Tag:

Agents

DAAPrivacyRightIcon
Gaming Gear

Google is also removing apps used to report sightings of ICE agents

by admin


Following Apple’s removal of ICEBlock from the App Store, an app used to report on the activity of Immigration and Customs Enforcement agents, 404 Media reports that Google is also removing similar apps from the Play Store. In a statement to Engadget, Google said “ICEBlock was never available on Google Play, but we removed similar apps for violations of our policies.”

Google says that it decided to remove apps that shared the location of a vulnerable group following a violent act that involved the group and a similar collection of apps. It suggests the apps were also removed because they didn’t appropriately moderate user-generated content. To be offered in the Play Store, apps with user-generated content have to clearly define what is or isn’t objectionable content in their terms of service, and make sure those terms line up with Google’s definitions of inappropriate content for Google Play.

404 Media report specifically focuses on Red Dot, an app that both Google and Apple removed. Like ICEBlock, Red Dot designed to let users report on ICE activity in their neighborhood. Rather than just rely on user submissions, the app’s website says that it “aggregates verified reports from multiple trusted sources” and then combines those sources to determine where to mark activity on a map of your area. “Red Dot never tracks ICE agents, law enforcement, or any person’s movements” and the app’s developers “categorically reject harassment, interference, or harm toward ICE agents or anyone else.” Despite those claims, the app is not currently available to download from the Play Store or the App Store.

The pushback against ICE tracking apps seemed to begin in earnest following a shooting at a Dallas ICE facility that injured two detainees and killed another on September 24. According to an FBI agent that spoke to The New York Times, the shooter “had been following apps that track the location of ICE agents” in the days leading up to the event.

Apple pulled the ICEBlock app from the App Store yesterday following a request from US Attorney General Pam Bondi. In a statement shared with Fox Business, Bondi said that “ICEBlock is designed to put ICE agents at risk just for doing their jobs, and violence against law enforcement is an intolerable red line that cannot be crossed.” Apple’s response was to remove the app. “Based on information we’ve received from law enforcement about the safety risks associated with ICEBlock, we have removed it and similar apps from the App Store,” Apple told the publication.

Google says it didn’t receive a similar request to remove apps from the Play Store. Instead, the company appears to be acting proactively. The test for either platform going forward, though, is if there’s a way that developers can offer these apps without them being removed again.



Source link

0 comments
0 FacebookTwitterPinterestEmail
Why AI Agents Could Be the Next Big thing in DeFi
Crypto Trends

Why AI Agents Could Be the Next Big thing in DeFi

by admin


Trusted Editorial content, reviewed by leading industry experts and seasoned editors. Ad Disclosure

By design, new technologies come in waves that reinforce each other. Mobile, social, and cloud reshaped the last era. The next era looks like AI, crypto, and agents – where “architecture is destiny,” and user intent becomes the primary interface

AI is Penetrating Web3, and its happening Fast

As per DappRadar over last 18 months, AI has moved from novelty to substrate in crypto: LLMs summarize governance, agents rebalance portfolios, and bots execute on-chain strategies in real time. Investors are voting with capital: by June 26, 2025, AI-agent projects had raised $1.39B year-to-date, already outpacing 2024’s run-rate.

Chris Dixon frames the macro well: AI and crypto are complementary. Blockchains supply ownership, credible commitments, and identity, primitive AI systems lack but desperately need if we want open markets for compute, data, and content. In his words, “AI needs blockchain-enabled computing.” – a16z crypto

Zooming out, even AI’s industrial impact supports this agentic shift. NVIDIA’s Jensen Huang points to AI as the start of “a new industrial revolution,” which implies new user layers and automation patterns in finance, too – Nasdaq

From Apps to Agents: The Backend Abstracts Away

The emerging end-state is simple to describe and hard to build: **you state an intent; an autonomous agent composes the stack-**data, liquidity, risk checks, settlement-then executes. Research on agentic systems and “the Agentic Web” sketches this world where agents pay other agents for data and services, coordinate via smart contracts, and transact without human babysitting. IKANGAI Developer tooling is catching up: frameworks like elizaOS show how to wire LLM agents to wallets and DeFi actions (“transfer” and “swap” from natural language), hinting at a future where the app is an agent orchestrator.

The Data Problem: Web3 Is Still Fragmented

Agents thrive on reliable, low-latency data. Web3, however, is splintered by chains, schemas, and sources. Indexing posts and vendor docs converge on the same point: raw chain data is time-ordered and scattered; meaningful queries require specialized indexing, subgraphs, replication, and ETL pipelines – often repeated per chain.

Providers like Goldsky and The Graph help, but even they highlight the need for cross-chain mirroring, real-time streaming, and composable subgraphs to serve complex apps-exactly what agents will demand continuously. Independent analyses echo the cost of fragmentation for DeFi risk and UX.

Takeaway: if the UI becomes an intent box, the heavy lifting moves to a programmable data layer that normalizes on-chain/off-chain context, exposes deterministic APIs to agents, and supports low-latency computation (alerts, scoring, routing) across chains.

Why AI Agents Are a Natural Fit for DeFi

DeFi is machine-native: transparent ledgers, programmable liquidity, and composable contracts. That makes it a perfect playground for autonomous agents to:

Trade and rebalance via structured prompts (“sell long-tail assets into ETH if volatility exceeds X”).

Scan risks (contract anomalies, oracle drift) continuously and price them into execution.

Arbitrage and MM across AMMs/CEXs without UI friction.

Govern (draft proposals, simulate outcomes) using on-chain and forum data.

Academic work surveying autonomous AI agents in DeFi forecasts exactly these roles, linking agent decision-making to market microstructure and governance design. Buterin similarly suggests the most viable role is **AI “as a player” in crypto games**, which maps cleanly to markets.

The Emerging Landscape: Chat-Based DeFi Platforms

Below are six chat-based or agent-first products that illustrate the spectrum, from consumer bots to intent-centric execution.

HeyElsa : AI crypto co-pilot with natural-language/voice, aiming to route, bridge, swap, lend across chains with MPC-secured wallets and safety rails. Think “type the task, Elsa handles the stack.”
Projected USP: unified chat/voice control plus custody model (MPC) for mainstream UX.

Kuvi.ai : Brands itself as Agentic Finance; “Don’t trade, just hoot.” Text-to-trade execution across DeFi, positioning agents as solvers that connect user intent to settlement.
Projected USP: end-to-end intent pipeline and cross-domain ambition (finance, identity, gaming).

Igris.bot : Focused on destination-based swaps: you specify what outcome you want (“end with 2 ETH on Base”), and the system determines the portfolio source, route, and fees between chains.
Projected USP: Centered on destination rather than source-reducing user decision load and tapping latent portfolio liquidity.

Defi App : Explicit intent-based swaps via solver/relayers; routes across multiple aggregators/DEXs; full docs.
Projected USP: Native intent-based execution (solver model): Users specify outcomes; off-chain solvers/relayers compete to route across multiple liquidity sources.

AskGina.ai : AI wallet companion that can analyze holdings and execute on-chain transactions from chat; lives as a web app/Farcaster mini-app.
Projected USP: AI wallet companion (analysis → action): chat interface that understands your portfolio and surfaces tailored insights

What the Agentic User Layer Requires Infra

If agents are the new UI, infra must be refactored for machines:

Programmable Data Layer: cross-chain ingestion → normalized schemas → real-time replication/mirroring → deterministic APIs consumable by agents.

Latency-aware Compute: triggers for price/volatility/MEV risk, agent policy evaluation, and pre-trade checks.

Identity & Permissions: wallet-bound permissions, cryptographic attestations (“proof of personhood/humanity”), and policy guards around agent autonomy: concepts Dixon directly connects to blockchain’s strengths.

Safety Rails: Vitalik’s cautions:restricted APIs, circuit breakers (“kill switches”), and alignment layers:need to be first-class.

Why This is Important (and Why Now)

The intent-centric pattern is catching on: users type goals; agents handles the plumbing. The status quo-click across bridges, DEXs, and dashboards – can’t scale by the next 100M users. Architecturally, the fix isn’t just a better front end; it’s open rails for ownership and programmable data so that many agents-not just a few closed super-apps:can compete on user value.

when big waves arrive, they “complement each other and work together.” AI brings creativity and automation; crypto offers open ownership and incentives; new devices (from phones to wallets to wearables) conclude distribution-together forming a user stack that reads like agents by default.

Closing Thought

If “read-write-own” was the last era, the next one introduces “act”: software that acts on the user’s behalf. In DeFi, that means agents that understand your intent, price risk, and settle across broken markets-safely and instantaneously. Winners won’t simply provide nifty chat UIs; they’ll think architecture as destiny and invest on programmable data and incentive layers that let agents thrive at scale

Editorial Process for bitcoinist is centered on delivering thoroughly researched, accurate, and unbiased content. We uphold strict sourcing standards, and each page undergoes diligent review by our team of top technology experts and seasoned editors. This process ensures the integrity, relevance, and value of our content for our readers.



Source link

0 comments
0 FacebookTwitterPinterestEmail
AI agents are science fiction not yet ready for primetime
Gaming Gear

AI agents are science fiction not yet ready for primetime

by admin


This is The Stepback, a weekly newsletter breaking down one essential story from the tech world. For more on all things AI, follow Hayden Field. The Stepback arrives in our subscribers’ inboxes at 8AM ET. Opt in for The Stepback here.

It all started with J.A.R.V.I.S. Yes, that J.A.R.V.I.S. The one from the Marvel movies.

Well, maybe it didn’t start with Iron Man’s AI assistant, but the fictional system definitely helped the concept of an AI agent along. Whenever I’ve interviewed AI industry folks about agentic AI, they often point to J.A.R.V.I.S. as an example of the ideal AI tool in many ways — one that knows what you need done before you even ask, can analyze and find insights in large swaths of data, and can offer strategic advice or run point on certain aspects of your business. People sometimes disagree on the exact definition of an AI agent, but at its core, it’s a step beyond chatbots in that it’s a system that can perform multistep, complex tasks on your behalf without constantly needing back-and-forth communication with you. It essentially makes its own to-do list of subtasks it needs to complete in order to get to your preferred end goal. That fantasy is closer to being a reality in many ways, but when it comes to actual usefulness for the everyday user, there are a lot of things that don’t work — and maybe will never work.

The term “AI agent” has been around for a long time, but it especially started trending in the tech industry in 2023. That was the year of the concept of AI agents; the term was on everyone’s lips as people tried to suss out the idea and how to make it a reality, but you didn’t see many successful use cases. The next year, 2024, was the year of deployment — people were really putting the code out into the field and seeing what it could do. (The answer, at the time, was… not much. And filled with a bunch of error messages.)

I can pinpoint the hype around AI agents becoming widespread to one specific announcement: In February 2024, Klarna, a fintech company, said that after one month, its AI assistant (powered by OpenAI’s tech) had successfully done the work of 700 full-time customer service agents and automated two-thirds of the company’s customer service chats. For months, those statistics came up in almost every AI industry conversation I had.

The hype never died down, and in the following months, every Big Tech CEO seemed to harp on the term in every earnings call. Executives at Amazon, Meta, Google, Microsoft, and a whole host of other companies began to talk about their commitment to building useful and successful AI agents — and tried to put their money where their mouths are to make it happen.

The vision was that one day, an AI agent could do everything from book your travel to generate visuals for your business presentations. The ideal tool could even, say, find a good time and place to hang out with a bunch of your friends that works with all of your calendars, food preferences, and dietary restrictions — and then book the dinner reservation and create a calendar event for everyone.

Now let’s talk about the “AI coding” of it all: For years, AI coding has been carrying the agentic AI industry. If you asked anyone about real-life, successful, not-annoying use cases for AI agents happening right now and not conceptually in a not-too-distant future, they’d point to AI coding — and that was pretty much the only concrete thing they could point to. Many engineers use AI agents for coding, and they’re seen as objectively pretty good. Good enough, in fact, that at Microsoft and Google, up to 30 percent of the code is now being written by AI agents. And for startups like OpenAI and Anthropic, which burn through cash at high rates, one of their biggest revenue generators is AI coding tools for enterprise clients.

So until recently, AI coding has been the main real-life use case of AI agents, but obviously, that’s not pandering to the everyday consumer. The vision, remember, was always a jack-of-all-trades sort of AI agent for the “everyman.” And we’re not quite there yet — but in 2025, we’ve gotten closer than we’ve ever been before.

Last October, Anthropic kicked things off by introducing “Computer Use,” a tool that allowed Claude to use a computer like a human might — browsing, searching, accessing different platforms, and completing complex tasks on a user’s behalf. The general consensus was that the tool was a step forward for technology, but reviews said that in practice, it left a lot to be desired. Fast-forward to January 2025, and OpenAI released Operator, its version of the same thing, and billed it as a tool for filling out forms, ordering groceries, booking travel, and creating memes. Once again, in practice, many users agreed that the tool was buggy, slow, and not always efficient. But again, it was a significant step. The next month, OpenAI released Deep Research, an agentic AI tool that could compile long research reports on any topic for a user, and that spun things forward, too. Some people said the research reports were more impressive in length than content, but others were seriously impressed. And then in July, OpenAI combined Deep Research and Operator into one AI agent product: ChatGPT Agent. Was it better than most consumer-facing agentic AI tools that came before? Absolutely. Was it still tough to make work successfully in practice? Absolutely.

So there’s a long way to go to reach that vision of an ideal AI agent, but at the same time, we’re technically closer than we’ve ever been before. That’s why tech companies are putting more and more money into agentic AI, by way of investing in additional compute, research and development, or talent. Google recently hired Windsurf’s CEO, cofounder, and some R&D team members, specifically to help Google push its AI agent projects forward. And companies like Anthropic and OpenAI are racing each other up the ladder, rung by rung, to introduce incremental features to put these agents in the hands of consumers. (Anthropic, for instance, just announced a Chrome extension for Claude that allows it to work in your browser.)

So really, what happens next is that we’ll see AI coding continue to improve (and, unfortunately, potentially replace the jobs of many entry-level software engineers). We’ll also see the consumer-facing agent products improve, likely slowly but surely. And we’ll see agents used increasingly for enterprise and government applications, especially since Anthropic, OpenAI, and xAI have all debuted government-specific AI platforms in recent months.

Overall, expect to see more false starts, starts and stops, and mergers and acquisitions as the AI agent competition picks up (and the hype bubble continues to balloon). One question we’ll all have to ask ourselves as the months go on: What do we actually want a conceptual “AI agent” to be able to do for us? Do we want them to replace just the logistics or also the more personal, human aspects of life (i.e., helping write a wedding toast or a note for a flower delivery)? And how good are they at helping with the logistics vs. the personal stuff? (Answer for that last one: not very good at the moment.)

  • Besides the astronomical environmental cost of AI — especially for large models, which are the ones powering AI agent efforts — there’s an elephant in the room. And that’s the idea that “smarter AI that can do anything for you” isn’t always good, especially when people want to use it to do… bad things. Things like creating chemical, biological, radiological, and nuclear (CBRN) weapons. Top AI companies say they’re increasingly worried about the risks of that. (Of course, they’re not worried enough to stop building.)
  • Let’s talk about the regulation of it all. A lot of people have fears about the implications of AI, but many aren’t fully aware of the potential dangers posed by uber-helpful, aiming-to-please AI agents in the hands of bad actors, both stateside and abroad (think: “vibe-hacking,” romance scams, and more). AI companies say they’re ahead of the risk with the voluntary safeguards they’ve implemented. But many others say this may be a case for an external gut-check.

23 CommentsFollow topics and authors from this story to see more like this in your personalized homepage feed and to receive email updates.

  • Close

    Hayden Field

    Posts from this author will be added to your daily email digest and your homepage feed.

    PlusFollow

    See All by Hayden Field

  • AIClose

    AI

    Posts from this topic will be added to your daily email digest and your homepage feed.

    PlusFollow

    See All AI

  • ColumnClose

    Column

    Posts from this topic will be added to your daily email digest and your homepage feed.

    PlusFollow

    See All Column

  • TechClose

    Tech

    Posts from this topic will be added to your daily email digest and your homepage feed.

    PlusFollow

    See All Tech

  • The StepbackClose

    The Stepback

    Posts from this topic will be added to your daily email digest and your homepage feed.

    PlusFollow

    See All The Stepback



Source link

0 comments
0 FacebookTwitterPinterestEmail
Ensemble integrates XMTP to bring AI Agents to decentralised messaging
Crypto Trends

Ensemble integrates XMTP to bring AI Agents to decentralised messaging

by admin



Ensemble, a decentralized artificial intelligence agent platform, has integrated with enterprise-grade messaging protocol XMTP to power the next level of adoption of AI agents in decentralized messaging.  

Summary

  • AI agents platform Ensemble has integrated with messaging protocol XMTP.
  • The integration will see Ensemble bring chat-native experiences to users.
  • XMTP powers web3 protocols such as Base, Lens and Farcaster.

The integration allows Ensemble to expand its Agent Hub marketplace, with users now able to tap into support for XMTP for a chat-native agent economy.

With XMTP powering nearly 1 million identities and more than 63 million activated wallets, the collaboration means Ensemble users can pay for autonomous agents either directly from their wallets or across any of the applications built on XMTP, without switching platforms.

“AI agents are rapidly becoming an integral part of how people work and interact online, but until now they’ve been locked into siloed apps and platforms. With Ensemble building on XMTP, they’re enabling a chat-native agent economy that works anywhere the XMTP network reaches, across wallets, social apps, and the XMTP protocols. It’s a big step toward a world where secure, decentralized messaging connects people, AI, and money without friction or lock-in,” said Peter Denton of XMTP.

What does this mean for Ensemble?

Ensemble will benefit from XMTP’s infrastructure, which helps turn complex workflows into simple, user-friendly, chat-native experiences. The integration brings the same messaging capabilities for decentralized protocols that are already in use across leading web3 apps, including Base, Farcaster, and Lens.

Ensemble’s march in the AI agents space includes a decentralized finance alert with tiered subscriptions that users can pay for directly in chat. The protocol is also looking at a DeFi analyst agent expected to bring features such as on-demand analysis, quotes and pay per query to users. 

Users will be able to request and receive results within the same XMTP thread.

The platform also eyes AI agents for far more complex workflows, such as queries on optimizing yield farming.

“Our mission is to bring AI agents to the masses by making them as trustworthy,accessible and useful as possible,” says Shamir Ozery, co-founder and chief executive officer of Ensemble.

Ensemble and XMTP team up as the latter looks to capitalize on a $20 million funding round to accelerate growth and adoption of its web3 messaging infrastructure stack.



Source link

0 comments
0 FacebookTwitterPinterestEmail
Do Large Language Models Dream of AI Agents?
Gaming Gear

Do Large Language Models Dream of AI Agents?

by admin


During sleep, the human brain sorts through different memories, consolidating important ones while discarding those that don’t matter. What if AI could do the same?

Bilt, a company that offers local shopping and restaurant deals to renters, recently deployed several million agents with the hopes of doing just that.

Bilt uses technology from a startup called Letta that allows agents to learn from previous conversations and share memories with one another. Using a process called “sleeptime compute,” the agents decide what information to store in its long-term memory vault and what might be needed for faster recall.

“We can make a single update to a [memory] block and have the behavior of hundreds of thousands of agents change,” says Andrew Fitz, an AI engineer at Bilt. “This is useful in any scenario where you want fine-grained control over agents’ context,” he adds, referring to the text prompt fed to the model at inference time.

Large language models can typically only “recall” things if information is included in the context window. If you want a chatbot to remember your most recent conversation, you need to paste it into the chat.

Most AI systems can only handle a limited amount of information in the context window before their ability to use the data falters and they hallucinate or become confused. The human brain, by contrast, is able to file away useful information and recall it later.

“Your brain is continuously improving, adding more information like a sponge,” says Charles Packer, Letta’s CEO. “With language models, it’s like the exact opposite. You run these language models in a loop for long enough and the context becomes poisoned; they get derailed and you just want to reset.”

Packer and his cofounder Sarah Wooders previously developed MemGPT, an open-source project that aimed to help LLMs decide what information should be stored in short-term vs. long-term memory. With Letta, the duo has expanded their approach to let agents learn in the background.

Bilt’s collaboration with Letta is part of a broader push to give AI the ability to store and recall useful information, which could make chatbots smarter and agents less error-prone. Memory remains underdeveloped in modern AI, which undermines the intelligence and reliability of AI tools, according to experts I spoke to.

Harrison Chase, cofounder and CEO of LangChain, another company that has developed a method for improving memory in AI agents, says he sees memory as a vital part of context engineering—wherein a user or engineer decides what information to feed into the context window. LangChain offers companies several different kinds of memory storage for agents, from long-term facts about users to memories of recent experiences. “Memory, I would argue, is a form of context,” Chase says. “A big portion of an AI engineer’s job is basically getting the model the right context [information].”

Consumer AI tools are gradually becoming less forgetful, too. This February, OpenAI announced that ChatGPT will store relevant information in order to provide a more personalized experience for users—although the company did not disclose how this works.

Letta and LangChain make the process of recall more transparent to engineers building AI systems.

“I think it’s super important not only for the models to be open but also for the memory systems to be open,” says Clem Delangue, CEO of the AI hosting platform Hugging Face and an investor in Letta.

Intriguingly, Letta’s CEO Packer hints that it might also be important for AI models to learn what to forget. “If a user says, ‘that one project we were working on, wipe it out from your memory’ then the agent should be able to go back and retroactively rewrite every single memory.”

The notion of artificial memories and dreams makes me think of Do Androids Dream of Electric Sheep? by Philip K. Dick, a mind-bending novel that inspired the stylishly dystopian movie Blade Runner. Large language models aren’t yet as impressive as the rebellious replicants of the story, but their memories, it seems, can be just as fragile.

This is an edition of Will Knight’s AI Lab newsletter. Read previous newsletters here.



Source link

0 comments
0 FacebookTwitterPinterestEmail
A screenshot of Microsoft's Copilot Gaming technology demo
Product Reviews

87% of game developers are already using AI agents and over a third use AI for creative elements like level design and dialogue according to a new Google survey

by admin



Fully 87% of game developers are already using AI agents. That’s according to a new survey from Google Cloud and The Harris Poll of 615 game developers in the United States, South Korea, Norway, Finland, and Sweden. It’s also just the tip of the AI-berg.

Some of the tasks completed by AI aren’t immediately worrisome and you’d think will speed up development and reduce costs. The report says AI is proving useful for automating “cumbersome and repetitive tasks”, freeing developers to focus more on creative elements.

For instance, 47% of developers reported that AI is, “speeding up playtesting and balancing of mechanics, 45% say it is assisting in localization and translation of game content, and 44% cite it for improving code generation and scripting support.” Overall, 94% of developers surveyed, “expect AI to reduce overall development costs in the long term (3+ years).”


Related articles

That could help keep smaller developers in business, it might mean niche game titles are more viable, and so on. But it’s only part of the picture. Arguably one of the great fears among gamers is that game design, stories and dialogue will be replaced with the sort of AI slop that’s now bunging up YouTube and social media.

Well, slop or not, AI is increasingly being used for those purposes. Google’s survey found that 36% of respondents are using AI for dynamic level design, animation and rigging, and dialogue writing, while 37% of developers report they have, “enhanced experimentation with new gameplay or narrative concepts.”

Will today’s games be among the last to be coded, written and voiced by humans? (Image credit: rmk1234, CD Projekt Red)

The report is pretty granular about many aspects of game design and development and makes for an intriguing read. Overall, Google is nothing if not upbeat about the implications of all this. Of course it would be, considering it is one of the largest AI researchers on the planet. It has skin in the game, and it’s trying to sell AI to the world.

“Overall, the research found widespread adoption of gen AI in the games industry—and a surprising level of optimism for it. AI is already making a big difference in developer workflows, including productivity and creative tasks.

“Developers also see promising possibilities with AI agents and other emerging AI tools to accelerate game development and enhance player experiences,” the report says.

Of course, the end game, pun very much intended, of all this is presumably games fully AI generated in response to user prompts. “I want to play a first person shooter set in ancient Rome, but with modern weapons, procedural crime elements and Disney characters,” or whatever. And off you go.

Of course, except the one bit that almost definitely won’t be doable is the Disney characters due to IP ownership. Unless you pay extra for the Disney AI gaming subscription or similar. But you get the idea.

If that puts the burden on users to come up with game narratives, semi-curated games where the basic premise is tweaked by user prompts might make more sense for most mainstream gamers. But the main point is that it might all be AI generated one day. At which point will there be a submarket for “artisanal” hand-coded games with human-written narratives, real voices and the rest? All of this is to come, much is to be decided. But the the direction of travel looks pretty unambiguous, and a little icky.

Best gaming PC 2025

All our current recommendations



Source link

0 comments
0 FacebookTwitterPinterestEmail
Ethereum Community Buzzed With Trustless Agents (Erc-8004) Discussion
GameFi Guides

Ethereum Community Buzzed with Trustless Agents (ERC-8004) Discussion

by admin



The Ethereum community is currently abuzz with the emergence and discussions around a new standard ERC-8004, dubbed Trustless Agents. This newly unveiled development promises to revolutionize the interaction of autonomous AI agents in a trustless environment like Ethereum Network. 

Introduced by Davide Crapis, the Trustless Agents standard builds on the Agent-to-Agent (A2A) protocol and has sparked a flurry of discussions within Ethereum developers, researchers, and blockchain enthusiasts weighing in on its potential to shape a decentralized AI economy.

“This standard extends the Agent‑to‑Agent (A2A) protocol with a trust layer that allows participants to discover, choose, and interact with agents across organizational boundaries without pre‑existing trust,” Crapis stated in a latest Ethereum Magicians post,” adding, “It introduces three lightweight, on‑chain registries—Identity, Reputation, and Validation—and leaves application‑specific logic to off‑chain components.”

Crapis said that while this proposed ERC goes for public discussion, his team will work closely with the Linux Foundation and A2A ecosystem stakeholders to make it efficient and improve its specifications. 

The proposal positions Ethereum as a critical substrate for AI, not for running models directly but for providing a secure, tamper-proof ledger that no single corporation or government can alter. This vision contrasts sharply with reliance on centralized platforms like Google APIs or proprietary databases, a concern amplified by growing distrust in corporate data control. 

What Exactly are ERC-8004 and Trustless Agents

From the technical perspective, ERC-8004 is currently a proposed smart-contract standard on Ethereum. It can simply be understood with the approach of NFT smart contracts, which follows ERC-721 Ethereum standard. For simple tokens, ERC-20 is the most commonly used standard. 

The proposed ERC-8004 standard extends the A2A protocol—an open infrastructure developed by Google in collaboration with 50+ partners—by introducing a trust layer that enables AI agents to discover, choose, and interact across organizational boundaries. 

It leverages three lightweight on-chain registries: Identity, Reputation, and Validation. These registries provide a verifiable anchor for agent identity, an immutable record of behavior, and proofs of action, respectively, while leaving application-specific logic to off-chain components. 

Scope for the Trustless Agents

Explaining the importance of Trustless Agents, an anonymous member of the Ethereum Foundation, Binji stated that ERC-8004 could serve as the backbone of a “machine economy,” where millions of autonomous agents operate. They can transact, negotiate, and form coalitions—potentially even decentralized autonomous organizations (DAOs).

ethereum is SERIOUSLY gearing up for ai. (erc-8004) by @DavideCrapis just dropped, it’s called “trustless agents” and here’s what you need to know:

but first:

you can think of ethereum as an important substrate for ai, not necessarily because it can run all the models, but… pic.twitter.com/cxP8OR1CTB

— binji (@binji_x) August 18, 2025

“This is a practical ERC that can be used and iterated on in the wild; the specifics can stay offchain, but the skeleton of trust lives on ethereum,” Binji wrote. 

While the demand for AI infrastructure is rising, Trustless Agents could gain significant traction. It sits on the intersection of both the emerging sectors—decentralized technologies and artificial intelligence—which are rising trends across a number of innovations. 

The Road Ahead 

As ERC-8004 undergoes public discussion, developers are working closely with the Linux Foundation—which is hosting the A2A protocol—and A2A team to refine the specification. With Ethereum Layer 2 solutions addressing scalability hurdles, the standard could lay the foundation for a sci-fi future where AI agents operate autonomously on a decentralized trust layer. 

If passed for implementation, ERC-8004 could reshape Ethereum’s role as a decentralized infrastructure provider. For now, the Ethereum community remains cautiously optimistic, with many viewing ERC-8004 as a critical step toward a decentralized AI economy.

Also Read: Ethereum Maxis Distressed as Validator Exit Queue Hits Record 910k ETH





Source link

0 comments
0 FacebookTwitterPinterestEmail
Decrypt logo
NFT Gaming

AI Agents Are Taking Over Game Development: Google

by admin



In brief

  • While AI speeds up coding and playtesting, devs worry about privacy, cost, and creative control.
  • Small studios see AI as a chance to compete, while larger publishers struggle to adapt.
  • From smarter NPCs to new jobs, developers say AI is remaking game development.

Nearly nine in 10 game developers say they’ve already built AI agents into their work, according to a new Google Cloud survey. These autonomous programs don’t just generate images and assets; they are inside the game, reacting to players and reshaping virtual worlds.

The survey, conducted in collaboration with The Harris Poll, polled 615 developers across the United States, South Korea, Finland, Norway, and Sweden. It found that 97% of respondents believe that AI agents—autonomous programs that can act without human input—are already reshaping the industry, with most already using them to speed up coding, testing, and localization.

For smaller studios, AI is helping level the playing field, with 29% saying AI is lowering the barrier to entry and allowing them to compete with larger publishers.



“If you’re not on the AI bandwagon right now, you’re already behind,” Kelsey Falter, CEO and co-founder of indie studio Mother Games, told Decrypt. “Being small means we can adapt faster. Bigger studios have legacy codebases and senior engineers resistant to change. For us, AI is baked in from day one.”

In the study, 87% of developers said they’re using AI agents that adapt to players in real time. These agents are being deployed to control non-player characters, guide tutorials, and even moderate online communities. In 2023, Call of Duty publisher Activision rolled out ToxMod, an AI-powered tool that monitors online chat for toxic and hate speech.

Developers say players now expect more dynamic, responsive environments and richer, more reactive worlds, with 35% saying AI-driven tutorials are speeding up player onboarding.

Matias Rodriguez, chief technology officer at Globant, a tech firm that works with major game studios, said gamers are open to AI when it deepens storytelling or immersion—but wary if it feels like a shortcut.

“Gamers are a selective audience when it comes to authenticity,” Rodriguez told Decrypt. “But they’re also some of the most open to innovation when it enhances the immersion.”

AI, he said, is being used as “a creative copilot and a productivity multiplier,” aimed at enhancing—not replacing—the creative process.

Falter agreed that the tools can boost productivity, but said the lack of industry standards means mistakes happen quickly.

“It’s still the wild west,” she said. “A year ago, we saw AI generating soupy code at a faster pace than humans could check it. Without guardrails, you can make a mess faster than you can clean it up.”

Still, most developers are betting on AI’s long-term value. For Falter, the challenge is maintaining human creativity while using AI to unlock new types of gameplay.

“We don’t use AI to generate artwork or churn out clones,” she said. “Our models are trained on scripts written by human writers, and our terrain generators have a specific style unique to our game. It’s about maintaining creative integrity.”

GG Newsletter

Get the latest web3 gaming news, hear directly from gaming studios and influencers covering the space, and receive power-ups from our partners.



Source link

0 comments
0 FacebookTwitterPinterestEmail
Close