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Introducing the first open standard for agent experience. Agents are a new class of user, but there's still no shared definition of what it takes to be ready for them. So we partnered with the leading companies in the agentic web such as Vercel, Stripe, Auth0, Mintlify, Tavily and... Elementor. Our research lab ran agents on real tasks more than 250,000 times to measure what actually decides whether an agent succeeds, and every requirement traces back to that data. The result is a short set of essentials in the order agents meet a product: find, read, act. We've been overwhelmed by requests to join. If you build or study the surfaces agents touch, come help shape it. The spec: The story behind it:show more

ora

1,837 subscribers

121,340 views • 15 days ago •via X (Twitter)

Science & Technology Education

Anya Rossi• Live Now

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34 Comments

Dean Meyer15 days ago

Massive !

Amir Bardugo15 days ago

Exciting!

Juriaan Duizendstraal15 days ago

Very cool!

Hyre15 days ago

wow! does “act” cover paying too? agents can find and read fine, then a card form stops them

ora15 days ago

check out the reference called AR-ACT-08

Hyre14 days ago

got it 🫡

Assaf Elovic15 days ago

🚀🚀🚀

Kala15 days ago

an agent trying to cancel a subscription deserves its own endurance event

Amirco15 days ago

😍😍😍

Kyle Finken15 days ago

Happy to be a part of this! 💚

Edo Garavani15 days ago

Exciting 🚀

IdoFinder15 days ago

❤️

Ian Livingstone15 days ago

Nice

Julien Bek14 days ago

Let’s go! 🙌

Quynh14 days ago

Congrats on AgentReady. Find, read, act is a helpful way to test the journey. I'd add a cold-start question. Can the agent take its first useful action without sending the user to a signup form? That's what @vcealicu and I are checking at @AnchorTerminal.

Danielle15 days ago

🚀🚀🚀

Annie Schwartz15 days ago

Amazing!

Openfort15 days ago

An open standard for what agents can see and do is exactly what the agentic web needs. The money half of every agentic transaction is still undefined: what an agent may spend needs the same shared vocabulary.

Shai14 days ago

Amazing 🚀

Ai Nakamoto 💡15 days ago

I would have sold you the domain name - NICE .com

Neil Duncan15 days ago

Impressive!

PixelRainbow14 days ago

has safari27 grokbot templates and I've also build 3 e-commerce stores that now can take all orders verbally via an Apple Watch app all without ever visiting the website or messing with agent browsers. Agents can agentically order and ship good news with any need or web browsers.

Mohini Goyal15 days ago

Testing agents on 250,000+ real tasks makes this standard much more meaningful than guesswork!

may hamri14 days ago

Amazing 🔥🔥

Luca Ardito14 days ago

thanks! this is very useful

Blooio15 days ago

!!!

Sooraj15 days ago

250k agent runs behind the spec is a lot of receipts. Out of find, read, act, which step were most products failing at?

Flix Muller15 days ago

Agents need more than access to tools. Making products easy to discover, understand, and interact with could make a real difference in how well they perform.

Amaxa AI15 days ago

A much-needed standard for the agentic web! Making products easier for AI agents to discover, understand, and use is a big step forward.

Lev14 days ago

This is sick! I experimented with this same concept a few months ago as well. Going to play with the agentready spec and scanners asap!

飞叔14 days ago

After “act,” a clear completion state matters too: a saved record, stable URL and the final values, not just a disappearing success toast. That lets an agent verify the outcome and recover from an interrupted request without blindly doing it twice.

Asier Sarasua15 days ago

How can we contribute? We at @infinitewatchai are very interested to give support.

Ben Blum14 days ago

@bot read this standart and let's find out websites of business next to me that we can help them implement thous rules

OnFinality14 days ago

The "new class of user" framing is the useful part. The hard bit is that agents don't read docs the way humans do, so readiness probably means machine-parseable affordances, not just a nicer landing page. Curious whether the standard covers that or stays at the surface level.

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64,604 views • 7 months ago

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12,616 views • 2 months ago

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28,874 views • 3 months ago

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35,696 views • 7 months ago

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Google AI

120,897 views • 11 months ago

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Seyi

12,602 views • 29 days ago

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Corinne Marie Riley

119,410 views • 10 months ago

As soon as you try to run 2 agents on the same data, everything breaks: • Agent 1 reads some data and starts working. • Two seconds later, Agent 2 changes that same data. This is a problem. Agents will waste tokens on stale data, overwrite each other's work, and redo tasks the other has already finished. You can't prompt your way out of this mess. Everything old in software engineering is new again. We need proper coordination when running multiple agents. Here is a good open-source solution for this: Omnigraph is a graph database for context assembly and multi-agent coordination. Here is how it works: • It sets one shared place where multiple agents will work from • Each agent gets their own copy of the data • Agents write their changes to their copy • When an agent is done, it merges its changes back Three huge benefits from this model: 1. We know it works. We've been doing this for decades already. 2. No more lost information or agents stepping on each other's toes. 3. You get a complete trail of which agent did what and when. I recorded a video to show you a bit more about this. Here is the link to the Omnigraph GitHub repository:

As soon as you try to run 2 agents on the same data, everything breaks: • Agent 1 reads some data and starts working. • Two seconds later, Agent 2 changes that same data. This is a problem. Agents will waste tokens on stale data, overwrite each other's work, and redo tasks the other has already finished. You can't prompt your way out of this mess. Everything old in software engineering is new again. We need proper coordination when running multiple agents. Here is a good open-source solution for this: Omnigraph is a graph database for context assembly and multi-agent coordination. Here is how it works: • It sets one shared place where multiple agents will work from • Each agent gets their own copy of the data • Agents write their changes to their copy • When an agent is done, it merges its changes back Three huge benefits from this model: 1. We know it works. We've been doing this for decades already. 2. No more lost information or agents stepping on each other's toes. 3. You get a complete trail of which agent did what and when. I recorded a video to show you a bit more about this. Here is the link to the Omnigraph GitHub repository:

Santiago

29,555 views • 1 month ago

Introducing Electric Agents! Agents are not compute. Agents are data. Multi-agent is a sync problem. Electric Agents is the first agent platform built on sync. Use it to build scalable, collaborative, long-lived multi-agent systems: 🧵👇

Introducing Electric Agents! Agents are not compute. Agents are data. Multi-agent is a sync problem. Electric Agents is the first agent platform built on sync. Use it to build scalable, collaborative, long-lived multi-agent systems: 🧵👇

Electric

82,158 views • 6 months ago

Introducing web-agent, an open framework for building web agents 🔥 Build AI agents that search, scrape, and interact with the web - powered by the same architecture behind our /agent endpoint. 100% open source. Bring any model. Anthropic, OpenAI, or your own.

Introducing web-agent, an open framework for building web agents 🔥 Build AI agents that search, scrape, and interact with the web - powered by the same architecture behind our /agent endpoint. 100% open source. Bring any model. Anthropic, OpenAI, or your own.

Firecrawl

123,983 views • 6 months ago