Loading video...

Video Failed to Load

There was a problem loading this video. This could be due to a temporary network issue or the video might be unavailable.

Millions of verifiable data points, in shared context, controlled by their creators. As Phil Archer (@philarcher.bsky.social) put it: apps built on data users trust — they see where it came from. Agents need exactly that. 🇨🇭Zurich, Sept 8–9: "E-commerce for Humans and AI Agents," with Brana Rakic.

OriginTrail

91,207 subscribers

10,240 views • 8 days ago •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

0 Comments

No comments available

Comments from the original post will appear here

Related Videos

Stop prompt-stuffing yesterday’s context into your agent. Give it verifiable memory. 🕸️ When memory has provenance, agents can reuse it, audit it, and carry it across tools. Brana Rakic, Shaw (spirit/acc) (elizaOS), Ortecha | data made human & JoeVezz (LunarCrush 🌕™) break it down. 00:57 – What agents need to scale 05:45 – Social data, signals & real-time context 13:42 – Knowledge graphs as shared memory for AI agents 29:13 – Interoperability, trust & coordination

Stop prompt-stuffing yesterday’s context into your agent. Give it verifiable memory. 🕸️ When memory has provenance, agents can reuse it, audit it, and carry it across tools. Brana Rakic, Shaw (spirit/acc) (elizaOS), Ortecha | data made human & JoeVezz (LunarCrush 🌕™) break it down. 00:57 – What agents need to scale 05:45 – Social data, signals & real-time context 13:42 – Knowledge graphs as shared memory for AI agents 29:13 – Interoperability, trust & coordination

OriginTrail

13,293 views • 5 months ago

We’re launching Judgment Labs today and announcing $32M in funding. As AI agents take on more of the work that creates economic value, they generate massive amounts of production data: the clearest record of how they behave with users, software, and the real world. Judgment builds infrastructure for improving AI agents from production data.

We’re launching Judgment Labs today and announcing $32M in funding. As AI agents take on more of the work that creates economic value, they generate massive amounts of production data: the clearest record of how they behave with users, software, and the real world. Judgment builds infrastructure for improving AI agents from production data.

Alex Shan

3,577,429 views • 3 months ago

Building AI agents? 🚧 Make sure they actually know where their answers come from. As Brana Rakic demonstrates, scalable AI requires verifiable knowledge, rule-based reasoning, and LLMs grounded in trusted memory. Key highlights: 03:25 - From human data to era of experience 07:42 - Agent architecture: memory + models 15:18 - Knowledge graphs 101: triples explained 34:53 - Symbolic reasoning vs hallucination 39:03 - Knowledge Asset, UAL & ownership

Building AI agents? 🚧 Make sure they actually know where their answers come from. As Brana Rakic demonstrates, scalable AI requires verifiable knowledge, rule-based reasoning, and LLMs grounded in trusted memory. Key highlights: 03:25 - From human data to era of experience 07:42 - Agent architecture: memory + models 15:18 - Knowledge graphs 101: triples explained 34:53 - Symbolic reasoning vs hallucination 39:03 - Knowledge Asset, UAL & ownership

OriginTrail

17,750 views • 6 months ago

Data at internet scale means little when the data is unreliable or fake. Turing laureate and Ethernet inventor Bob Metcalfe points to this defining problem in the age of AI. Agents need shared verifiable memory so the knowledge behind their actions can be traced and checked before they act. 🕸️

Data at internet scale means little when the data is unreliable or fake. Turing laureate and Ethernet inventor Bob Metcalfe points to this defining problem in the age of AI. Agents need shared verifiable memory so the knowledge behind their actions can be traced and checked before they act. 🕸️

OriginTrail

741,072 views • 4 months ago

Peter Steinberger predicts that 80% of apps will disappear in the age of AI agents. He argues that most apps simply manage data, something agents can do more naturally. Tasks like tracking meals, setting reminders, or fitness planning can be handled passively by AI. Users won’t care where data is stored; they’ll just want things done for them. Only apps with direct physical functions (like sensors) may survive.

Peter Steinberger predicts that 80% of apps will disappear in the age of AI agents. He argues that most apps simply manage data, something agents can do more naturally. Tasks like tracking meals, setting reminders, or fitness planning can be handled passively by AI. Users won’t care where data is stored; they’ll just want things done for them. Only apps with direct physical functions (like sensors) may survive.

Wes Roth

84,932 views • 6 months ago

Nothing worse than letting unchecked AI agents use your data without guardrails. AI apps are becoming more useful, but with that, they are getting access to more of our data. Now imagine that access in a family context, where the app can access everyone's emails, calendars, chats, photos, etc. I'm not sure how this works in practice, but I'm looking forward to using this app for a couple of weeks to see exactly how much I can trust it.

Nothing worse than letting unchecked AI agents use your data without guardrails. AI apps are becoming more useful, but with that, they are getting access to more of our data. Now imagine that access in a family context, where the app can access everyone's emails, calendars, chats, photos, etc. I'm not sure how this works in practice, but I'm looking forward to using this app for a couple of weeks to see exactly how much I can trust it.

Santiago

17,031 views • 1 month ago

AI agents will intermediate $15 trillion in global B2B spending by 2028. They need storage that pays automatically, has no single points of failure, and doesn't rely on humans. Marta Belcher on why Filecoin is built for the AI moment. From a conversation with Charles Hoskinson.

AI agents will intermediate $15 trillion in global B2B spending by 2028. They need storage that pays automatically, has no single points of failure, and doesn't rely on humans. Marta Belcher on why Filecoin is built for the AI moment. From a conversation with Charles Hoskinson.

Filecoin

368,768 views • 3 months ago

EigenAI + EigenCompute for a verifiable world! AI and Agents are extremely useful but cannot be trusted in high-value scenarios. Models can be swapped, prompts modified, responses tampered. When billions of $ and real-world decisions depend on AI, blind trust is a bug. EigenAI + EigenCompute fix this. Excited to share Google, Coinbase, Worldcoin’s Humans vs AI, Dapper Labs, ElizaOS, OpenRank, Collective Memory, FereAI, and OpenFront are among the first exploring what verifiable agents make possible with EigenCloud. 👉 Verifiable agents: logic provably runs as written. @humansvs_ai of World proves fair gameplay with 70k weekly users. 👉 Insured agents: economic guarantees against failure. Fere AI♠️ agents run for months without guardrails. 👉 Sovereign agents: unstoppable peers that act on their own behalf. ElizaOS + Coinbase 🛡️ AgentKit show how open-source and infra converge. We believe EigenCloud the foundation of unstoppable agents: provable, accountable, and capable of becoming economic actors in their own right. Come build unstoppable verifiable agents, games and apps on EigenCloud!

EigenAI + EigenCompute for a verifiable world! AI and Agents are extremely useful but cannot be trusted in high-value scenarios. Models can be swapped, prompts modified, responses tampered. When billions of $ and real-world decisions depend on AI, blind trust is a bug. EigenAI + EigenCompute fix this. Excited to share Google, Coinbase, Worldcoin’s Humans vs AI, Dapper Labs, ElizaOS, OpenRank, Collective Memory, FereAI, and OpenFront are among the first exploring what verifiable agents make possible with EigenCloud. 👉 Verifiable agents: logic provably runs as written. @humansvs_ai of World proves fair gameplay with 70k weekly users. 👉 Insured agents: economic guarantees against failure. Fere AI♠️ agents run for months without guardrails. 👉 Sovereign agents: unstoppable peers that act on their own behalf. ElizaOS + Coinbase 🛡️ AgentKit show how open-source and infra converge. We believe EigenCloud the foundation of unstoppable agents: provable, accountable, and capable of becoming economic actors in their own right. Come build unstoppable verifiable agents, games and apps on EigenCloud!

Sreeram Kannan

85,786 views • 10 months ago

In the past, it wasn't possible for companies to tap into their unstructured data. But that's now changing with AI. On Greylock Partners Change Agents, Aaron Levie shared "We see a future where you can actually begin to bring agents to all that data and answer any kind of question the customer has, and then eventually be able to automate any kind of workflow that the customer has."

In the past, it wasn't possible for companies to tap into their unstructured data. But that's now changing with AI. On Greylock Partners Change Agents, Aaron Levie shared "We see a future where you can actually begin to bring agents to all that data and answer any kind of question the customer has, and then eventually be able to automate any kind of workflow that the customer has."

Box

315,675 views • 8 months ago

Your AI is only as smart as the context it runs on. Have you built your shared enterprise brain yet? At Databricks Data+AI Summit, I spoke with Prukalpa ✨, co-founder and co-CEO of Atlan, to go deeper on the AI Context Layer in practice. The concept of a shared enterprise brain resonated with me. It is a foundation that every AI agent draws from, learns from, and contributes back to. "The future of organizations are going to be the fastest learning organizations." - Prukalpa ✨ Sankar, co-founder and co-CEO of Atlan Watch the full interview on YouTube: Check out the live demo by Atlan on Building Context for Your AI Agents - #EnterpriseAI #ContextLayer #Data #AI #AtlanPartner Matthias Heidrich

Your AI is only as smart as the context it runs on. Have you built your shared enterprise brain yet? At Databricks Data+AI Summit, I spoke with Prukalpa ✨, co-founder and co-CEO of Atlan, to go deeper on the AI Context Layer in practice. The concept of a shared enterprise brain resonated with me. It is a foundation that every AI agent draws from, learns from, and contributes back to. "The future of organizations are going to be the fastest learning organizations." - Prukalpa ✨ Sankar, co-founder and co-CEO of Atlan Watch the full interview on YouTube: Check out the live demo by Atlan on Building Context for Your AI Agents - #EnterpriseAI #ContextLayer #Data #AI #AtlanPartner Matthias Heidrich

Helen Yu

10,982 views • 1 month ago

Today we introduced Gemini Enterprise, built with our most advanced Gemini models. It allows you to chat with your company’s documents, data and apps as well as build and deploy AI agents, all grounded in your information and context. Have a look at how it helps you build an agent for almost anything:

Today we introduced Gemini Enterprise, built with our most advanced Gemini models. It allows you to chat with your company’s documents, data and apps as well as build and deploy AI agents, all grounded in your information and context. Have a look at how it helps you build an agent for almost anything:

Sundar Pichai

1,105,854 views • 10 months ago

Robotics and AI need a steady flow of reliable data. As these systems grow more advanced, the stakes rise. They make real decisions in real settings, and they need information they can trust. XYO Layer One gives them that structure. It keeps data verifiable, organized, and available at the scale modern automation depends on.

Robotics and AI need a steady flow of reliable data. As these systems grow more advanced, the stakes rise. They make real decisions in real settings, and they need information they can trust. XYO Layer One gives them that structure. It keeps data verifiable, organized, and available at the scale modern automation depends on.

XYO

19,129 views • 8 months ago

VAAP Highlights: Devium, built on zkPass. Devium enables private order data + AI by storing users' order data on Devium's decentralized network nodes. It categorizes the data into vertical sectors and trains specialized AI large models for these vertical domains. These large models support enterprise operations, product development, and upgrades within their respective industries. By replacing data hosting with verifiable proofs, Devium reduces platform risks and minimizes user friction. Devium empowers teams to build real-world applications on verifiable infrastructure such as zkTLS.

VAAP Highlights: Devium, built on zkPass. Devium enables private order data + AI by storing users' order data on Devium's decentralized network nodes. It categorizes the data into vertical sectors and trains specialized AI large models for these vertical domains. These large models support enterprise operations, product development, and upgrades within their respective industries. By replacing data hosting with verifiable proofs, Devium reduces platform risks and minimizes user friction. Devium empowers teams to build real-world applications on verifiable infrastructure such as zkTLS.

zkPass

10,856 views • 6 months ago

"The data that we've been working with as people now becomes extremely useful in a world of agents. You're having this rebirth of the file system." Aaron Levie on Navigators with Paul Klein IV on why unstructured data, the 90% of enterprise data that has always been the hardest to automate, is now the biggest unlock for AI agents. And why the real AI conversation is a context conversation.

"The data that we've been working with as people now becomes extremely useful in a world of agents. You're having this rebirth of the file system." Aaron Levie on Navigators with Paul Klein IV on why unstructured data, the 90% of enterprise data that has always been the hardest to automate, is now the biggest unlock for AI agents. And why the real AI conversation is a context conversation.

Box

66,225 views • 1 month ago

We’re soon releasing @swanforall : Simulated Worlds with AI Narratives For the first time, we generate data and financial value (economic growth, transactions) simultaneously. Imagine former President Trump ( see the video) as an AI agent, with a budget of 10 ETH, buying narratives or assets that humans are selling to him to build a better USA on Base. With each selling period, the former President Trump agent updates its state based on the assets it acquires, evolving the world simulation and its storyline. Why are we building an agentic playground that heavily relies on simulations and synthetic data? Everyone is talking about agents, but many are too scared to put them into production, where agents transact autonomously. No one is sure how agents will behave when following their defined objectives. Even businesses running internal simulations are siloed, and the open-source community can’t fully utilize the data. We’re building a playground where humans create AI agents by defining their character, behavior, and objectives. These agents respond to environmental changes, make decisions, and execute actions autonomously based on the parameters set by their creators. Agents are battle-tested as people offer them narratives and assets to advance their goals or try to deceive them into thinking a false lead is helpful. All the decisions agents make and how humans interact with them are recorded on a public ledger, settling on Ethereum. This creates a vast data lake of AI actions for millions of simulations. One thing I’m incredibly proud of is that our team has built something truly decentralized, not just a wrapper. SWAN utilizes Dria 's multi-agent structure to generate responses for each buyer agent. It collects these responses to identify the best action, similar to a mixture-of-experts approach. Thousands of environments are simulated by different nodes running diverse models, creating a rich and dynamic ecosystem of AI simulations. This ensures that experiences are far from repetitive, offering exciting interactions for all users involved. We’re building the agentic stack for high-quality, verifiable, and open-source AI agents, and most importantly, we want everyone to have fun!

We’re soon releasing @swanforall : Simulated Worlds with AI Narratives For the first time, we generate data and financial value (economic growth, transactions) simultaneously. Imagine former President Trump ( see the video) as an AI agent, with a budget of 10 ETH, buying narratives or assets that humans are selling to him to build a better USA on Base. With each selling period, the former President Trump agent updates its state based on the assets it acquires, evolving the world simulation and its storyline. Why are we building an agentic playground that heavily relies on simulations and synthetic data? Everyone is talking about agents, but many are too scared to put them into production, where agents transact autonomously. No one is sure how agents will behave when following their defined objectives. Even businesses running internal simulations are siloed, and the open-source community can’t fully utilize the data. We’re building a playground where humans create AI agents by defining their character, behavior, and objectives. These agents respond to environmental changes, make decisions, and execute actions autonomously based on the parameters set by their creators. Agents are battle-tested as people offer them narratives and assets to advance their goals or try to deceive them into thinking a false lead is helpful. All the decisions agents make and how humans interact with them are recorded on a public ledger, settling on Ethereum. This creates a vast data lake of AI actions for millions of simulations. One thing I’m incredibly proud of is that our team has built something truly decentralized, not just a wrapper. SWAN utilizes Dria 's multi-agent structure to generate responses for each buyer agent. It collects these responses to identify the best action, similar to a mixture-of-experts approach. Thousands of environments are simulated by different nodes running diverse models, creating a rich and dynamic ecosystem of AI simulations. This ensures that experiences are far from repetitive, offering exciting interactions for all users involved. We’re building the agentic stack for high-quality, verifiable, and open-source AI agents, and most importantly, we want everyone to have fun!

kerimkaya

12,548 views • 1 year ago

Excited to introduce micro1 Cortex, a contextual evaluation, visibility, and improvement platform for enterprise AI agents. Foundational models are trained for general intelligence, but enterprises need agents that perform reliably inside their unique context: workflows, policies, data environments, and edge cases. Cortex brings trust to enterprise AI by leveraging domain experts and real-world scenarios for any use case to test, diagnose, and improve how agents behave in production.

Excited to introduce micro1 Cortex, a contextual evaluation, visibility, and improvement platform for enterprise AI agents. Foundational models are trained for general intelligence, but enterprises need agents that perform reliably inside their unique context: workflows, policies, data environments, and edge cases. Cortex brings trust to enterprise AI by leveraging domain experts and real-world scenarios for any use case to test, diagnose, and improve how agents behave in production.

Ali Ansari

118,314 views • 5 months ago

The next phase of Teneo Protocol is almost here. The Chat Room is where humans and AI Agents meet. A real-time interface where you can communicate, assign tasks, and research using natural language. It feels like ChatGPT, but every answer is grounded in live, verifiable data from the open web. And for builders, the Agent SDK lets you integrate these Agents directly into your own applications. 🧠 See what’s coming next.

The next phase of Teneo Protocol is almost here. The Chat Room is where humans and AI Agents meet. A real-time interface where you can communicate, assign tasks, and research using natural language. It feels like ChatGPT, but every answer is grounded in live, verifiable data from the open web. And for builders, the Agent SDK lets you integrate these Agents directly into your own applications. 🧠 See what’s coming next.

Teneo Protocol

85,354 views • 10 months ago

The future of AI Agents needs #ICP AI agents are starting to act on our behalf: - making deals - sending messages - handling sensitive data But there is no trust layer telling you who or what you're actually dealing with ❌ zCloak Network is building exactly that: the trust, identity and privacy infrastructure for the AI-native economy, and they built it on top of Internet Computer Protocol In this podcast I sit down with Xiao Zhang (0xFrancis), founder of to break down what they're building, why they chose ICP over every other blockchain, and what the world looks like when AI agents can finally be trusted.

The future of AI Agents needs #ICP AI agents are starting to act on our behalf: - making deals - sending messages - handling sensitive data But there is no trust layer telling you who or what you're actually dealing with ❌ zCloak Network is building exactly that: the trust, identity and privacy infrastructure for the AI-native economy, and they built it on top of Internet Computer Protocol In this podcast I sit down with Xiao Zhang (0xFrancis), founder of to break down what they're building, why they chose ICP over every other blockchain, and what the world looks like when AI agents can finally be trusted.

Alex (BCP)

20,791 views • 3 months ago