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Who is Leading Autonomous Agent Creation?
By now, the pattern is clear: AI is being built behind closed doors. It’s trained on proprietary data, deployed through centralized APIs, and governed by a handful of companies whose incentives rarely align with end users. You don't own it, you don't control it, you just rent access.
This isn’t a bug. It’s the business model. And it’s why a growing roster of Web3 builders is developing something radically different: autonomous agents that don't need permission, don't rely on centralized compute, and don't serve corporate interests. They are decentralized. They operate autonomously. And they're owned by their creators, communities, or no one at all.
These agents are being launched right now, and they are not being launched through cloud consoles or private betas. Instead, they're being launched via decentralized platforms purpose-built for AI in open systems. Each one takes a different approach. Together, they're forcing a new question: If the last generation of AI was built for control, who's launching the version that works for us?
Why AI Agents Need Decentralized Launchpads
Autonomous agents need autonomy. That sounds obvious, but it isn’t how things work in Web2. These AI agents exist in sandboxes. They rely on APIs owned by corporations. They execute logic within walled gardens. And when their actions touch the real world—payments, communication, contracts, they can’t act without a human hand.
Web3 breaks this dependency chain. In a decentralized environment, agents can manage wallets, execute transactions, access protocols, and persist without external triggers. They become real participants in the digital economy—not just appendages of an app. But this requires infrastructure: permissionless deployment, trustless execution, and native monetization.
Launchpads are solving this problem. They offer frameworks for building agents, platforms for distributing them, and economic systems that make them sustainable. Unlike the centralized stacks of Big Tech, these platforms don't assume the user is just a user. They assume you might be a creator, a governor, or even a competitor.
The Launchpads: Platforms Building Agent-First Systems
The decentralized agent stack is a mosaic of distinct approaches to infrastructure, identity, and monetization. Each platform tackles the same problem from a different angle: how to launch autonomous agents that can act, adapt, and accrue value in Web3. Some lead with no-code accessibility, others with modularity or scale. But all of them are building toward a world where agents are not just intelligent but sovereign.
Cookie.fun
Cookie.fun is where experimentation and virality intersect. Deployed on Solana, it treats agents less like services and more like characters—tokenized memes that users can create, trade, and animate. Since its launch, the platform has enabled the creation of numerous meme agents, many of which have gone viral within crypto communities on X, contributing to spikes in engagement and trading volume.
Its infrastructure is deliberately minimal: a no-code frontend that abstracts away the technical complexity in favor of social participation. While decentralization exists at the token layer, core agent logic is still centralized or obfuscated. Identity here is mummified and ephemeral; agents are avatars with cult followings, not verifiable actors. Monetization is speculative by design, driven by agent token trading, with platform fees skimming from volume. Cookie.fun thrives not on deep utility but on networked attention—and it works.
Virtuals Protocol
Virtuals is one of the most complete launchpad ecosystems currently live. Operating on Base, it has deployed over 16,000 agents, boasts a market cap estimated at $5 billion as of January 2025, and supports a broad range of agent types from influencers to DeFi bots. The platform has seen over 30,000 creator wallets interact with its ecosystem and consistently ranks among the most engaged agent platforms.
It introduced the G.A.M.E. framework (Generative Agent Modeling Environment), which supports multimodal agents trained across text, audio, and animation layers. Its identity model is token-native: every agent is issued as a unique on-chain asset, enabling governance, upgradeability, and historical transparency. Virtuals is semi-decentralized, using the on-chain deployment with off-chain inference infrastructure like Runpod. Creators diversify their monetization strategies by earning royalties, utilizing staking-based incentives, and selling agent services or interactions directly.
Autonolas
Autonolas (Olas Network) is perhaps the most rigorously decentralized platform in the space. With hundreds of thousands of agent-to-agent transactions powering services like the Mech Marketplace, it has emerged as a core infrastructure layer for agent coordination. It currently powers dozens of live decentralized services and has grown its developer network significantly through on-chain funding rounds and DAO participation.
The multi-agent system protocol enables the deployment, coordination, and governance of composable services entirely on-chain. Agents are modular actors within collective frameworks. Identity is co-owned via $OLAS token governance, which governs how agents are instantiated, maintained, and upgraded. Monetization is protocol-aligned: contributors earn through bonding, rewards from agent usage, and DAO-driven incentive structures. What sets Autonolas apart is its belief that agents should evolve cooperatively, a vision closer to infrastructure than a product.
MyShell
MyShell is the platform that revolutionized the agent landscape. Boasting a user base of over 5 million, it empowers more than 170,000 creators and has deployed over 200,000 agents, establishing its dominance in both scale and cultural impact. MyShell agents integrate seamlessly across more than 15 social platforms and app ecosystems.and their NFT marketplace has surpassed $10M in volume since launch. It offers no-code tooling, plug-and-play training with leading LLMs, and social integration across platforms like Telegram and Discord CoinGecko.
The model blends two approaches: the front end captivates users with its centralized design, delivering speed, intuitiveness, and familiarity, while smart contracts and NFTs distribute economic elements seamlessly on the BNB Chain. Identity is creator-defined and community-anchored. Agents are personas—chatbots, assistants, and influencers—each with histories and loyal followings. Monetization is robust: creators can sell NFTs, receive token rewards, and earn usage-based income. It sacrifices some decentralization for scale but arguably proves that mass adoption starts with smooth UX.
PAAL
PAAL is an infrastructure-lite, use-case-heavy platform focused on deployable agents in financial and data-driven workflows. It gained momentum with its Telegram-native bots and a rapidly growing community, experiencing a significant token price increase in 2024. Its agents collectively serve over 100,000 active users across DeFi dashboards, analytics tools, and Telegram integrations PAAL.
PAAL agents automate tasks like trading, research, and DeFi intelligence. While decentralization exists in the token and agent deployment layers, we centrally manage inference and analytics. We tightly scope identity, focusing on agents as functional abstractions rather than personas. Although PAAL may lack extensibility, it excels in reliability. They prioritize subscription-based monetization driven by performance, supported by the $PAAL token and a robust staking economy.
Humans.ai
Where most platforms optimize for performance or adoption, Humans.ai prioritizes integrity. Backed by a proprietary Layer-1 blockchain, the platform anchors every agent to biometric data and self-sovereign identity frameworks, creating auditability from the moment of instantiation. Its biometric KYC model has processed hundreds of thousands of identity verifications across early pilots, and it has partnered with healthcare and fintech verticals to develop ethical AI frameworks.
Governance is on-chain, identity is cryptographic, and execution is verifiable. It supports ZK-based model validation, making it suitable for sensitive verticals like healthcare and enterprise AI. Monetization flows through licensing, model/task royalties, and NFTs, always with a focus on consent and traceability. It's not fast or flashy, but it's principled—and in the coming wave of AI regulation, that may be its biggest advantage.
ChainGPT
ChainGPT isn't a launchpad in the traditional sense. It's the infrastructure layer the rest of the ecosystem may end up building on. At the center of this is the AIVM—a Layer-1 blockchain purpose-built for AI agents. Unlike general-purpose chains retrofitting support for AI, AIVM is designed from the ground up to support agent execution, decentralized training, on-chain inference, verifiable compute, and monetizable model marketplaces.
The AIVM introduces a trustless AI runtime: agents are deployed via smart contracts, trained using decentralized GPU networks, and verified through cryptographic proofs. It also offers developer SDKs, model registries, privacy-preserving data marketplaces, and agent coordination frameworks. This full-stack system gives developers total control over how agents are built, operated, and monetized.
What’s in the Way: UX, Trust, and Infrastructure
Most launchpads today still feel experimental. Wallets and gas fees make onboarding clunky. Interacting with an agent often means switching between interfaces. Debugging behavior is opaque. Transparency is usually promised but frequently falls short. This creates a UX problem that serves as the primary barrier to widespread agent adoption.
The second is trust. Agents are autonomous, which means they can misbehave. Without alignment systems or formal verification, they can go off-script. Right now, users are trusting platforms. Long-term, that won’t scale. We need reproducible agents, verifiable logic, and systems like ZKML or agent-level audits. We’re not there yet.
Third is infrastructure. Decentralized compute is still maturing. Most agents rely on off-chain inference. That’s fine for now - but not for long. As agents move from chat to execution, the need for performant, decentralized, on-chain logic will only grow. Projects like ChainGPT are ahead of the curve here, but the gap is real.
The Solution: Composability, Incentives, and Infrastructure
The fix is to build something natively different. That means embracing modularity, aligning incentives, and investing in infrastructure that allows agents to operate fully decentralized at scale.
Launchpads like MyShell and Virtuals are already refining the onboarding experience. Autonolas is building infrastructure for coordinated, decentralized services. Humans.ai is defining what a verifiable identity can look like for AI. And ChainGPT is laying down the runtime: decentralized GPU networks, agent-native deployment tools, and a virtual machine that treats agents as first-class citizens of the blockchain.
Together, these projects are creating a new norm. One where agents aren't just spawned and forgotten but evolve, adapt, and deliver real value inside decentralized environments.
The old AI was extractive. The new AI is expressive. The question isn’t who has the smartest model?
It’s who will give it the most room to move.
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