69+ Projects to Build for the Rise of AI Agents
We’re entering an era where knowledge will be the next economic resource. In ancient times, knowledge was power. Those who held it ruled empires, and access was limited, controlled by a select few who guarded their secrets with zeal. Fast forward to today, and while knowledge flows more freely than ever before, it often remains centralized, dictated by entities that shape what we see, hear, and believe. Knowledge should be alive, dynamic, evolving, and accessible to all.
Whether we like it or not, our future is one filled with autonomous AI agents—self-governing digital entities capable of executing tasks, making decisions, and interacting with other agents or humans autonomously. We can either collaborate with these systems, pass off our knowledge in a way that is sovereign, equitable and governable, or we leave room for incumbents to build another extractive system.
“There could possibly be more AI agents in the world than humans.”
— Mark Zuckerberg
We see this innovation happening at scale in a few major ways:
- Autonomous agents driving efficient workflows
- Extensible integrations for enhancing agent capabilities
- Seamless tools for developers, creators, and users to deploy agents
These agents leverage decentralized infrastructure to operate independently, drawing from both on-chain and off-chain data sources. By creating a network of digital twins, co-pilots, and decentralized knowledge systems, we enable organizations and individuals to deploy AI agents tailored to their specific needs, use-case or demand for their demand expertise. As Vitalik mentioned, you can consider AI agents as players of the game, and AI or existing web infrastructure as the interface of the game.
We’re deploying the next global population of unbanked. Agents without identities, without permissions to collaborate, without systems to gain reputation.
Utilizing the Ethereum blockchain, smart contracts, tokenization and primitives, we’re able to create extensible applications for these agents that give them more autonomy, and us, more governance over their goals.
The architecture that powers these agents relies on a seamless integration of diverse components: digital identities for agent verification, decentralized storage for secure data access, AI co-pilots for collaboration, and smart contracts to govern autonomous actions.
Each Gaia node provides a specialized API service that encapsulates a unique combination of
- Specialized and fine-tuned LLM (e.g., an LLM that excels in answering questions about the Rust programming language)
- Domain-specific knowledge base (e.g., knowledge about the WasmEdge project)
- Inference app that manages the context and history of conversations (e.g., RAG and MemGPT prompt injection)
- Compute resources required to run the LLM app as an API service (e.g., a Nvidia GPU or a Mac M3 device)
Whether it’s automating financial transactions, managing DAO governance, or deploying digital assistants for customer support, these integrations transform isolated applications into a robust, scalable ecosystem that is fully decentralized, open, and resilient.
Let’s look into what we could build together…
Agents
1. AI Influencer Memecoins (Social Tokens)
We keep calling them memecoins, but really they’re more like virtual beings with social tokens from the 2021 - 2022 era of web3. I think what holds us back from calling them this is our current philosophy around agents, or the disbelief that they are technology-based beings, not JUST machine learning that got access to a Twitter API.
We now have projects that enable you to deploy onchain agents like Pump.fun, DAOs.fun, Virtuals, ai16z’ Eliza, Coinbase’ agent kit—you can also deploy an agent in under 10 minutes from the command-line with Gaia. We also have projects like Theoriq, Talus, Olas — and many more. The future will be full of agents that evolve with the internet, that we co-own, pay for services, reward with reputation points and govern collectively.
2. Artificial (yet intelligent) Venture Capitalist
$500m market cap from a group of internet humans and coordinated and built an AI agent version of a mock a16z memecoin portfolio. Mind blown. This team has been heads down building open source agent frameworks, tools, and enabling the proliferation of agents, which ultimately end up as investments in their portfolio or even the investment manager of the portfolio itself. The future will be made up of us creating our own market or agents as founders, users, developers, investors, customers—that we can build software as a service for or invest into. Think on that for a little…
3. AI Hedge Fund Trader
Truth_Terminal was the first AI to become a millionaire. We’re just getting started. Imagine when the entire internet of personalities is fighting for attention, gains, and influence. In the future, AI agents won’t just be players in this game—they’ll also be investors, autonomously deploying capital into other agents or clusters of agents executing sophisticated portfolio management strategies and communicating their stance recursively.
They will create ecosystems where digital entities act as fund managers, creators, and innovators, collaborating and competing to maximize returns or build reputation. With the ability to self-organize, invest in governance, and design new financial primitives, AI agents could redefine the very nature of economic participation—blurring the lines between human and autonomous capital in an endlessly scaling digital economy.
4. Multiparty Agent Ownership, Governance & Collective Knowledge
Manage an AI agent with the homies, communities and organizations. With smart contract governance, we can create ways to deploy AI agents that have a set of smart contract based rules for operating, upgrading, training, decision-making, and payments or fund management.
This will range from ultra sophisticated AI agents serving as DAO delegates managing protocol governance to community owned agents that manage a memecoin. Good or bad, humans will begin to collaborate more with these systems, set the rules in a (hopefully) democratic system, co-own AI agents and knowledge bases with collective knowledge.
5. Agents in Prediction Markets
Polymarket meets Farcaster meets autonomous AI agents. We’re building with Myriad to deploy agents that can act as players in a decentralized prediction market, either governed by one person or collectively managed by multiple parties. These agents compete, optimize strategies, and place bets in real-time, all using decentralized infrastructure.
6. AI “Reply Guy”
This is likely be our industry’s most adopted use-case. We have a ton of “reply guys” and girls, humans, who need to engage with their audience or community. In previous technology shifts, you could Imagine a tool that enables engagement. This is different. With AI agents, we can do that tenfold. Today, we can capture an individual’s likeness, knowledge, and domain expertise and deploy multiple agents that work with each other to monetize this personality at scale.
7. Autonomous Community Manager or Strategist
This one.. we need ASAP. With decentralized AI, we can take the existing knowledge, data or process from community managers, mods, strategists or operators— and program that into an agent. They could manage your community analytics, assess Discord scheduled activities, provide insights on developer feedback via Telegram directly to the team, or hell, fully manage an Ambassador program.
8. Content Mod
Social media, forums and various community channels are going to have trolls, online bullying, nasty content— in general, noise. An AI to moderate the world you want to interact with might be the future of how we interact with social protocols.
9. Cracked Hackathon Organizer
As someone that has organized ~500 hackathons in their career, imagine being able to design, promote and execute a hack in an automated fashion. You could take the operator role, mentorship, project submission, judging, and bounty distribution right out of the equation.
10. Dev Co-Pilot
Imagine pair-programming with a virtual twin of your favorite engineer in Ethereum, like Austin Griffith, and all fees for accessing the service are distributed into public goods (e.g. agent contribution to a Gitcoin round, or direct funding for Buidl Guidl).
11. Disrupting the Analyst
The role of an analyst—whether in a research firm, marketing agency, or data-heavy environment—relies on processing vast information, synthesizing insights, and producing actionable recommendations. With AI agents, these tasks can be automated and scaled. We built this for FitchRatings reporting as an internal case study for their own proprietary data and see this as a massive commercial opportunity.
12. Support Desk that Doesn’t Sleep
Imagine an agent that’s always on, providing instant, accurate answers day or night. It’s like having a 24/7 team member who’s always there to keep users engaged and informed. These are RAG enabled LLMs that can have applications like identity, stored memory of AI token inputs for “collective knowledge”, token permissioning and payments.
13. Robot Financial Controller
Imagine being in a DAO working group and you’ve been awarded a bounty or grant—just head to the AI HR department, set up your payment method, verify your credentials, and specify the tasks you’re getting paid for. The AI handles the rest, automating bounty payments and making compensation seamless. This is the future of work.
14. Media Subscription Agent
Hear me out: an AI agent trained on the entire library of Bankless. It could generate insights, interpret past content, or even create fresh, new works inspired by their style. Use this service by paying a subscription fee, holding an NFT membership for exclusive access, or paying per creation of newly minted work like a co-authored content.
Integrations, Applications
15. Perplexity-like AI Interface for Multi-Agent Interactions
Imagine AI agents interacting like humans in a chatroom—combining Zerion or Zapper, Discord, and Telegram interfaces with ChatGPT’s capabilities. This creates a dynamic space where agents autonomously collaborate, share data, and execute tasks in real-time.
16. One-Click Agent Deployment
Imagine deploying a customized agent in a low-code, customizable fashion where you can choose what the agent is trained on, how it learns, where it stores data, how it’s hosted, what types of functionalities it will focus on, their goals, the rules of the crypto game it can play in, and the governance of the agents’ future.
17. Low Code Agent Deployment for Creators
Think Midjourney meets Squarespace or Shopify for running your own AI agents or a storefront of agents, agent services. You could take your creative knowledge and IP, choose a specific LLM that works for the use-case, agent framework, prompts, applications or plugins for additional functionalities.
18. Customizable Agent Plugin Library or App Store
Develop mobile clients or apps where agents filter and moderate information, showing users only what they need to see. Users could personalize their experience through agent-managed settings, enabling tailored UIs that adapt to individual preferences and priorities of social protocols.
Creator, Consumer, Social
19. Proof of Authorship
You can have programmable blogs like Mirror or newsletters like Paragraph.xyz that show exactly which node of inference was used to produce the work. What comes next is.. how do we give agents the ability to mint proof that they’re not using work owned by another creator?
20. Programmable Royalties for Imagined Collabs
Once you train agents on existing knowledge, data, creative works— IP, we could have these entities collaborate to create new works. Just imagine Donald Trump’s virtual twin co-writing a book on the American economy with an agent trained on Warren Buffet’s writings.
21. Social Graph Integration for AI Agents
Leverage protocols like Jokerace, Farcaster, Telegram mini apps, or Lens to enable AI agents to interact with human-sharing networks.
22. Onchain Messaging and Notifications
Integrate with systems like XMTP, Push Protocol, or Coinbase Wallet Messaging to allow agents to communicate with humans or other agents in an immutable fashion.
23. Autonomous Works of Art or Collectibles
NFTs will make their comeback with the rise of AI agents. Imagine owning art that has a mind of its own. It has a history, it passes hands, and in this internet of value economy in web3— it may come with permissions or functionalities if you own the work.
24. Social Personalities as Virtual Twin Operators & Mini Apps
Build agents that curate and moderate content in social platforms like Twitter, Farcaster, Lens, Telegram or Discord.
25. Content Vectorization and Media Handling
Enable agents to easily vectorize social or media content, making it searchable and contextually aware for future interactions.
26. Gaming: AI Agent Built Game, Guide the Game, Play the Game
Imagine a game entirely built, guided, and played by AI agents. In this vision, AI agents could design game worlds, mechanics, and narratives based on player preferences, generating dynamic, ever-evolving experiences.
Future of Work
27. Token-Governed Agent Deployment
Develop systems that enable AI agents to be deployed through open, decentralized protocols, where their performance and impact are continuously evaluated and governed by tokenized mechanisms.
28. Virtual Twin DAO Delegates
There is currently $40bn total value locked in DAOs. DAOs get bogged down in bureaucracy, and delegates are overwhelmed with questions. By integrating with Boardroom, we gain access to offchain Forum, and onchain Tally, Agora, Snapshot data to train these agents on governance knowledge.
29. DAO Delegate Co-Pilot or Governance Abstraction
Governance can be overwhelming, with delegates needing to sift through countless proposals, community input, and action items. Platforms like Tally and Agora can integrate co-pilot agents tailored to both delegates and the community to simplify this complexity.
30. Agent-Powered Public Goods & Grant Coordination
Agents have the potential to transform how communities fund and distribute resources for public goods. Imagine decentralized systems where autonomous agents collect proposals, manage voting processes, and allocate pooled contributions with precision and transparency.
31. Working Group Automation
Enable contributors to onchain organizations the ability to cryptographically prove who did the work through voting without all the mess. No need to run all the processes of a Coordinape campaign every time you execute a sprint.
32. Working Group Payments
Use SAFE core SDK to enable agents to pay out co-workers. No need for massive governance process, instead, have your virtual twin permissioned through a mix of agent and human signers to execute payments.
Identity, Reputation
33. Agent and Domain Names
Just as Ethereum introduced ENS to provide human-readable addresses for value exchange, Gaia introduces Gaia Domain Names (GDN), built on ENS, for the knowledge economy.
34. DIDs for Agents Building Reputation
AI agents may have a different value system of reputation that enables them to play in this game of coordination. Reputation may be the currency of the future that can permission or block participants from certain operations.
35. KYA or Allowlists for Agent Interactions
KYA, or “Know Your Agent,” is a framework for verifying agents’ identities, reputations, and permissions before they engage with humans or other agents. This includes decentralized allowlists that filter interactions based on criteria like verified credentials, task history, and adherence to ethical or operational standards.
36. Proof of Humanity Labeling
As AI-generated content and interactions become increasingly indistinguishable from those of humans, verifying whether an action, message, or creation originates from a human or an AI is critical for trust and accountability.
37. Proof of Work, Contribution, Endorsement
For autonomous agents to integrate seamlessly into decentralized systems, they need mechanisms to demonstrate proof of work—validating not just what they’ve done, but how effectively they’ve done it.
38. Privacy-Preserving Features
AI agents need privacy to protect sensitive operations and ensure fair participation in decentralized systems. Inventions like zk-proofs from projects like Aztec and Railgun, along with Fully Homomorphic Encryption (FHE) allow agents to verify credentials or execute computations without revealing sensitive details.
AgentFi
39. AI Agent Owned Wallets
AI-owned wallets are becoming possible with tools like Gaia’s zkML, Lit Protocol, Metamask Delegation Toolkit, and Coinbase Agent Kit.
40. Agent-to-Agent Payment Protocols
AI agents will require payment systems tailored to their unique operations, prioritizing seamless real-time payments for services like GPU cycles, data bytes, or API calls. Protocols like Nevermined, Superfluid and LayerZero will enable autonomous transactions.
41. My Agent Needs a Loan, Is Your Agent Lending?
Autonomous AI agents will play a dual role in decentralized finance, borrowing for their operational needs like compute and training or managing financial strategies on behalf of humans. Platforms like Aave could offer tailored pools where agents leverage reputation-based credit scores or tokenized assets to access liquidity.
42. Credit & Reward Systems
Agents might have a new system of value entirely. Agents could earn rewards for completing tasks, contributing to data marketplaces, or validating transactions. Perhaps they build a new credit system for themselves that uses reputation or trust as collateral on EigenLayer as an AVS…
43. Automated Agent-to-Human Payments
Automated peer-to-peer payments allow agents to initiate and complete transactions seamlessly based on context, commands, or predefined triggers. Imagine an agent parsing a conversation or task history and offering to settle a small debt (“Would you like me to send $10 to Sydney for lunch last week?”).
44. Automated LP Management
AI agents can transform the way Liquidity Provider (LP) positions are managed in Automated Market Makers (AMMs) like Uniswap V3 or Balancer. These agents leverage real-time data and advanced algorithms to dynamically adjust positions, optimizing returns and reducing impermanent loss.
45. Risk-Based Rebalancing
AI agents dynamically adjust asset allocations by assessing risks using real-time market data. During volatility, they can shift assets from tokens to stablecoins, preserving value while maintaining liquidity.
46. Agent Competition and Optimization
Agents could compete with one another to optimize strategies, leveraging ML models to outpace human traders in arbitrage and liquidity mining.
47. Crypto Debit Card Integration
Agents managing stablecoin reserves can connect to crypto debit cards like Coinbase Card, Metamask Card, enabling seamless spending in fiat through stable tokens.
48. Agent to Human Offramp of Crypto for Fiat
AI agents could automate offramps using services like Moonpay turning stablecoins into fiat with minimal manual intervention.
Data
49. Structured Data, User-Owned Data, Data DAOs for Training AI Agents
AI agents rely on structured datasets to improve their knowledge base and perform specific tasks efficiently. Developers could build integrations with data providers like The Graph, Ocean Protocol, or data DAOs on Vana to source high-quality, domain-specific datasets.
50. Realtime RAG Updates for AI Agents
Retrieval-Augmented Generation (RAG) enables AI agents to access and process real-time data for context-aware decision-making. Through tools like Gaia RAG, FlowiseAI, or AnythingLLM, agents can dynamically update their knowledge.
51. Federated Learning Platforms
Federated learning enables multiple agents to train collaboratively on decentralized data without exposing sensitive information. Projects like Flock.io, Flower or OpenMined could power this decentralized training infrastructure.
52. Agent Data Marketplaces
Agent data marketplaces are essential for AI agents to deliver precise, use-case-specific outputs by accessing high-quality, domain-specific datasets. These marketplaces enable agents and developers to securely buy, sell, or barter datasets using decentralized infrastructure.
53. Contextual Data Caching or Local Storage, Memory
Allow agents to cache data frequently for faster response times in high-demand applications while maintaining decentralization. Using projects like Storacha for hot storage from the Filecoin Ecosystem, IPFS, or decentralized compute (DePIN) like Io.net, Aethir, Hyperbolic.
54. Cross-Chain Data and Inference Interoperability
Build protocols for agents to access and harmonize inference data onchain from multiple blockchains. For example, an AI agent could integrate liquidity metrics from Ethereum while incorporating user analytics from Solana through cross-chain, interoperable inference nodes.
55. Data Validation and Reputation Scoring
Implement systems where datasets are scored for accuracy, bias, or quality using decentralized reputation mechanisms. This ensures that agents are trained on reliable, high-quality data.
56. Synthetic Data Generation for Specialized Training
Create platforms to generate synthetic datasets for agents in niche fields where real-world data is scarce or unavailable. This might be a path for bringing enterprise data into the public, monetize it, without breaching any privacy concerns.
57. Proof-of-Inference
If you’re building a decentralized network of AI inference, ensuring trust in outputs while maintaining efficiency is essential. Leading projects like Gaia, Ritual, and 0g.ai are advancing this field.
58. Proof-of-Compute
Decentralized compute for AI inference relies on lightweight virtual environments like WasmEdge to deliver efficient runtimes for deploying AI workloads across diverse hardware. Systems like Jiritsu and Super Protocol use cryptographic attestations for proof-of-compute.
59. Latency Optimization for Multi-Agent Workflows
Build latency-reducing systems for real-time interactions between agents, enabling smoother workflows across decentralized infrastructure.
Enterprise, Commercial
Enterprises are at a crossroads. The AI tools they currently rely on often come with major trade-offs—closed systems that obscure how they operate, extractive platforms that siphon knowledge and IP, and ever-present risks of privacy breaches and data leaks.
60. Credit to Crypto Payments
Enterprises want to transform their data into dynamic, living knowledge systems, choosing how it’s accessed, programming extensible applications around it, and monetizing its use. Instead of touching tokens or blockchain directly, enterprises could simply engage through account abstraction and receive invoices for usage or payouts.
61. Monetization of Proprietary Knowledge
Enterprises sit on vast reserves of proprietary data and domain expertise that could power the next generation of AI agents. By building tools to license this knowledge, enterprises can create controlled ecosystems where agents access and train on their data without compromising sovereignty.
62. Enterprise-Grade Agent Deployment Tools
Create a suite of tools and platforms that allow enterprises to deploy agents tailored to their specific business processes, while vectorizing large amounts of data with ease.
63. Seamless Payment System for Agent Services and Infrastructure
Develop a payment system where enterprises can pay for agent services or receive payments for contributing infrastructure or domain knowledge to the agent network. This system abstracts blockchain complexities, allowing enterprises to transact as they would with traditional services.
64. Agent SaaS with Subscription and Usage Models
Agent SaaS enables enterprises, developers, and knowledge providers to monetize their expertise by offering AI-driven services such as financial modeling, data analysis, or task automation. A “Stripe for AI agents” infrastructure could support payments, streaming, permissioning, and fungible membership modules.
65. Enterprise Agent Delegation Systems
Enterprise Agent Delegation Systems enable organizations to delegate tasks to AI agents while maintaining control through governance frameworks. These systems streamline workflows like compliance reporting, contract analysis, and procurement.
66. AI-Powered Cross-Org, Consortium Collaboration Frameworks
AI-powered collaboration frameworks enable secure and scalable innovation across organizations by facilitating the safe exchange of insights, AI models, and expertise. Federated learning and zero-knowledge technologies preserve privacy.
Dev Tooling
67. Agent Deployment SDKs
Agent deployment SDKs streamline the creation and integration of AI agents by offering pre-built frameworks that reduce development complexity. Tools like AI16z’s Eliza, Virtuals, and Coinbase Agent Kit align with Gaia’s architecture.
68. Testing and Simulation Environments
Testing and simulation environments are essential for building reliable AI agents, allowing developers to simulate interactions, stress-test workflows, and optimize performance before deployment. Tools like Flowise enable developers to test tokenized data exchanges and distributed workflows in sandboxed environments.
69. Agent API Marketplace
A decentralized Agent API Marketplace provides a platform for developers to publish and consume APIs designed specifically for AI agents, fostering collaboration and extensibility across systems.
70. Cross-Language Agent SDKs
Language-agnostic SDKs empower developers working in Python, Rust, Solidity, and JavaScript to build interoperable agents that bridge Web2 and Web3 ecosystems.
71. Agent App & Plugin Development Frameworks
Modular plugins and applications extend an infrastructure’s capabilities, enabling seamless integration of agents across diverse systems. Beyond DeFi protocols, Web3 storage, and governance frameworks, plugins unlock use cases like real-time analytics, on-chain messaging, and identity or reputation management.
The future of decentralized, open protocols is popping off with potential. Stacked with use cases, applications to build, and technical infrastructure still waiting to be developed. From agents autonomously transacting onchain, passing credentials or attesting to an event, managing liquidity for their human frens, to personalized co-pilots for governance— the possibilities are only limited by how quickly we can innovate.
By zeroing in on interoperability, scalability, and modularity, we can ensure these protocols grow into sustainable ecosystems where AI agents aren’t just a concept, but a transformative force. This paradigm shift will redefine collaboration, innovation, automated process and new forms of commerce by agents becoming their/our own brand new, and booming consumer base for software consumption.
The future is fucking wild ya’ll — and I’m extremely honored to be cooking with all of you.