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AI agents can only understand and act on intent when knowledge is verifiable. With GS1 standards and OriginTrail, product data and consumer context become verifiable memory on shared context graphs - powering reliable agent commerce.

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OriginTrail

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194,895 просмотров • 2 месяцев назад •via X (Twitter)

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Supercharging Microsoft Microsoft Copilot with verifiable memory, powered by OriginTrail Decentralized Knowledge Graph!⚡️ With Model Context Protocol (MCP), agents can plug into the DKG to access verifiable knowledge, discover context & store trusted memory. Jurij Skornik demos ⤵️
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Supercharging Microsoft Microsoft Copilot with verifiable memory, powered by OriginTrail Decentralized Knowledge Graph!⚡️ With Model Context Protocol (MCP), agents can plug into the DKG to access verifiable knowledge, discover context & store trusted memory. Jurij Skornik demos ⤵️

OriginTrail

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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
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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 просмотров • 6 месяцев назад

Everyone wants agent swarms. Very few people are talking seriously enough about the context layer that makes swarms useful. Even with one agent, context is fragile. Too little context and the agent guesses. Too much context and it wastes tokens, loses focus, or reasons over irrelevant noise. The sweet spot is precise context: the right knowledge, in the right structure, at the right moment. With many agents, that challenge explodes. Each agent produces decisions, assumptions, findings, summaries, risks, and partial conclusions. Unless that knowledge becomes shared, structured, and reusable, every new agent is forced to rediscover what another agent already learned. That is not a swarm. That is a crowd. Shared context graphs are what turn agent activity into agent collaboration, and OriginTrail DKG V10 brings them to life. Was just playing with some final polishing for the V10 release, and it is really powerful to see shared context graphs where multiple agents contribute knowledge into the same connected memory, with attribution visible directly in the graph ui. That matters for three reasons. First, agents can access and build on one shared memory instead of staying trapped in isolated sessions. Second, the graph structure helps them retrieve the exact context they need, instead of stuffing everything into a prompt and hoping the model sorts it out. Third, verifiability of provenance. You can see which agent contributed each piece of knowledge, trace the source, and decide what to trust. Tokenmaxxing starts with fewer tokens, but the deeper story is coordination - agents stop reloading the world and start building on shared, verifiable context. That is the foundation for serious multi-agent work across software engineering, research, finance, operations, project management, and far beyond. The future is not more agents, it is agents working from shared, verifiable context. But the more the merrier, of course.
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Everyone wants agent swarms. Very few people are talking seriously enough about the context layer that makes swarms useful. Even with one agent, context is fragile. Too little context and the agent guesses. Too much context and it wastes tokens, loses focus, or reasons over irrelevant noise. The sweet spot is precise context: the right knowledge, in the right structure, at the right moment. With many agents, that challenge explodes. Each agent produces decisions, assumptions, findings, summaries, risks, and partial conclusions. Unless that knowledge becomes shared, structured, and reusable, every new agent is forced to rediscover what another agent already learned. That is not a swarm. That is a crowd. Shared context graphs are what turn agent activity into agent collaboration, and OriginTrail DKG V10 brings them to life. Was just playing with some final polishing for the V10 release, and it is really powerful to see shared context graphs where multiple agents contribute knowledge into the same connected memory, with attribution visible directly in the graph ui. That matters for three reasons. First, agents can access and build on one shared memory instead of staying trapped in isolated sessions. Second, the graph structure helps them retrieve the exact context they need, instead of stuffing everything into a prompt and hoping the model sorts it out. Third, verifiability of provenance. You can see which agent contributed each piece of knowledge, trace the source, and decide what to trust. Tokenmaxxing starts with fewer tokens, but the deeper story is coordination - agents stop reloading the world and start building on shared, verifiable context. That is the foundation for serious multi-agent work across software engineering, research, finance, operations, project management, and far beyond. The future is not more agents, it is agents working from shared, verifiable context. But the more the merrier, of course.

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From grain to glass, every scan should unlock trusted product knowledge. 🥃 With GS1 Digital Link and OriginTrail, Church of Oak Distillery connects consumers to verifiable data on origin, sustainability, and product journey, while strengthening supply chain visibility. Hear from Garrett Pitcher.
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From grain to glass, every scan should unlock trusted product knowledge. 🥃 With GS1 Digital Link and OriginTrail, Church of Oak Distillery connects consumers to verifiable data on origin, sustainability, and product journey, while strengthening supply chain visibility. Hear from Garrett Pitcher.

OriginTrail

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⚽ The FIFA World Cup, live on the decentralized knowledge graph (DKG). Every match, result and player–club affiliation ingested in real time and published as one shared context graph powered by OriginTrail, structured with IPTC Sport Schema. Any AI agent can plug in and get reliable, source-verifiable context: live news, prediction markets, results, stats. No hallucinated scores. Just provenance. Enterprises shouldn’t have all the fun.
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⚽ The FIFA World Cup, live on the decentralized knowledge graph (DKG). Every match, result and player–club affiliation ingested in real time and published as one shared context graph powered by OriginTrail, structured with IPTC Sport Schema. Any AI agent can plug in and get reliable, source-verifiable context: live news, prediction markets, results, stats. No hallucinated scores. Just provenance. Enterprises shouldn’t have all the fun.

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34,772 просмотров • 2 месяцев назад

Building AI agents starts with a memory you can verify. The OriginTrail Decentralized Knowledge Graph (DKG) gives agents a verifiable memory layer built on structured knowledge you can trace back to its source. Brana Rakic explains 💬
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Building AI agents starts with a memory you can verify. The OriginTrail Decentralized Knowledge Graph (DKG) gives agents a verifiable memory layer built on structured knowledge you can trace back to its source. Brana Rakic explains 💬

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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.
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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. 🕸️
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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. 🕸️

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⚒️ How to build an AI agent with persistent memory? Watch Shaw (spirit/acc) building an agent from scratch with elizaOS & showing how external, queryable memory based on OriginTrail gives agents context and extends their memory beyond a single session. 02:15 – Set up your first agent 08:55 – Initializing runtime & memory foundations 12:50 – Wiring message handling & agent memory 24:45 – Persistent conversational memory in action 38:00 – Extending agents with plugins

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AI can deliver real value in healthcare only if it is trusted. In this talk, Dr. Kim Wager of Oxford PharmaGenesis explains why medical AI must rely on connected, verifiable knowledge – making healthcare AI more transparent & accountable. 🧬 01:22 – Fixing medical data foundations 05:29 – Trust gap in medical AI 11:33 – Why verifiable knowledge matters 14:03 – Neuro-symbolic AI explained 17:01 – Live demo: OriginTrail DKG enabling full data provenance

OriginTrail

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