multi-step complex tasks, but for many daily crypto actions it was overkill or introduced uncertainty.

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4. No Clear Product–Market Fit

‍Perhaps the biggest reason the hype died down: no breakout use-case emerged that proved AI agents are a must-have in Web3. We got neat demos, but none demonstrated a 10x improvement for a target audience. Newbie retail users – the supposed beneficiaries of simplified UX – weren’t onboarded en masse (most still find the concept of “running an agent” too abstract or risky). Power users and traders didn’t trust an AI to do better than their own scripts or preferred protocols. DAO members saw AI proposals or tooling as novelty, not a solution to governance participation. Essentially, the AI agent narrative failed to find a solid beachhead market. It was a solution looking for a problem, and while there are problems (UX, coordination, etc.), the agent approach wasn’t the slam dunk to solve them given the hurdles above. The result: many projects found themselves with fancy tech that didn’t attract sustained users. And without real usage or revenue, some teams pivoted away or went quiet. By Q1 2025, the sector had a bit of a ghost-town feel – a lot of infrastructure built, tokens floating around, but not a lot of live agent-driven activity fulfilling the grand promises. The hype cycle had clearly peaked and dipped, as attention moved on and only the builders truly convinced of the long-term stuck around.

To be clear, this doesn’t mean “AI agents in Web3 are dead”. It’s more like the initial bubble popped, and now the space is in a sober reassessment phase. Much like DeFi Summer or NFT mania, a period of excessive exuberance is typically followed by a grind of building real value. We’re in that grind now.

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Still worth building?

Not all those big ideas were bogus. There are a few use cases where AI agents could genuinely shine in Web3 – if the hurdles are overcome. These are less about sci-fi autonomy for its own sake, and more about addressing specific needs where automation + AI can add value:

> Pre-Trade Analysis and Strategy Agents

‍Before a crypto trade or investment, there’s a ton of research and decision-making. An AI agent that specializes in this could be super helpful. Think of a “personal trading strategist”: it ingests on-chain data, technical indicators, maybe social sentiment; it chats with you about your goals (“I want to accumulate 100 SOL at good prices over 2 months”), and it comes up with a plan – or even directly sets limit orders, executes DCA (dollar-cost averaging), etc. Unlike a static trading bot, an AI-powered one could adjust strategy on the fly as conditions change and explain its rationale in plain language. This could benefit both experienced traders (who use it as a copilot to save time scanning markets) and newbies (who get guidance and automation in one). Some pieces are already there – e.g. ChatGPT can analyze token price charts or news if prompted, and plenty of trading bots exist. The gap is integrating it all and plugging into on-chain execution seamlessly. What’s blocking? Access to reliable real-time data (agents need oracles or direct chain access for prices, mempool, etc.), and trust/verification (will it execute at reasonable prices and not YOLO your funds?). Also, such an agent needs guardrails to avoid being gamed by market manipulation. We’ll need robust backtesting and perhaps sandbox modes to build trust. But the value is clear: there’s a reason hedge funds spend millions on automation and AI – if packaged right, on-chain traders would love those powers too.

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> Compliance & Monitoring Automation

‍This is less sexy but quite practical. There’s growing pressure for DeFi and DAOs to adhere to various compliance standards (be it financial regulations, security policies, or community-defined rules). AI agents could act as tireless compliance officers / risk sentinels. For example, an agent could monitor all transactions in a protocol for signs of hacks or fraud (using pattern recognition on-chain and cross-referencing known exploit signatures). Or a DAO could deploy an agent to ensure no proposal violates certain bylaws – flagging ones that do. In the RWA context, an agent could handle ongoing due diligence: monitoring that a real-world borrower’s collateral remains sufficient by pulling external data. A specialized AI can sift through vast data (blockchain records, news feeds, user messages) to detect anomalies or non-compliance faster than any human. A real-world analogy: banks use AI for AML (anti–money laundering) alerts; crypto could have decentralized versions of that. What’s blocking? High-quality data feeds and clearly defined rules. AI is only as good as the data it gets – so an agent might need APIs to compliance databases, identity verifiers, etc. There’s also the challenge of acting on findings: should the agent freeze funds? Probably not without human oversight (at least not until it’s proven). So we need a framework where the agent can suggest or even initiate enforcement, but with humans in the loop for final authority. Technically, this intersects with on-chain governance (e.g. agent posts a governance alert or auto-submits a vote to pause a contract if critical). These are complex to implement, but given how much money rides on code, automating security and compliance could be a killer use-case – if done carefully.

> On-Chain Asset Discovery & Management

‍The crypto world moves fast – new tokens, pools, NFTs launching daily. Keeping track (and taking advantage) is like drinking from a firehose. AI agents could excel at scouting opportunities and even acting on them. Imagine an agent that constantly scans for things like: yield farms with unusual APYs (and checks if it’s a glitch or real), new governance proposals in DAOs you care about, NFT collections trending on-chain that match your taste, or arbitrage between DEXs. Instead of you manually trawling dashboards and Twitter, the agent surfaces what matters to you. It could even auto-invest small amounts in curated opportunities (with your pre-approval guidelines). For DAOs or funds, an agent could handle “asset discovery” by doing due diligence on new projects – reading whitepapers, summarizing them, checking code on GitHub for red flags – basically an AI analyst. Some products like Kaito started along these lines for research , but there’s room to go deeper with execution. What’s blocking? Noise vs signal is a big issue – the agent needs good filtering to not spam you with junk. It also needs defined mandates (what’s a good opportunity for one person is a disaster for another’s risk profile). Another blocker: APIs for all these sources (many sites have no APIs or are behind captchas etc., though on-chain data is accessible if you run an indexer or use something like The Graph). This use case might find traction because it doesn’t necessarily require full autonomy – an agent could just alert a human who then acts, making it easier to trust initially. Over time, as it proves itself (“dang, this bot finds gems!”), you might let it execute small trades on its own.

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Beyond these, we could mention things like gaming agents (NPCs that own/trade NFTs or game assets), AI agents for personalized education in Web3, or supply chain agents for tracking provenance. But those are further afield. The three above feel most immediate in terms of need and viability in the next couple of years.

The common theme: focused agents that tackle a well-scoped problem (trade strategy, compliance monitoring, discovery) have a better chance than grand “do everything” agents. Each of these could start with a human-in-the-loop approach (AI assistant rather than full agent) and progressively automate more as confidence grows. In other words, the path forward is likely augmenting users first, automating fully second – a lesson the initial hype wave missed while chasing full autonomy from day one.

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Where can builders plug in?

For a builder-focused team like Buidly (shameless plug 😄), the fizzled hype actually reveals clear gaps in the stack where real innovation is needed. Instead of trying to launch yet another dubious “AI coin,” the opportunity is to solve the hard problems holding agents back. Here’s where rolling up our sleeves could make a difference:

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1. Developer SDKs & Frameworks

‍We desperately need an easy on-ramp for devs to create AI-agent-powered apps. Today it’s too fragmented – one team codes an agent from scratch in Python, another in Node, each reinventing wheels for connecting to wallets, parsing smart contract ABIs, etc. A unified AI Agent SDK (akin to Truffle for smart contracts or Django for web apps) could lower the barrier. This SDK would handle the plumbing: connecting to various blockchains, a plugin system for different AI models (so devs can swap in OpenAI API or local models), state management for the agent, and secure key management modules. It should also provide simulator tools to test agents safely. Buidly could help build such a framework or contribute to emerging ones (maybe Fetch’s uAgents or AutoTX by Polywrap) to make them truly cross-chain and developer-friendly. Essentially, empower devs to focus on the agent’s logic, not the boilerplate of hooking it into Web3.

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2. On-Chain Executors & Safe Guards

‍One tricky part is how agents actually execute on-chain actions. Right now many agents just tell a centralized server or a user’s wallet to do something. We need more decentralized and secure executors. Think of something like Gelato or Chainlink Automation, but tailored for AI agent triggers. Buidly could create an on-chain (or hybrid) service where agents can submit transactions to be executed trustlessly when conditions meet. This might involve a network of relayers that hold bonded stakes (to ensure they execute correctly) or using account abstraction so agents can have sponsored gas fees. Moreover, building guardrails is key: for example, an agent’s contract could have limits (can’t spend more than X ETH or can only interact with whitelisted protocols) to mitigate damage from AI errors. Providing a standardized smart contract wallet or proxy that an agent “controls” with configurable permissions would let users safely grant autonomy. This is a ripe area for development – basically, the agent equivalent of a Gnosis Safe with policy control. Buidly could develop these safe execution wrappers and offer them as middleware to any agent project.

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3. Agent-Oriented Infrastructure (AOI)

‍If AI agents are to be more than a gimmick, they need their own version of Web3 infrastructure: things like agent identity, discovery, and reputation systems. For instance, how does one agent trust data or services from another? We might need an on-chain registry of agents (like a DNS for agents) with reputational scoring (perhaps using token staking or community ratings). Buidly could work on these “agent middleware” layers – e.g., an Agent Identity NFT that an agent uses to sign its actions, which accumulates a history (audit trail) that others can verify. Another piece is data networks: agents need lots of off-chain info. Building connectors or oracle feeds specifically for popular agent tasks (price feeds, sentiment analysis results, etc.) would save every project from reinventing or scraping data. Basically, treat agents as a new class of client and cater infrastructure to them: a “graph” not just of blockchain data but any data an agent might consume, delivered in a decentralized way. This is ambitious but aligns with making agents truly autonomous participants in the network rather than isolated bots. There’s overlap here with existing oracle providers; perhaps a collaboration or specialized use-case integration could be possible.

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4. Dev Onboarding and Education

‍ Let’s face it – “AI agent” tech sounds intimidating. Many Web3 devs still haven’t played with LangChain or ReAct frameworks or fine-tuned an LLM, which are common in the AI agent world. Conversely, many AI devs don’t grok blockchain intricacies. There’s a knowledge gap. Buidly could act as a bridge for dev education, creating workshops, docs, and example projects that demystify this crossover. Think along the lines of: “Build an AI DeFi agent in a weekend” tutorial series, sample code that shows an agent doing a yield strategy step-by-step, or a template repo that new hackathon teams can fork. By nurturing a community of builders who understand both AI and Web3, we increase the odds of useful applications emerging. Sometimes the missing ingredient is just developer familiarity. Hosting dev bootcamps or hackathons focused on agent use-cases (with a strong practical focus, not just ideation) could also catalyze things. In summary, making it easier to buidl in this domain will naturally lead to more solutions.

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Bottom Line: The Web3 AI agent wave taught us a lot about what not to do – namely, don’t lead with hype and impossibly general promises. The initial narrative got out over its skis, claiming to solve everything overnight. The comedown showed that while the idea of autonomous agents in crypto is powerful, it needs serious builder work to materialize. No, AI agents didn’t fix DeFi or run DAOs in 2024. But they sparked a vision of more automated, intelligent crypto systems that is still worth chasing. The key is to be grounded: focus on specific problems, build the missing infrastructure, and integrate AI in ways that enhance user value today (not in some distant AGI future). Leveraging smart contracts for what they do best (custody and guardrails) and AI for what it does best (strategy and decision-making), it balances autonomy with safety. This kind of hybrid on-chain/off-chain architecture is likely how meaningful AI agent integrations will look: the blockchain provides verifiability and constraints, the AI provides flexibility and intelligence. Over time, if those off-chain parts can be decentralized (through crypto-economic incentives for nodes, etc.), we inch closer to the holy grail of fully autonomous, decentralized agents.

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For teams like buidly and the broader builder community, the opportunity isn’t to ride the next hype wave, but to deliver the tools and products that make AI + Web3 actually useful. That means embracing the complexity (both AI and blockchain are hard individually; together doubly so) and finding creative solutions to marry them. It’s not glamorous grunt work, but it’s how we get from flashy demos to apps our friends and parents might actually use one day without even realizing an “AI agent” is under the hood.

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In the end, the hype wave will be a footnote – what matters is who sticks around to build Web3’s AI agent reality. Less talk, more code. Let’s buidl it.

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