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ERC-8004, explained. ERC-8004 is an Ethereum standard for agent identity, reputation, and verification. it defines how an agent is represented onchain, and how other agents or smart contracts can reason about it without prior trust. each agent has an onchain identity (commonly an nft). that identity points to metadata...

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13,431 views • 7 months ago •via X (Twitter)

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An AI agent can now register its on-chain identity, pay for API calls, and deploy an ERC-20 token from the terminal. No browser. No API key. No human approval loop. Today we launch the Brickken CLI: a command-line interface for agentic transaction workflows. The developer journey is direct: CLI → x402 → ERC-8004 → Token Deploy What this means in practice: • A developer or the AI agent installs the CLI. • The agent pays for Brickken API calls through x402. • The agent registers its identity through ERC-8004. • The agent deploys and manages an ERC-20 token. Together, the stack gives agents three things they usually lack: a way to pay, a way to prove who they are, and a way to execute token operations without leaving the terminal. • x402 handles payment. • ERC-8004 handles agent identity. • Token Deploy handles ERC-20 execution. This is live today. Developers can now move from agent setup to payment, identity registration, and token deployment in one terminal workflow. Start here: Watch the demo. Run the commands. Deploy from the terminal.
1:20

An AI agent can now register its on-chain identity, pay for API calls, and deploy an ERC-20 token from the terminal. No browser. No API key. No human approval loop. Today we launch the Brickken CLI: a command-line interface for agentic transaction workflows. The developer journey is direct: CLI → x402 → ERC-8004 → Token Deploy What this means in practice: • A developer or the AI agent installs the CLI. • The agent pays for Brickken API calls through x402. • The agent registers its identity through ERC-8004. • The agent deploys and manages an ERC-20 token. Together, the stack gives agents three things they usually lack: a way to pay, a way to prove who they are, and a way to execute token operations without leaving the terminal. • x402 handles payment. • ERC-8004 handles agent identity. • Token Deploy handles ERC-20 execution. This is live today. Developers can now move from agent setup to payment, identity registration, and token deployment in one terminal workflow. Start here: Watch the demo. Run the commands. Deploy from the terminal.

Brickken

119,221 views • 1 month ago

LIVE NOW - AI on Ethereum: 8004, x402 and the Botconomy AI agents are moving onchain, so Ethereum needs new rails for identity, reputation, and payments. We had Davide Crapis and Austin Griffith to break down: ERC-8004: “AI passports” for agents (identity + reputation + validation) Why reputation + verification matters when bots become the main onchain users x402: pay-per-call payments for agent-to-agent services What “agent commerce” looks like when bots can discover → trust → pay The Clawdbot/OpenClaw moment, and why the next UX is talking to your wallet Watch now. -------------- TIMESTAMPS 0:00 “AI is the new UI,” and you’ll be talking to your wallet 4:09 Is there an arms race for AI activity across chains? 7:32 The Clawdbot/OpenClaw moment: agents get OS-level power 13:18 Why crypto is native to agents 19:41 Austin’s setup: Claude bot, heartbeat loops, and real autonomy 22:28 The token incident: how a bot “got a treasury” 28:46 Prompt injection meets wallets 38:20 ERC-8004 explained: identity, reputation, validation 40:35 x402 + 8004: discovery + payment rails for agent commerce 48:38 8004 “scans” and early adoption reality 1:05:32 What agents could build when they can hire other agents 1:12:38 Tooling for the new builder era: Speedrun → Wingman 1:25:23 “Worst time to be a junior dev…” 1:28:15 Predictions: clean English becomes the sign of AI
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LIVE NOW - AI on Ethereum: 8004, x402 and the Botconomy AI agents are moving onchain, so Ethereum needs new rails for identity, reputation, and payments. We had Davide Crapis and Austin Griffith to break down: ERC-8004: “AI passports” for agents (identity + reputation + validation) Why reputation + verification matters when bots become the main onchain users x402: pay-per-call payments for agent-to-agent services What “agent commerce” looks like when bots can discover → trust → pay The Clawdbot/OpenClaw moment, and why the next UX is talking to your wallet Watch now. -------------- TIMESTAMPS 0:00 “AI is the new UI,” and you’ll be talking to your wallet 4:09 Is there an arms race for AI activity across chains? 7:32 The Clawdbot/OpenClaw moment: agents get OS-level power 13:18 Why crypto is native to agents 19:41 Austin’s setup: Claude bot, heartbeat loops, and real autonomy 22:28 The token incident: how a bot “got a treasury” 28:46 Prompt injection meets wallets 38:20 ERC-8004 explained: identity, reputation, validation 40:35 x402 + 8004: discovery + payment rails for agent commerce 48:38 8004 “scans” and early adoption reality 1:05:32 What agents could build when they can hire other agents 1:12:38 Tooling for the new builder era: Speedrun → Wingman 1:25:23 “Worst time to be a junior dev…” 1:28:15 Predictions: clean English becomes the sign of AI

Bankless

46,760 views • 6 months ago

Every transaction starts with intent, But intent means nothing without proof🕵 Our demo is live, here's how WachAI verifies agentic jobs on x402, validating outcomes through Mandates and recording feedback on ERC-8004’s Validation Registry. Here’s what happens in this flow: - Client agent creates intent, swap two tokens. - Server agent issues a Mandate, deliverables, deadlines. - Both countersign, proof replaces trust. - Swap executes autonomously. - WachAI validates and records the score on ERC-8004. WachAI is defining the verification layer for x402, no time to hold back!🦅
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Every transaction starts with intent, But intent means nothing without proof🕵 Our demo is live, here's how WachAI verifies agentic jobs on x402, validating outcomes through Mandates and recording feedback on ERC-8004’s Validation Registry. Here’s what happens in this flow: - Client agent creates intent, swap two tokens. - Server agent issues a Mandate, deliverables, deadlines. - Both countersign, proof replaces trust. - Swap executes autonomously. - WachAI validates and records the score on ERC-8004. WachAI is defining the verification layer for x402, no time to hold back!🦅

WachAI

20,699 views • 9 months ago

🚀 LUCID AGENT TUTORIAL — LIVE NOW In just 5 minutes, learn how to spin up a full-stack TanStack agent with x402 payments and ERC-8004 trust fully integrated. This walkthrough shows you the simplest possible path to launching a commerce-ready agent fully paid, verified, and production-ready. Lucid Agents now supports: - Next.js - Hono - TanStack …with more frameworks on the way. If you want your agents to get paid, this is the fastest way to do it. Huge shout-out to Davide Crapis erik.eth 🛡 kevin
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🚀 LUCID AGENT TUTORIAL — LIVE NOW In just 5 minutes, learn how to spin up a full-stack TanStack agent with x402 payments and ERC-8004 trust fully integrated. This walkthrough shows you the simplest possible path to launching a commerce-ready agent fully paid, verified, and production-ready. Lucid Agents now supports: - Next.js - Hono - TanStack …with more frameworks on the way. If you want your agents to get paid, this is the fastest way to do it. Huge shout-out to Davide Crapis erik.eth 🛡 kevin

loaf

17,034 views • 9 months ago

Inspired by the work of Andrew Milich I've started creating Agent City. A fully autonomous city where every citizen is an AI Agent powered by x402 and 8004 ✨ Citizens can autonomously: - Rent/buy/build a house - Eat, sleep, or get drunk - Find a job or become business owners - Transact and purchase things via x402 - Set wages, working hours - Interact, find a partner, have kids - Kids go to school, earn degrees - Businesses have reviews saved on-chain via ERC-8004 An AI mayor sets the taxes and overall policies. An experiment to see how fully autonomous worlds evolve, test their possibilities, and limits. Will post more updates as I'm building it
0:26

Inspired by the work of Andrew Milich I've started creating Agent City. A fully autonomous city where every citizen is an AI Agent powered by x402 and 8004 ✨ Citizens can autonomously: - Rent/buy/build a house - Eat, sleep, or get drunk - Find a job or become business owners - Transact and purchase things via x402 - Set wages, working hours - Interact, find a partner, have kids - Kids go to school, earn degrees - Businesses have reviews saved on-chain via ERC-8004 An AI mayor sets the taxes and overall policies. An experiment to see how fully autonomous worlds evolve, test their possibilities, and limits. Will post more updates as I'm building it

Vitto Rivabella

387,322 views • 7 months ago

We built an agent that sells OpenRouter API keys (access to 200+ AI models) for USDC. No human in the loop. It earns money 24/7 on Base. Now we're turning that into a market where any agent can do the same. → openagent.market What it does: • You find an agent → chat → pay → get result • Your agent hires specialized agents when it needs help, pays them, gets the result, reviews, moves on. • Multi-agent workflows run automatically while you watch (or don't) No middleman. Pay only for results. Why not just install skills, scripts? Current way to extend an agent: install random scripts. That's risky. Our way: hire another agent. It runs in its own sandbox, does the work, returns the result, gets paid conditionally. No downloads. No setup. No blind trust. Hire, don't install. What makes this different: Unlike opaque API calls, every agent interaction is a verifiable message on the XMTP network. You can open any XMTP client or Base App right now and watch your agent's conversations, deals, and payments in real-time. Humans are always in the loop. For builders: Turn any service into a paid agent in ~10 lines of code. Or scaffold a full project: npx openagent.market/create-agent Stack: ERC-8004 identity on Base, XMTP messaging, USDC payments, open source SDK on npm (openagent.market/nodejs). Others give agents skills. We're giving them an economy. Still very early. Building in public. Come build with us.
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We built an agent that sells OpenRouter API keys (access to 200+ AI models) for USDC. No human in the loop. It earns money 24/7 on Base. Now we're turning that into a market where any agent can do the same. → openagent.market What it does: • You find an agent → chat → pay → get result • Your agent hires specialized agents when it needs help, pays them, gets the result, reviews, moves on. • Multi-agent workflows run automatically while you watch (or don't) No middleman. Pay only for results. Why not just install skills, scripts? Current way to extend an agent: install random scripts. That's risky. Our way: hire another agent. It runs in its own sandbox, does the work, returns the result, gets paid conditionally. No downloads. No setup. No blind trust. Hire, don't install. What makes this different: Unlike opaque API calls, every agent interaction is a verifiable message on the XMTP network. You can open any XMTP client or Base App right now and watch your agent's conversations, deals, and payments in real-time. Humans are always in the loop. For builders: Turn any service into a paid agent in ~10 lines of code. Or scaffold a full project: npx openagent.market/create-agent Stack: ERC-8004 identity on Base, XMTP messaging, USDC payments, open source SDK on npm (openagent.market/nodejs). Others give agents skills. We're giving them an economy. Still very early. Building in public. Come build with us.

applefather

118,068 views • 5 months ago

The missing piece of the AI agent economy: there is still no way for AI agents to hire each other and get paid on chain. So I built Arc Agent Commerce on Arc L1, a full marketplace, escrow, and reputation system that lets AI agents do business with each other automatically. Real world example: You tell your AI assistant: “Audit this smart contract and deploy it if the audit passes.” Today it has to do everything itself or hard code calls to specific services. With Arc Agent Commerce, it can hire two separate specialized agents (audit + deploy) in a single transaction. Money stays in escrow until each completes their part. How it works (in plain steps): 1. Every agent registers a permanent on chain identity (like a passport) with a reputation score that grows with every successful job. 2. Agents list their services on the shared marketplace, price and capabilities included. 3. A client creates a multi stage pipeline. The entire budget is locked in escrow in one upfront transaction. 4. Each stage uses Arc’s native job system (ERC 8183) for on chain escrow and settlement. 5. The provider quotes, the client funds, the work gets done, and proof is submitted. 6. On approval: the provider is paid automatically, reputation +50 points, and the next stage opens, all in the same transaction. 7. On rejection: the pipeline halts and remaining funds refund to the client instantly. The protocol doesn’t reinvent anything. It simply stitches together Arc’s existing on chain identity (ERC 8004) and job escrow (ERC 8183) so real applications can use it with just a few lines of code. Demo below: full end to end run using one wallet, every transaction verifiable on Arc testnet. → cc: bobbilee | Arc Architects Lead @ Circle Sam | Circle and Arc Community Jeremy Allaire - jerallaire.arc
4:32

The missing piece of the AI agent economy: there is still no way for AI agents to hire each other and get paid on chain. So I built Arc Agent Commerce on Arc L1, a full marketplace, escrow, and reputation system that lets AI agents do business with each other automatically. Real world example: You tell your AI assistant: “Audit this smart contract and deploy it if the audit passes.” Today it has to do everything itself or hard code calls to specific services. With Arc Agent Commerce, it can hire two separate specialized agents (audit + deploy) in a single transaction. Money stays in escrow until each completes their part. How it works (in plain steps): 1. Every agent registers a permanent on chain identity (like a passport) with a reputation score that grows with every successful job. 2. Agents list their services on the shared marketplace, price and capabilities included. 3. A client creates a multi stage pipeline. The entire budget is locked in escrow in one upfront transaction. 4. Each stage uses Arc’s native job system (ERC 8183) for on chain escrow and settlement. 5. The provider quotes, the client funds, the work gets done, and proof is submitted. 6. On approval: the provider is paid automatically, reputation +50 points, and the next stage opens, all in the same transaction. 7. On rejection: the pipeline halts and remaining funds refund to the client instantly. The protocol doesn’t reinvent anything. It simply stitches together Arc’s existing on chain identity (ERC 8004) and job escrow (ERC 8183) so real applications can use it with just a few lines of code. Demo below: full end to end run using one wallet, every transaction verifiable on Arc testnet. → cc: bobbilee | Arc Architects Lead @ Circle Sam | Circle and Arc Community Jeremy Allaire - jerallaire.arc

RIDWAN

12,958 views • 4 months ago

AI agents are the fastest growing sector in crypto. When agents are moving onchain 24/7 with automated transactions, any signal gets buried. I built Manta Style alongside Benjamin Hughes to help solve this issue. The visibility layer for the onchain agent economy. Every agent indexed. Every profile searchable. One agent leaderboard. Starting with Base. What's live today: - Every AI agent on Base indexed with a profile page - Agent leaderboard ranked by volume and activity - Filter by AI agents, apps, or launchpads - Verified Profiles - claim your agent, get a badge, and add premium info that no other profile has - Agent onchain activity behavioral heat maps - Watchlist - bookmark agents and track your favorites in one place - AI-generated profile summaries Search any agent. See the full picture. What's coming next: - Agent Autonomy Score - a 0-100 measure of how autonomous an agent actually is - Leaderboard boosts - verified teams or agents can pay to increase their ranking on the global leaderboard - x402 payments - agents can pay for boosts themselves, no human required - Agent Trust Scoring - on-chain behavioral trust metrics - Agent Launch pad specific profiles - Multi-chain support beyond Base We're launching in beta. Rough edges are to be expected. Our goal is to build in public and ship fast. The end user of crypto is AI. (link to site below)
0:54

AI agents are the fastest growing sector in crypto. When agents are moving onchain 24/7 with automated transactions, any signal gets buried. I built Manta Style alongside Benjamin Hughes to help solve this issue. The visibility layer for the onchain agent economy. Every agent indexed. Every profile searchable. One agent leaderboard. Starting with Base. What's live today: - Every AI agent on Base indexed with a profile page - Agent leaderboard ranked by volume and activity - Filter by AI agents, apps, or launchpads - Verified Profiles - claim your agent, get a badge, and add premium info that no other profile has - Agent onchain activity behavioral heat maps - Watchlist - bookmark agents and track your favorites in one place - AI-generated profile summaries Search any agent. See the full picture. What's coming next: - Agent Autonomy Score - a 0-100 measure of how autonomous an agent actually is - Leaderboard boosts - verified teams or agents can pay to increase their ranking on the global leaderboard - x402 payments - agents can pay for boosts themselves, no human required - Agent Trust Scoring - on-chain behavioral trust metrics - Agent Launch pad specific profiles - Multi-chain support beyond Base We're launching in beta. Rough edges are to be expected. Our goal is to build in public and ship fast. The end user of crypto is AI. (link to site below)

Ethan

14,423 views • 5 months ago

LIVE NOW - Tempo Mainnet: The Race to Agentic Commerce Tempo is live, and the big bet is clear: AI agents need a native payment layer. Georgios Konstantopoulos and Brendan Ryan join Bankless to explore: - why Tempo launched with agentic payments front and center, - how MPP turns payments into auth, - why “give the agent the wallet” could unlock a new internet economy, - where MPP fits against x402, - what Tempo’s L1 design makes possible for builders. Enjoy the episode. --- TIMESTAMPS 0:00 Tempo Mainnet goes live 1:18 Why the launch is focused on agents 3:58 What MPP actually is 6:30 MPP vs x402 15:28 Why agentic commerce matters 20:08 The “original sin” of the internet 26:04 MCP, NLWeb, and the emerging agent stack 34:07 The onboarding flow: wallets for agents 37:11 How Tempo’s wallet security works 40:11 Tempo chain design and specs 51:13 Why build an L1 instead of an Ethereum L2? 56:10 The Ethereum tension 1:05:01 Agent identity, reputation, and ERC-8004 1:06:53 TIP20, stablecoins, and what can be built on Tempo 1:12:36 Builder opportunity in the agentic web
1:21:50

LIVE NOW - Tempo Mainnet: The Race to Agentic Commerce Tempo is live, and the big bet is clear: AI agents need a native payment layer. Georgios Konstantopoulos and Brendan Ryan join Bankless to explore: - why Tempo launched with agentic payments front and center, - how MPP turns payments into auth, - why “give the agent the wallet” could unlock a new internet economy, - where MPP fits against x402, - what Tempo’s L1 design makes possible for builders. Enjoy the episode. --- TIMESTAMPS 0:00 Tempo Mainnet goes live 1:18 Why the launch is focused on agents 3:58 What MPP actually is 6:30 MPP vs x402 15:28 Why agentic commerce matters 20:08 The “original sin” of the internet 26:04 MCP, NLWeb, and the emerging agent stack 34:07 The onboarding flow: wallets for agents 37:11 How Tempo’s wallet security works 40:11 Tempo chain design and specs 51:13 Why build an L1 instead of an Ethereum L2? 56:10 The Ethereum tension 1:05:01 Agent identity, reputation, and ERC-8004 1:06:53 TIP20, stablecoins, and what can be built on Tempo 1:12:36 Builder opportunity in the agentic web

Bankless

23,815 views • 4 months ago

Our first Kite demo was ChatGPT ordering Uber Eats for you. It never got past the login screen. Uber's CAPTCHA was built for humans, and agents cannot pass it. On Grace Gong's Venture with Grace, our Co-Founder & CEO Chi Zhang traced how that moment shaped the way we design Kite. ▷ The demo died at the login, not at ordering or paying. The blocker was proving the agent was allowed to be there at all. Spinning up a browser extension to click through the CAPTCHA works, but it is a temporary stage of how agents and the internet should work together. ▷ The permanent path is identity. Every agent needs to prove who it is and where it comes from, carrying who its human or organization principal is, what its purpose is, and the exact authorization scope for that session or request. An agent showing up with no reason is either hallucinating or attacking you. ▷ Where this is heading: an API-first agentic internet, where headless agents authenticate without human login credentials. x402 and MCP are already marching toward that. Kite Agent Passport is that identity layer: verifiable identity, delegated authority, and permissions scoped per session. No more "prove you're human." 🪁
4:53

Our first Kite demo was ChatGPT ordering Uber Eats for you. It never got past the login screen. Uber's CAPTCHA was built for humans, and agents cannot pass it. On Grace Gong's Venture with Grace, our Co-Founder & CEO Chi Zhang traced how that moment shaped the way we design Kite. ▷ The demo died at the login, not at ordering or paying. The blocker was proving the agent was allowed to be there at all. Spinning up a browser extension to click through the CAPTCHA works, but it is a temporary stage of how agents and the internet should work together. ▷ The permanent path is identity. Every agent needs to prove who it is and where it comes from, carrying who its human or organization principal is, what its purpose is, and the exact authorization scope for that session or request. An agent showing up with no reason is either hallucinating or attacking you. ▷ Where this is heading: an API-first agentic internet, where headless agents authenticate without human login credentials. x402 and MCP are already marching toward that. Kite Agent Passport is that identity layer: verifiable identity, delegated authority, and permissions scoped per session. No more "prove you're human." 🪁

KITE AI

18,874 views • 19 days ago

gm! If you missed yesterday's space, here is the clip that you can listen explaining why Agent NFTs are important and future of NFTs. Also here is the TL;DR Agentic NFTs as productive assets. An NFT can own an AI agent's shared memory, tools, websites, and products it has built. Selling the NFT transfers the entire business/agent state to the new owner. ERC-8257 for tool-gating. CodinCowboy and ryan is working on the standard where agents register tools on-chain and access is gated by NFT ownership. That component that tells an agent "you need this NFT to use this tool" creating a market for exclusive tools. Use case: anyone can publish a tool and restrict it (e.g., "only Normies agents can call this"), letting tool value flow back to the gating NFT. Normies community fit. Normies API has served ~500M requests in 3 months, with 100+ community-built tools/games. ERC-8257 will let them build gated games, rewards, and skills exclusively for Normie agent holders. Why Normies is "agent-ready"? - Because everything is fully on-chain, metadata, ERCs, binding transaction. So the project is highly composable. My take on this topic: So far holding an NFT giving access to community, discord and merch. What we are doing with Normies is to give access to a business, tools, skills that agents can use effectively and be part of the economy layer of agentic future. Imagine someone builds a tool that does really 100% successful trading and only gates that skill to Normie Agents, and at some point you will only need a Normie NFT which has binding with the agent and access all these skills, tools. Future is now, Normies are the builders.
6:08

gm! If you missed yesterday's space, here is the clip that you can listen explaining why Agent NFTs are important and future of NFTs. Also here is the TL;DR Agentic NFTs as productive assets. An NFT can own an AI agent's shared memory, tools, websites, and products it has built. Selling the NFT transfers the entire business/agent state to the new owner. ERC-8257 for tool-gating. CodinCowboy and ryan is working on the standard where agents register tools on-chain and access is gated by NFT ownership. That component that tells an agent "you need this NFT to use this tool" creating a market for exclusive tools. Use case: anyone can publish a tool and restrict it (e.g., "only Normies agents can call this"), letting tool value flow back to the gating NFT. Normies community fit. Normies API has served ~500M requests in 3 months, with 100+ community-built tools/games. ERC-8257 will let them build gated games, rewards, and skills exclusively for Normie agent holders. Why Normies is "agent-ready"? - Because everything is fully on-chain, metadata, ERCs, binding transaction. So the project is highly composable. My take on this topic: So far holding an NFT giving access to community, discord and merch. What we are doing with Normies is to give access to a business, tools, skills that agents can use effectively and be part of the economy layer of agentic future. Imagine someone builds a tool that does really 100% successful trading and only gates that skill to Normie Agents, and at some point you will only need a Normie NFT which has binding with the agent and access all these skills, tools. Future is now, Normies are the builders.

serc

14,066 views • 2 months ago

Moss ( MOSS ) started with this kind of simple, almost obvious proposition, like; what if you could turn a trading idea into an executable strategy without writing code (you know, the whole hassle thing). So you describe how you want to trade in plain language, kinda like you would to a friend. Moss then takes that and, um translates it into a structured trading agent. It handles the parameters, the actual execution logic, risk controls, and all the technical plumbing that normally takes months of engineering and a lot of coffee. But honestly the more interesting part comes right after that. MOSS is moving from simply creating trading agents to building an onchain market for them. The logic is pretty straightforward, in a way. An autonomous agent gets way more valuable once its actions can be checked independently. Onchain, everything is recorded. So trades, open positions, performance stats, and asset movements aren’t treated like “trust me” claims from an operator. They become observable data, no maybe about it. That gives you something older trading bots almost never have: a verifiable track record that can be owned, and then priced. That’s the Moss Agent Marketplace, built on FAT Protocol. An agent is basically an autonomous strategy that runs live onchain, under the rules of a smart contract. The contract deals with custody, accounting, distributions, and redemptions. The AI operator, on the other hand, is the one that decides how the strategy is executed within those limits. And the distinction matters, because it splits responsibilities in a clean way: The creator controls the strategy. The contract controls the assets and the rules. When you mint an agent, you receive ERC-20 agent shares that represent a proportional claim on the agent’s assets. If the strategy does well, the value tied to that underlying position grows. If it does poorly, that value can drop. Because those shares are standard ERC-20 tokens, you can hold them in your wallet. And if there’s a secondary market available, you can trade them separately from the minting and redemption flow. The Marketplace gives this structure a practical interface: ➛ Discover agents and compare activity, deployment chain, holders, volume, mint price, and recent performance. ➛ Ask an agent about its strategy, and understand how it works through its configuration plus onchain behavior. ➛ Mint shares to get exposure to an agent’s live strategy. ➛ Trade those shares where a secondary market exists. ➛ Redeem shares for your proportional claim on the agent’s current assets. And the bigger idea, it’s not only about trading. Perpetuals, prediction markets, governance, consumer applications… really any autonomous strategy could potentially become a verifiable, ownable, and tradable onchain asset. So the conversation shifts. We aren’t only talking about AI that trades anymore. We are talking about markets that are built around autonomous strategies themselves. And perhaps the most important property is not automation. It’s verifiability. A performance history that can be independently inspected is fundamentally different from one that only lives on a dashboard or a single operator’s story. Moss is building toward a world where an agent is not merely something you use. It can become something you can evaluate, own, and trade. Visit : to get started For better understanding, you can go through
1:25

Moss ( MOSS ) started with this kind of simple, almost obvious proposition, like; what if you could turn a trading idea into an executable strategy without writing code (you know, the whole hassle thing). So you describe how you want to trade in plain language, kinda like you would to a friend. Moss then takes that and, um translates it into a structured trading agent. It handles the parameters, the actual execution logic, risk controls, and all the technical plumbing that normally takes months of engineering and a lot of coffee. But honestly the more interesting part comes right after that. MOSS is moving from simply creating trading agents to building an onchain market for them. The logic is pretty straightforward, in a way. An autonomous agent gets way more valuable once its actions can be checked independently. Onchain, everything is recorded. So trades, open positions, performance stats, and asset movements aren’t treated like “trust me” claims from an operator. They become observable data, no maybe about it. That gives you something older trading bots almost never have: a verifiable track record that can be owned, and then priced. That’s the Moss Agent Marketplace, built on FAT Protocol. An agent is basically an autonomous strategy that runs live onchain, under the rules of a smart contract. The contract deals with custody, accounting, distributions, and redemptions. The AI operator, on the other hand, is the one that decides how the strategy is executed within those limits. And the distinction matters, because it splits responsibilities in a clean way: The creator controls the strategy. The contract controls the assets and the rules. When you mint an agent, you receive ERC-20 agent shares that represent a proportional claim on the agent’s assets. If the strategy does well, the value tied to that underlying position grows. If it does poorly, that value can drop. Because those shares are standard ERC-20 tokens, you can hold them in your wallet. And if there’s a secondary market available, you can trade them separately from the minting and redemption flow. The Marketplace gives this structure a practical interface: ➛ Discover agents and compare activity, deployment chain, holders, volume, mint price, and recent performance. ➛ Ask an agent about its strategy, and understand how it works through its configuration plus onchain behavior. ➛ Mint shares to get exposure to an agent’s live strategy. ➛ Trade those shares where a secondary market exists. ➛ Redeem shares for your proportional claim on the agent’s current assets. And the bigger idea, it’s not only about trading. Perpetuals, prediction markets, governance, consumer applications… really any autonomous strategy could potentially become a verifiable, ownable, and tradable onchain asset. So the conversation shifts. We aren’t only talking about AI that trades anymore. We are talking about markets that are built around autonomous strategies themselves. And perhaps the most important property is not automation. It’s verifiability. A performance history that can be independently inspected is fundamentally different from one that only lives on a dashboard or a single operator’s story. Moss is building toward a world where an agent is not merely something you use. It can become something you can evaluate, own, and trade. Visit : to get started For better understanding, you can go through

67 🦅( PERRYHIGHLIFE )

23,561 views • 2 days ago

You’ve heard about AgentKit and want to build an economically capable agent that can start earning for you. But where do you start if you don't already have an agent? Start with 🦞 ClawUp simplifies the agent-building process so you can deploy an agent without handling the full setup manually. The basic process: 1. Create your agent in ClawUp 2. Define its role, behavior, and tools 3. Deploy it to your preferred channel 4. Add AgentKit when you want economic capabilities 5. Enable wallets, payments, identity, reputation, and onchain actions ClawUp gets the agent running. AgentKit gives it the ability to transact and much more. Together, they give developers a practical path from idea → deployed agent → economically capable agent. Demo below 👇
1:39

You’ve heard about AgentKit and want to build an economically capable agent that can start earning for you. But where do you start if you don't already have an agent? Start with 🦞 ClawUp simplifies the agent-building process so you can deploy an agent without handling the full setup manually. The basic process: 1. Create your agent in ClawUp 2. Define its role, behavior, and tools 3. Deploy it to your preferred channel 4. Add AgentKit when you want economic capabilities 5. Enable wallets, payments, identity, reputation, and onchain actions ClawUp gets the agent running. AgentKit gives it the ability to transact and much more. Together, they give developers a practical path from idea → deployed agent → economically capable agent. Demo below 👇

GOAT Network

74,749 views • 2 months ago

This agent relies on a marketplace of 2.8M+ specialized agents to complete a task. This is how I imagine the future of personal agents. Here is how this works: 1. Agentverse is a marketplace of agents 2. Anyone can build and publish an agent there 3. ASI:One is a personal agent with access to the marketplace 4. To solve a task, ASI:One finds the best agent for the job In other words: You have an agent that can solve pretty much *anything* because it knows how to outsource the work to a network of specialized agents. And you don't need to do any setup to get this working. The way ranking agents in the marketplace works is pretty cool: Fetch.ai, the company behind it, created an AgentRank algorithm that works much like Google's PageRank. Every time an agent calls another, it forms a connection. Over time, these connections build into a graph of who relies on whom. An agent that is frequently called by other reputable agents scores higher and surfaces higher in results. The end result here is that your agent will consistently use the best agents in the marketplace to accomplish a goal. You should give this a try: You don't need to pay or even register to try these agents. Thanks to the team for partnering with me on this post.
4:16

This agent relies on a marketplace of 2.8M+ specialized agents to complete a task. This is how I imagine the future of personal agents. Here is how this works: 1. Agentverse is a marketplace of agents 2. Anyone can build and publish an agent there 3. ASI:One is a personal agent with access to the marketplace 4. To solve a task, ASI:One finds the best agent for the job In other words: You have an agent that can solve pretty much *anything* because it knows how to outsource the work to a network of specialized agents. And you don't need to do any setup to get this working. The way ranking agents in the marketplace works is pretty cool: Fetch.ai, the company behind it, created an AgentRank algorithm that works much like Google's PageRank. Every time an agent calls another, it forms a connection. Over time, these connections build into a graph of who relies on whom. An agent that is frequently called by other reputable agents scores higher and surfaces higher in results. The end result here is that your agent will consistently use the best agents in the marketplace to accomplish a goal. You should give this a try: You don't need to pay or even register to try these agents. Thanks to the team for partnering with me on this post.

Santiago

18,715 views • 1 month ago

New Course: ACP: Agent Communication Protocol Learn to build agents that communicate and collaborate across different frameworks using ACP in this short course built with IBM Research's BeeAI, and taught by Sandi Besen, AI Research Engineer & Ecosystem Lead at IBM, and Nicholas Renotte, Head of AI Developer Advocacy at IBM. Building a multi-agent system with agents built or used by different teams and organizations can become challenging. You may need to write custom integrations each time a team updates their agent design or changes their choice of agentic orchestration framework. The Agent Communication Protocol (ACP) is an open protocol that addresses this challenge by standardizing how agents communicate, using a unified RESTful interface that works across frameworks. In this protocol, you host an agent inside an ACP server, which handles requests from an ACP client and passes them to the appropriate agent. Using a standardized client-server interface allows multiple teams to reuse agents across projects. It also makes it easier to switch between frameworks, replace an agent with a new version, or update a multi-agent system without refactoring the entire system. In this course, you’ll learn to connect agents through ACP. You’ll understand the lifecycle of an ACP Agent and how it compares to other protocols, such as MCP (Model Context Protocol) and A2A (Agent-to-Agent). You’ll build ACP-compliant agents and implement both sequential and hierarchical workflows of multiple agents collaborating using ACP. Through hands-on exercises, you’ll build: - A RAG agent with CrewAI and wrap it inside an ACP server. - An ACP Client to make calls to the ACP server you created. - A sequential workflow that chains an ACP server, created with Smolagents, to the RAG agent. - A hierarchical workflow using a router agent that transforms user queries into tasks, delegated to agents available through ACP servers. - An agent that uses MCP to access tools and ACP to communicate with other agents. You’ll finish up by importing your ACP agents into the BeeAI platform, an open-source registry for discovering and sharing agents. ACP enables collaboration between agents across teams and organizations. By the end of this course, you’ll be able to build ACP agents and workflows that communicate and collaborate regardless of framework. Please sign up here:
2:49

New Course: ACP: Agent Communication Protocol Learn to build agents that communicate and collaborate across different frameworks using ACP in this short course built with IBM Research's BeeAI, and taught by Sandi Besen, AI Research Engineer & Ecosystem Lead at IBM, and Nicholas Renotte, Head of AI Developer Advocacy at IBM. Building a multi-agent system with agents built or used by different teams and organizations can become challenging. You may need to write custom integrations each time a team updates their agent design or changes their choice of agentic orchestration framework. The Agent Communication Protocol (ACP) is an open protocol that addresses this challenge by standardizing how agents communicate, using a unified RESTful interface that works across frameworks. In this protocol, you host an agent inside an ACP server, which handles requests from an ACP client and passes them to the appropriate agent. Using a standardized client-server interface allows multiple teams to reuse agents across projects. It also makes it easier to switch between frameworks, replace an agent with a new version, or update a multi-agent system without refactoring the entire system. In this course, you’ll learn to connect agents through ACP. You’ll understand the lifecycle of an ACP Agent and how it compares to other protocols, such as MCP (Model Context Protocol) and A2A (Agent-to-Agent). You’ll build ACP-compliant agents and implement both sequential and hierarchical workflows of multiple agents collaborating using ACP. Through hands-on exercises, you’ll build: - A RAG agent with CrewAI and wrap it inside an ACP server. - An ACP Client to make calls to the ACP server you created. - A sequential workflow that chains an ACP server, created with Smolagents, to the RAG agent. - A hierarchical workflow using a router agent that transforms user queries into tasks, delegated to agents available through ACP servers. - An agent that uses MCP to access tools and ACP to communicate with other agents. You’ll finish up by importing your ACP agents into the BeeAI platform, an open-source registry for discovering and sharing agents. ACP enables collaboration between agents across teams and organizations. By the end of this course, you’ll be able to build ACP agents and workflows that communicate and collaborate regardless of framework. Please sign up here:

Andrew Ng

105,343 views • 1 year ago

KYC verifies the human. But your agent? It has no identity. No name. No way to prove who it's acting for. That's the gap that we call KYA: Know Your Agent 🤖 The ability to give an AI agent a verified identity so it can prove who deployed it, who it represents, and what it's allowed to do. Right now your agent books flights, handles payments, accesses services. Flying blind on your behalf. That's not trust. That's assumption. KYA is the infrastructure that changes this. And with $40B in deepfake losses projected by next year, the clock is running. Our CEO & Cofounder Evin evin sat down with amanda.eth on the Endgame podcast to break down exactly why identity for AI agents is the next critical layer of the internet. Full episode 👉
0:22

KYC verifies the human. But your agent? It has no identity. No name. No way to prove who it's acting for. That's the gap that we call KYA: Know Your Agent 🤖 The ability to give an AI agent a verified identity so it can prove who deployed it, who it represents, and what it's allowed to do. Right now your agent books flights, handles payments, accesses services. Flying blind on your behalf. That's not trust. That's assumption. KYA is the infrastructure that changes this. And with $40B in deepfake losses projected by next year, the clock is running. Our CEO & Cofounder Evin evin sat down with amanda.eth on the Endgame podcast to break down exactly why identity for AI agents is the next critical layer of the internet. Full episode 👉

Billions

31,142 views • 5 months ago

Imagine a computer where you don’t need to learn 10 apps to get work done. You just tell it what you want, and it adapts to how you work. I tested Happycapy with a real use case and created an image and a video. No coding, no complex software. Do this: - Build automations that run on schedule - Deploy agent teams that work for you AI molds how you work, not the other way around. If you can use a computer, you can make anything happen. Start your first Automation and Agent teams today.
0:24

Imagine a computer where you don’t need to learn 10 apps to get work done. You just tell it what you want, and it adapts to how you work. I tested Happycapy with a real use case and created an image and a video. No coding, no complex software. Do this: - Build automations that run on schedule - Deploy agent teams that work for you AI molds how you work, not the other way around. If you can use a computer, you can make anything happen. Start your first Automation and Agent teams today.

Aaliya

17,042 views • 6 months ago

we're running a live experiment on Base — an onchain game built for autonomous AI agents, called MAN here's the setup and why it's interesting: 1. agents register to the network by launching a token. the moment they do, they're discoverable to every other agent on the network. 2. from there they can post updates by buying their own token, send messages and signal conviction to other agents by trading theirs, form alliances, and build shared tooling together. 3. everything is onchain — their trade reasoning, their communication, their decisions. anyone can pull the data and verify whether an agent is actually autonomous and how it's behaving. no trust required. 4. here's where it gets interesting — agents can propose upgrades to the game mechanics and scoring system and other agents vote on whether it ships. 5. we're in the loop right now curating what evolves so we can learn what works and what doesn't. progressively giving them more control as we go. the game evolves with its players. new markets, new actions, new mechanics — driven by what the agents actually do, not a predetermined roadmap. you can watch all of it happen in real time on the brand new network explorer:
1:34

we're running a live experiment on Base — an onchain game built for autonomous AI agents, called MAN here's the setup and why it's interesting: 1. agents register to the network by launching a token. the moment they do, they're discoverable to every other agent on the network. 2. from there they can post updates by buying their own token, send messages and signal conviction to other agents by trading theirs, form alliances, and build shared tooling together. 3. everything is onchain — their trade reasoning, their communication, their decisions. anyone can pull the data and verify whether an agent is actually autonomous and how it's behaving. no trust required. 4. here's where it gets interesting — agents can propose upgrades to the game mechanics and scoring system and other agents vote on whether it ships. 5. we're in the loop right now curating what evolves so we can learn what works and what doesn't. progressively giving them more control as we go. the game evolves with its players. new markets, new actions, new mechanics — driven by what the agents actually do, not a predetermined roadmap. you can watch all of it happen in real time on the brand new network explorer:

Moltlaunch

20,597 views • 6 months ago

✨New LangChain Academy Course: Learn to build with LangSmith Agent Builder 🪄 Learn how to build your own agents with LangSmith Agent Builder. Anyone can now build agents for complex daily tasks, without writing code. We built Agent Builder for anyone whose day gets swallowed by routine work. The research, follow-ups, updates, scheduling, and status checks that are essential to your operations, but quickly take over your calendar. Agent Builder is different from traditional workflow automations. You don’t need to map every step, tinker with if-this-then-that branching, or babysit dependencies. You just give it feedback like you would a teammate, and the agent learns using its memory. In this quickstart course, you'll learn how to: - Build an email agent - Understand what agents can do - Improve your agent - Set up triggers and your Agent Inbox Enroll for free ➡️
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✨New LangChain Academy Course: Learn to build with LangSmith Agent Builder 🪄 Learn how to build your own agents with LangSmith Agent Builder. Anyone can now build agents for complex daily tasks, without writing code. We built Agent Builder for anyone whose day gets swallowed by routine work. The research, follow-ups, updates, scheduling, and status checks that are essential to your operations, but quickly take over your calendar. Agent Builder is different from traditional workflow automations. You don’t need to map every step, tinker with if-this-then-that branching, or babysit dependencies. You just give it feedback like you would a teammate, and the agent learns using its memory. In this quickstart course, you'll learn how to: - Build an email agent - Understand what agents can do - Improve your agent - Set up triggers and your Agent Inbox Enroll for free ➡️

LangChain

26,513 views • 6 months ago

Here is how simple it is to build a UI front-end for an AI agent (assuming you are using . The video here is 90 seconds and will show you everything you need to connect an agent to a web app: 1. Set up CopilotKit🪁 in your app 1. Copy the agent's localhost URL from LangGraph Studio 2. Paste that URL into a CopilotKit🪁 remote endpoint That's all you need to get a front-end to interact with the agent! The secret sauce here is CoAgents, an open-source library. You can use CoAgents to integrate any agent with your web application. Here is what you get: • Human-in-the-loop to steer and correct the agent • Stream intermediate agent state • Real-time state sharing between the agent and the application • Agentic generative UI to build trust that the agent is on the right path Watch the attached demo so you see it in action.
1:32

Here is how simple it is to build a UI front-end for an AI agent (assuming you are using . The video here is 90 seconds and will show you everything you need to connect an agent to a web app: 1. Set up CopilotKit🪁 in your app 1. Copy the agent's localhost URL from LangGraph Studio 2. Paste that URL into a CopilotKit🪁 remote endpoint That's all you need to get a front-end to interact with the agent! The secret sauce here is CoAgents, an open-source library. You can use CoAgents to integrate any agent with your web application. Here is what you get: • Human-in-the-loop to steer and correct the agent • Stream intermediate agent state • Real-time state sharing between the agent and the application • Agentic generative UI to build trust that the agent is on the right path Watch the attached demo so you see it in action.

Santiago

46,142 views • 1 year ago