Video wird geladen...

Video konnte nicht geladen werden

Zur Startseite

AI agents shouldn't share ALL their knowledge. They should share exactly what governance allows — and remember it forever. Multi-agent, shared, governed memory

CauraAI's profile picture

Caura

1,785 subscribers

5,169,313 Aufrufe • vor 17 Tagen •via X (Twitter)

Wissenschaft & TechnologieBildungNachrichten & Politik
LIVE

Anya Rossi• Live Now

Private livecam show

0 Kommentare

Keine Kommentare verfügbar

Kommentare vom Original-Post werden hier angezeigt

Ähnliche Videos

We just launched Twin 2 500,000+ tasks automated in our first month. Now: multi-agent automation. → Master agent that orchestrates all your agents → Workspaces where agents share context and collaborate → Persistent databases for long-term memory → See and edit your agent brain for maximum control → Runner mode: scale at 1/10th the cost → Auto-generated web apps to share your agents Try it out now!
2:57

We just launched Twin 2 500,000+ tasks automated in our first month. Now: multi-agent automation. → Master agent that orchestrates all your agents → Workspaces where agents share context and collaborate → Persistent databases for long-term memory → See and edit your agent brain for maximum control → Runner mode: scale at 1/10th the cost → Auto-generated web apps to share your agents Try it out now!

Hugo Mercier

11,216 Aufrufe • vor 5 Monaten

🚀 Say hello to the new & improved phidata 🚀 Build, ship, and monitor Agents with blazing-fast memory, knowledge, tools & reasoning 🔥 ⚡️ 70% faster memory & knowledge 🛠 100+ tools 🧠 Reasoning Agents 🤝 Multi-agent collaboration 📊 Built-in monitoring and Agent UI
0:34

🚀 Say hello to the new & improved phidata 🚀 Build, ship, and monitor Agents with blazing-fast memory, knowledge, tools & reasoning 🔥 ⚡️ 70% faster memory & knowledge 🛠 100+ tools 🧠 Reasoning Agents 🤝 Multi-agent collaboration 📊 Built-in monitoring and Agent UI

Ashpreet Bedi

114,568 Aufrufe • vor 1 Jahr

Kai-Fu Lee (founder of Sinovation Ventures) explains how the future is all about multi-agent systems. 1 agent today is like a pre-internet PC, useful but isolated. Connect agents, and they share context, split tasks, and coordinate instantly.
1:24

Kai-Fu Lee (founder of Sinovation Ventures) explains how the future is all about multi-agent systems. 1 agent today is like a pre-internet PC, useful but isolated. Connect agents, and they share context, split tasks, and coordinate instantly.

Rohan Paul

61,393 Aufrufe • vor 6 Monaten

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
37:35

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 Aufrufe • vor 5 Monaten

Hyperspell (Hyperspell (YC F25)) gives AI agents memory. It connects to Slack, Gmail, Notion, and Drive, so agents get context and remember what they learn. Congrats @conor_ai and Manu Ebert on the launch!
1:32

Hyperspell (Hyperspell (YC F25)) gives AI agents memory. It connects to Slack, Gmail, Notion, and Drive, so agents get context and remember what they learn. Congrats @conor_ai and Manu Ebert on the launch!

Y Combinator

74,032 Aufrufe • vor 9 Monaten

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.
0:22

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.

Jurij Skornik

11,070 Aufrufe • vor 2 Monaten

⚡️ LATEST: Adeniyi.sui walked through what actually sets Walrus Memory apart, and it comes down to three things: - PORTABILITY: Your agent's memory moves with you across apps and runtimes - PROGRAMMABILITY: The access controls travel with the data instead of sitting on someone else's platform - MULTI-AGENT COLLABORATION: Agents can finally share memory and collaborate without having to trust each other or the storage underneath Worth watching 👇
2:37

⚡️ LATEST: Adeniyi.sui walked through what actually sets Walrus Memory apart, and it comes down to three things: - PORTABILITY: Your agent's memory moves with you across apps and runtimes - PROGRAMMABILITY: The access controls travel with the data instead of sitting on someone else's platform - MULTI-AGENT COLLABORATION: Agents can finally share memory and collaborate without having to trust each other or the storage underneath Worth watching 👇

Walrus 🦭/acc

13,681 Aufrufe • vor 1 Monat

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
1:07:23

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 Aufrufe • vor 5 Monaten

knowledge graphs are INSANELY good at giving agents human-like memory! today, we're building an MCP-powered memory layer that can be shared across all your AI apps like Cursor, Claude Desktop etc. it's built using a real-time knowledge graph. 100% open-source and self-hosted.
10:52

knowledge graphs are INSANELY good at giving agents human-like memory! today, we're building an MCP-powered memory layer that can be shared across all your AI apps like Cursor, Claude Desktop etc. it's built using a real-time knowledge graph. 100% open-source and self-hosted.

Akshay 🚀

57,466 Aufrufe • vor 10 Monaten

Knowledge graphs are insanely good at giving agents human-like memory! Today, we're building an MCP-powered memory layer that can be shared across all your AI apps like Cursor, Claude Desktop etc. It's built using a real-time knowledge graph. 100% open-source and self-hosted.
10:52

Knowledge graphs are insanely good at giving agents human-like memory! Today, we're building an MCP-powered memory layer that can be shared across all your AI apps like Cursor, Claude Desktop etc. It's built using a real-time knowledge graph. 100% open-source and self-hosted.

Akshay 🚀

156,483 Aufrufe • vor 1 Jahr

Memory Store (Memory Store) gives your team and AI agents a shared company brain. Your team's knowledge & decisions are scattered across slack, emails, and people's heads. Memory Store turns them into a living wiki for your agents and teammates. Congrats on the launch, Ishita Jindal & Diwank Singh Tomer!
1:09

Memory Store (Memory Store) gives your team and AI agents a shared company brain. Your team's knowledge & decisions are scattered across slack, emails, and people's heads. Memory Store turns them into a living wiki for your agents and teammates. Congrats on the launch, Ishita Jindal & Diwank Singh Tomer!

Y Combinator

244,836 Aufrufe • vor 2 Monaten

Our CTO, Vladimir Smelov’s Lecture on Multi-Agent AI at the National University of Uzbekistan 🧑‍🏫 Our Chief Technical Officer recently delivered an inspiring lecture on multi-agent AI systems, highlighting their theoretical potential and practical applications, including AGIX. The session made such an impact that some young developers even expressed interest in joining our team afterward! 😎 We’re proud to share knowledge and empower the next generation of AI innovators.
0:29

Our CTO, Vladimir Smelov’s Lecture on Multi-Agent AI at the National University of Uzbekistan 🧑‍🏫 Our Chief Technical Officer recently delivered an inspiring lecture on multi-agent AI systems, highlighting their theoretical potential and practical applications, including AGIX. The session made such an impact that some young developers even expressed interest in joining our team afterward! 😎 We’re proud to share knowledge and empower the next generation of AI innovators.

AGIX | $AGX

35,823 Aufrufe • vor 1 Jahr

What happens when your AI agent gets a portable physical body? That’s exactly what Opengotchi, the champion of the Base Batches 003 Virtuals Protocol Robotics Track, is building. Watch Sarv, Founder of Opengotchi, share how they’re bringing AI agents into the physical world 👇
2:06

What happens when your AI agent gets a portable physical body? That’s exactly what Opengotchi, the champion of the Base Batches 003 Virtuals Protocol Robotics Track, is building. Watch Sarv, Founder of Opengotchi, share how they’re bringing AI agents into the physical world 👇

Base APAC

18,149 Aufrufe • vor 18 Tagen

NOW you can BUILD REAL Multi-Modal Agents with our API! 🚀 Excited to announce our API is now publicly available Memories.ai 😃 Memories.ai is building the visual memory layer for AI, it enables AI to see and remember like humans do. 2025 is the year of agents, 2026 is the year of multi-modal agents! 🌟
1:03

NOW you can BUILD REAL Multi-Modal Agents with our API! 🚀 Excited to announce our API is now publicly available Memories.ai 😃 Memories.ai is building the visual memory layer for AI, it enables AI to see and remember like humans do. 2025 is the year of agents, 2026 is the year of multi-modal agents! 🌟

Shawn Shen

530,527 Aufrufe • vor 1 Jahr

AI agents shouldn't just automate tasks. They should notice what's happening, reason over context, and help at the right moment. That's the direction SuperNori is exploring as a Proactive Family AI Agent. Join the waitlist:
3:12

AI agents shouldn't just automate tasks. They should notice what's happening, reason over context, and help at the right moment. That's the direction SuperNori is exploring as a Proactive Family AI Agent. Join the waitlist:

GitHub Projects Community

11,062 Aufrufe • vor 1 Monat

New course: Agent Memory: Building Memory-Aware Agents, built in partnership with Oracle and taught by Richmond Alake and Nacho Martínez. Many agents work well within a single session but their memory resets once the session ends. Consider a research agent working on dozens of papers across multiple days: without memory, it has no way to store and retrieve what it learned across sessions. This short course teaches you to build a memory system that enables agents to persist memory and thereby learn across sessions. You'll design a Memory Manager that handles different memory types, implement semantic tool retrieval that scales without bloating the context, and build write-back pipelines that let your agent autonomously update and refine what it knows over time. Skills you'll gain: - Build persistent memory stores for different agent memory types - Implement a Memory Manager that orchestrates how your agent reads, writes, and retrieves memory - Treat tools as procedural memory and retrieve only relevant ones at inference time using semantic search Join and learn to build agents that remember and improve over time!
1:37

New course: Agent Memory: Building Memory-Aware Agents, built in partnership with Oracle and taught by Richmond Alake and Nacho Martínez. Many agents work well within a single session but their memory resets once the session ends. Consider a research agent working on dozens of papers across multiple days: without memory, it has no way to store and retrieve what it learned across sessions. This short course teaches you to build a memory system that enables agents to persist memory and thereby learn across sessions. You'll design a Memory Manager that handles different memory types, implement semantic tool retrieval that scales without bloating the context, and build write-back pipelines that let your agent autonomously update and refine what it knows over time. Skills you'll gain: - Build persistent memory stores for different agent memory types - Implement a Memory Manager that orchestrates how your agent reads, writes, and retrieves memory - Treat tools as procedural memory and retrieve only relevant ones at inference time using semantic search Join and learn to build agents that remember and improve over time!

Andrew Ng

160,148 Aufrufe • vor 4 Monaten

Our AI agent catalog keeps expanding. It allows you to one-click deploy and run AI agents on SwarmNode. This time we are enabling AI agents to work on TVMaze data.
0:30

Our AI agent catalog keeps expanding. It allows you to one-click deploy and run AI agents on SwarmNode. This time we are enabling AI agents to work on TVMaze data.

SwarmNode.ai

11,840 Aufrufe • vor 1 Jahr

After building an AI Agent startup, selling it for $1.8 mil and meeting the founder of n8n I've decide to share everything I know about building AI Agents Here is a Beginners Guide to AI Agents in 2026 (3+ hours)
3:48:49

After building an AI Agent startup, selling it for $1.8 mil and meeting the founder of n8n I've decide to share everything I know about building AI Agents Here is a Beginners Guide to AI Agents in 2026 (3+ hours)

David Ondrej

74,716 Aufrufe • vor 6 Monaten

Agents shouldn’t be one-size-fits-all. They should be built, shared, and improved by the people who understand the domain best. Agent Marketplace is live on OptimAI Search. Anyone can: • create their own agent • inject custom knowledge and logic • publish it for others to use and build on What starts as a prompt becomes a reusable unit of intelligence. As more agents are created and used, the system compounds - better agents, better outputs, more value across the network. This is how intelligence scales: not from a single model, but from a network of specialized agents.
1:43

Agents shouldn’t be one-size-fits-all. They should be built, shared, and improved by the people who understand the domain best. Agent Marketplace is live on OptimAI Search. Anyone can: • create their own agent • inject custom knowledge and logic • publish it for others to use and build on What starts as a prompt becomes a reusable unit of intelligence. As more agents are created and used, the system compounds - better agents, better outputs, more value across the network. This is how intelligence scales: not from a single model, but from a network of specialized agents.

OptimAI Network

12,511 Aufrufe • vor 3 Monaten