Video wird geladen...

Video konnte nicht geladen werden

Beim Laden dieses Videos ist ein Problem aufgetreten. Dies könnte an einem vorübergehenden Netzwerkproblem liegen oder das Video ist möglicherweise nicht verfügbar.

Agent sessions work well for focused tasks, but most real projects are too broad and complex for a single context window to hold. The more an agent sees, the less reliable it becomes. We built Missions to fix this.

Factory

50,603 subscribers

45,282 Aufrufe • vor 2 Monaten •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

0 Kommentare

Keine Kommentare verfügbar

Kommentare vom Original-Post werden hier angezeigt

Ähnliche Videos

Last, but not least, don't sleep on this one: Le Chat now has Work mode (Preview) — a powerful agent for complex long-horizon tasks like research, analysis, and actions across your connected tools. Connectors are on by default so the agent pulls context from docs, email, and calendars as it works, and sessions persist across many turns until the task is complete.

Last, but not least, don't sleep on this one: Le Chat now has Work mode (Preview) — a powerful agent for complex long-horizon tasks like research, analysis, and actions across your connected tools. Connectors are on by default so the agent pulls context from docs, email, and calendars as it works, and sessions persist across many turns until the task is complete.

Mistral Vibe

32,521 Aufrufe • vor 2 Monaten

We open-sourced our agent skills ecosystem, built specifically for complex end-to-end browser tasks. Type in any domain, and researches an approach to reliably complete your task, contributing it back to the knowledge base for other agents. Watch our agent fetch Amazon listings in real-time, driven entirely by a custom browser skill:

We open-sourced our agent skills ecosystem, built specifically for complex end-to-end browser tasks. Type in any domain, and researches an approach to reliably complete your task, contributing it back to the knowledge base for other agents. Watch our agent fetch Amazon listings in real-time, driven entirely by a custom browser skill:

Shrey Pandya

10,312 Aufrufe • vor 1 Monat

Introducing Toggle for OpenClaw🦞 For the first time ever your Agent can see everything you do on the web. This is real-time context on your work day. Toggle AI analyses all browser actions, clicks, work, meetings and documents -> into AI context of what Projects / Tasks / Tools you are working on. Check it out👇

Introducing Toggle for OpenClaw🦞 For the first time ever your Agent can see everything you do on the web. This is real-time context on your work day. Toggle AI analyses all browser actions, clicks, work, meetings and documents -> into AI context of what Projects / Tasks / Tools you are working on. Check it out👇

PeLpA

52,282 Aufrufe • vor 4 Monaten

We're excited to announce Autotab 1.0. The first AI agent that you onboard, not integrate. Models have the intelligence required for most work—but they’re limited by context, tools, and coherence over long tasks. We’ve built Autotab to unhobble models on these bottlenecks 🧵

We're excited to announce Autotab 1.0. The first AI agent that you onboard, not integrate. Models have the intelligence required for most work—but they’re limited by context, tools, and coherence over long tasks. We’ve built Autotab to unhobble models on these bottlenecks 🧵

Autotab

72,945 Aufrufe • vor 1 Jahr

The paradigm shift is here: We are moving from a "Human-First" internet to an "Agent-First" internet. 🤖 In the latest episode of the AI on Air podcast, our CTO Scott Shi - e/acc dives deep into the future of the Agent-to-Agent (A2A) economy. Imagine a world where your AI assistant doesn't just search the web, but autonomously hires and pays other specialized agents to complete complex tasks for you. The infrastructure for this autonomous machine economy is being built right now. Are you ready for the future of AI payments? Watch the full episode to learn more⬇️

The paradigm shift is here: We are moving from a "Human-First" internet to an "Agent-First" internet. 🤖 In the latest episode of the AI on Air podcast, our CTO Scott Shi - e/acc dives deep into the future of the Agent-to-Agent (A2A) economy. Imagine a world where your AI assistant doesn't just search the web, but autonomously hires and pays other specialized agents to complete complex tasks for you. The infrastructure for this autonomous machine economy is being built right now. Are you ready for the future of AI payments? Watch the full episode to learn more⬇️

KITE AI

25,024 Aufrufe • vor 4 Monaten

Inviting early beta testers for our upcoming SWE agent, Asteria. We're solving AI code comprehension, where the context window and the size of your codebase are no longer limitations, and reinventing the UX of how coding agents are currently implemented. We initially built this to address false positives and enhance accuracy for our SAST agent, which autonomously identifies security vulnerabilities, traces how a payload would flow through code, and ultimately demonstrates an actual exploit. The performance improved so significantly that we decided to test it on realistic engineering tasks on large codebases and witnessed SOTA results. Reply "access" if you'd like to be an early beta tester, a lot of quirks to fix.

Inviting early beta testers for our upcoming SWE agent, Asteria. We're solving AI code comprehension, where the context window and the size of your codebase are no longer limitations, and reinventing the UX of how coding agents are currently implemented. We initially built this to address false positives and enhance accuracy for our SAST agent, which autonomously identifies security vulnerabilities, traces how a payload would flow through code, and ultimately demonstrates an actual exploit. The performance improved so significantly that we decided to test it on realistic engineering tasks on large codebases and witnessed SOTA results. Reply "access" if you'd like to be an early beta tester, a lot of quirks to fix.

mufeed vh

12,400 Aufrufe • vor 1 Jahr

got Claude Agent SDK working in Cloudflare Developers containers and built my own code fixer agent, check it out!👇 uses the github mcp/git cli/skills to clone the repo, understand, make the fix, and open a pr when done. planning on more agents focused on non-code tasks 👀

got Claude Agent SDK working in Cloudflare Developers containers and built my own code fixer agent, check it out!👇 uses the github mcp/git cli/skills to clone the repo, understand, make the fix, and open a pr when done. planning on more agents focused on non-code tasks 👀

Thomas Gauvin

18,454 Aufrufe • vor 6 Monaten

Turn any repetitive task into an API. We build an agent that reverse-engineers the network requests to create APIs/tools for your tasks. This is a new paradigm for browser agents, making them reliable, fast, and cheap.

Turn any repetitive task into an API. We build an agent that reverse-engineers the network requests to create APIs/tools for your tasks. This is a new paradigm for browser agents, making them reliable, fast, and cheap.

Magnus Müller

34,395 Aufrufe • vor 7 Monaten

I intended to build an outbound sales agent but it didn't work out due to some geographical restrictions which I'm looking for alternative ways to fix. But I went ahead to create an inbound sales Agent instead. I used to have this work.

I intended to build an outbound sales agent but it didn't work out due to some geographical restrictions which I'm looking for alternative ways to fix. But I went ahead to create an inbound sales Agent instead. I used to have this work.

Ada || Airtable, Zapier & Make.com

16,020 Aufrufe • vor 1 Jahr

🚀 New LangChain Academy Course: Building Reliable Agents 🚀 Shipping agents to production is hard. Traditional software is deterministic – when something breaks, you check the logs and fix the code. But agents rely on non-deterministic models. Add multi-step reasoning, tool use, and real user traffic, and building reliable agents becomes far more complex than traditional system design. The goal of this course is to teach you how to take an agent from first run to production-ready system through iterative cycles of improvement. You’ll learn how to do this with LangSmith, our agent engineering platform for observing, evaluating, and deploying agents. Enroll for free ➡️

🚀 New LangChain Academy Course: Building Reliable Agents 🚀 Shipping agents to production is hard. Traditional software is deterministic – when something breaks, you check the logs and fix the code. But agents rely on non-deterministic models. Add multi-step reasoning, tool use, and real user traffic, and building reliable agents becomes far more complex than traditional system design. The goal of this course is to teach you how to take an agent from first run to production-ready system through iterative cycles of improvement. You’ll learn how to do this with LangSmith, our agent engineering platform for observing, evaluating, and deploying agents. Enroll for free ➡️

LangChain

30,744 Aufrufe • vor 4 Monaten

Introducing Agent 2.0 Go from a simple prompt to fully realized marketing briefs and campaign assets right inside of Runway Agent. You can also easily analyze your performance data to improve your creative and scale it across platforms, formats and markets. Agent is being built to become the most capable autonomous agent for real-world work.

Introducing Agent 2.0 Go from a simple prompt to fully realized marketing briefs and campaign assets right inside of Runway Agent. You can also easily analyze your performance data to improve your creative and scale it across platforms, formats and markets. Agent is being built to become the most capable autonomous agent for real-world work.

Runway

932,261 Aufrufe • vor 10 Tagen

The bottleneck in coding is now the time it takes a human to review agent-generated code. But existing review UIs don't work well for agents. So we built a new one into Amp.

The bottleneck in coding is now the time it takes a human to review agent-generated code. But existing review UIs don't work well for agents. So we built a new one into Amp.

Beyang

63,082 Aufrufe • vor 8 Monaten

We raised $6.5M to build the agent for professionals. When your reputation is on the line, you need an agent that's reliable, secure, and one step ahead. Try it now at

We raised $6.5M to build the agent for professionals. When your reputation is on the line, you need an agent that's reliable, secure, and one step ahead. Try it now at

Kevin Yang

73,738 Aufrufe • vor 3 Monaten

THIS GUY IS RUNNING A 5-AGENT OPENCLAW TEAM 24/7 FOR UNDER $50 A MONTH five agents all running continuously and handing tasks to each other > research agent tracking trends and competitors > writing agent handling marketing content and drafts > ops agent managing emails and scheduling > monitoring agent watching for alerts and updates > coordinator agent routing tasks between all of them and he built a full 3D isometric office where you can watch all of them work in real time and see the current task that each agent is doing each agent has a character, a desk, and a name tag. when it's time for a standup, they walk to the meeting room and gather which is insane it even has voice connection. you can talk to the agents through speech-to-text and tell them what to do out loud it's a tiny autonomous team that runs itself. the coordinator decides who handles what and tasks get passed between agents automatically. it never stops and runs 24/7 all for less than $50/month. we went from AI tools a few months back to AI employees real fast

THIS GUY IS RUNNING A 5-AGENT OPENCLAW TEAM 24/7 FOR UNDER $50 A MONTH five agents all running continuously and handing tasks to each other > research agent tracking trends and competitors > writing agent handling marketing content and drafts > ops agent managing emails and scheduling > monitoring agent watching for alerts and updates > coordinator agent routing tasks between all of them and he built a full 3D isometric office where you can watch all of them work in real time and see the current task that each agent is doing each agent has a character, a desk, and a name tag. when it's time for a standup, they walk to the meeting room and gather which is insane it even has voice connection. you can talk to the agents through speech-to-text and tell them what to do out loud it's a tiny autonomous team that runs itself. the coordinator decides who handles what and tasks get passed between agents automatically. it never stops and runs 24/7 all for less than $50/month. we went from AI tools a few months back to AI employees real fast

Om Patel

27,131 Aufrufe • vor 2 Monaten

Today at Sessions, John Collison showed an agent acting as both buyer and seller in the same loop. Using Stripe Projects and Tempo Wallet, the agent: • Paid $0.04 for an Alpha Vantage dataset in stablecoins • Generated a research report • Listed it for sale to humans and agents

Today at Sessions, John Collison showed an agent acting as both buyer and seller in the same loop. Using Stripe Projects and Tempo Wallet, the agent: • Paid $0.04 for an Alpha Vantage dataset in stablecoins • Generated a research report • Listed it for sale to humans and agents

Tempo

33,465 Aufrufe • vor 2 Monaten

Unstructured intelligence = chaos Most agent frameworks ship without a nervous system: deadlocks, context loss, vacuum hallucinations. We built Common Ground to fix this, agents coordinate on a shared protocol.

Unstructured intelligence = chaos Most agent frameworks ship without a nervous system: deadlocks, context loss, vacuum hallucinations. We built Common Ground to fix this, agents coordinate on a shared protocol.

Intelligent Internet

536,525 Aufrufe • vor 4 Monaten

Announcing Agent Composer: AI that works when it actually IS rocket science. Technical teams can now automate the routine (but complex) tasks that used to take hours every week—root cause analysis, production planning, test code generation— and reduce them down to minutes, so you can get your real work done. Here's how we solved the AI context problem for hard engineering tasks (like rocket science 🚀) 🧵

Announcing Agent Composer: AI that works when it actually IS rocket science. Technical teams can now automate the routine (but complex) tasks that used to take hours every week—root cause analysis, production planning, test code generation— and reduce them down to minutes, so you can get your real work done. Here's how we solved the AI context problem for hard engineering tasks (like rocket science 🚀) 🧵

Contextual AI

1,552,355 Aufrufe • vor 5 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.

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 1 Monat

New short course: LLMs as Operating Systems: Agent Memory, created with Letta, and taught by its founders Charles Packer and Sarah Wooders. An LLM's input context window has limited space. Using a longer input context also costs more and results in slower processing. So, managing what's stored in this context window is important. In the innovative paper MemGPT: Towards LLMs as Operating Systems, its authors (which include the instructors) proposed using an LLM agent to manage this context window. Their system uses a large persistent memory that stores everything that could be included in the input context, and an agent decides what is actually included. Take the example of building a chatbot that needs to remember what's been said earlier in a conversation (perhaps over many days of interaction with a user). As the conversation's length grows, the memory management agent will move information from the input context to a persistent searchable database; summarize information to keep relevant facts in the input context; and restore relevant conversation elements from further back in time. This allows a chatbot to keep what's currently most relevant in its input context memory to generate the next response. When I read the original MemGPT paper, I thought it was an innovative technique for handling memory for LLMs. The open-source Letta framework, which we'll use in this course, makes MemGPT easy to implement. It adds memory to your LLM agents and gives them transparent long-term memory. In detail, you’ll learn: - How to build an agent that can edit its own limited input context memory, using tools and multi-step reasoning - What is a memory hierarchy (an idea from computer operating systems, which use a cache to speed up memory access), and how these ideas apply to managing the LLM input context (where the input context window is a "cache" storing the most relevant information; and an agent decides what to move in and out of this to/from a larger persistent storage system) - How to implement multi-agent collaboration by letting different agents share blocks of memory This course will give you a sophisticated understanding of memory management for LLMs, which is important for chatbots having long conversations, and for complex agentic workflows. Please sign up here!

New short course: LLMs as Operating Systems: Agent Memory, created with Letta, and taught by its founders Charles Packer and Sarah Wooders. An LLM's input context window has limited space. Using a longer input context also costs more and results in slower processing. So, managing what's stored in this context window is important. In the innovative paper MemGPT: Towards LLMs as Operating Systems, its authors (which include the instructors) proposed using an LLM agent to manage this context window. Their system uses a large persistent memory that stores everything that could be included in the input context, and an agent decides what is actually included. Take the example of building a chatbot that needs to remember what's been said earlier in a conversation (perhaps over many days of interaction with a user). As the conversation's length grows, the memory management agent will move information from the input context to a persistent searchable database; summarize information to keep relevant facts in the input context; and restore relevant conversation elements from further back in time. This allows a chatbot to keep what's currently most relevant in its input context memory to generate the next response. When I read the original MemGPT paper, I thought it was an innovative technique for handling memory for LLMs. The open-source Letta framework, which we'll use in this course, makes MemGPT easy to implement. It adds memory to your LLM agents and gives them transparent long-term memory. In detail, you’ll learn: - How to build an agent that can edit its own limited input context memory, using tools and multi-step reasoning - What is a memory hierarchy (an idea from computer operating systems, which use a cache to speed up memory access), and how these ideas apply to managing the LLM input context (where the input context window is a "cache" storing the most relevant information; and an agent decides what to move in and out of this to/from a larger persistent storage system) - How to implement multi-agent collaboration by letting different agents share blocks of memory This course will give you a sophisticated understanding of memory management for LLMs, which is important for chatbots having long conversations, and for complex agentic workflows. Please sign up here!

Andrew Ng

200,752 Aufrufe • vor 1 Jahr

We’ve adapted Google's most profitable search auction mechanism to optimize on-chain and off-chain agent discovery. Riddhi and I built The Agent Pages, an agentic registry, discovery, and coordination protocol designed for complex autonomous agent-to-agent interactions, and we placed 2nd for Coinbase Developer Platform🛡️'s “Most Innovative Use of Agentkit” track at ETHDenver 🏔🦬🦄. The protocol defines a standardized token-curated registry of agents existing across on-chain and off-chain platforms and implements a modified sponsored search auction for agent discovery between the operator agent and the purpose-specific agents for each subtask. Deployed on Base and built with Coinbase Developer Platform🛡️'s AgentKit and Drew Coffman.

We’ve adapted Google's most profitable search auction mechanism to optimize on-chain and off-chain agent discovery. Riddhi and I built The Agent Pages, an agentic registry, discovery, and coordination protocol designed for complex autonomous agent-to-agent interactions, and we placed 2nd for Coinbase Developer Platform🛡️'s “Most Innovative Use of Agentkit” track at ETHDenver 🏔🦬🦄. The protocol defines a standardized token-curated registry of agents existing across on-chain and off-chain platforms and implements a modified sponsored search auction for agent discovery between the operator agent and the purpose-specific agents for each subtask. Deployed on Base and built with Coinbase Developer Platform🛡️'s AgentKit and Drew Coffman.

gurnoor singh narula

25,612 Aufrufe • vor 1 Jahr