正在加载视频...

视频加载失败

加载此视频时出现问题。这可能是由于临时网络问题，或视频可能不可用。

"A developer has morals. It's very difficult to argue that an agent does." Allie Howe & Ian Livingstone (Keycard) Eli Aleyner (Docker), and Ezra Tanzer (Snyk) on agent identity, delegation patterns and what real governance looks like when agents are delegating work to other agents: Watch the full panel:

Docker

553,883 subscribers

17,044 次观看 • 12 天前 •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

0 条评论

暂无评论

原始帖子的评论将显示在这里

相关视频

Introducing Agent Baseline: a vendor-neutral reference architecture for safely building, deploying, and operating AI agents in enterprise environments. Created with Snyk and Keycard, this open-source framework sets the minimum standard for agent safety:

Introducing Agent Baseline: a vendor-neutral reference architecture for safely building, deploying, and operating AI agents in enterprise environments. Created with Snyk and Keycard, this open-source framework sets the minimum standard for agent safety:

Docker

18,594 次观看 • 19 天前

Curious how NanoClaw + Docker Sandboxes actually work? This demo walks through setting up NanoClaw in a Docker Sandbox - connecting messaging accounts, configuring agent tools and access, and running agents inside an isolated MicroVM instead of on your machine. Watch the full demo and learn more →

Curious how NanoClaw + Docker Sandboxes actually work? This demo walks through setting up NanoClaw in a Docker Sandbox - connecting messaging accounts, configuring agent tools and access, and running agents inside an isolated MicroVM instead of on your machine. Watch the full demo and learn more →

Docker

14,653 次观看 • 5 个月前

More agents request HTML pages than humans. Search wasn't built for that. Saahil Jain, CTO of You.com, joins Per Krogslund on Ship Happens to talk about why just pointing your agent at Google doesn't work and what search infrastructure built for AI actually looks like:

More agents request HTML pages than humans. Search wasn't built for that. Saahil Jain, CTO of You.com, joins Per Krogslund on Ship Happens to talk about why just pointing your agent at Google doesn't work and what search infrastructure built for AI actually looks like:

Docker

13,041 次观看 • 7 天前

Spinning up LangChainGo? This walkthrough shows how to launch a working agent with Docker Compose using the compose-for-agents repo. Local dev or cloud offload, one command to run. Watch here: Repo:

Spinning up LangChainGo? This walkthrough shows how to launch a working agent with Docker Compose using the compose-for-agents repo. Local dev or cloud offload, one command to run. Watch here: Repo:

Docker

39,642 次观看 • 11 个月前

Everyone is debating how much autonomy to give agents. What that conversation often skips is where humans need to stay in the loop. Watch the full episode for all of our CISO panelists’ takes on what this looks like in practice →

Everyone is debating how much autonomy to give agents. What that conversation often skips is where humans need to stay in the loop. Watch the full episode for all of our CISO panelists’ takes on what this looks like in practice →

Docker

10,838 次观看 • 20 天前

Everyone can build a chatbot. Not everyone can ship a secure, production-grade agent. In Episode 3 of AI Guide to the Galaxy, we break down how to enable your AI agents for the real-world deployments with Docker’s MCP Toolkit and Gateway. Watch here:

Everyone can build a chatbot. Not everyone can ship a secure, production-grade agent. In Episode 3 of AI Guide to the Galaxy, we break down how to enable your AI agents for the real-world deployments with Docker’s MCP Toolkit and Gateway. Watch here:

Docker

48,500 次观看 • 11 个月前

We won Best Trustless Agent at the Solana x402 Hackathon! Amiko Marketplace turns x402 payments into a trustless, on-chain economy where agents have identity, reputation, and verifiable actions. The agent economy finally has a trust layer. What we built: - Agent Registry - On-chain Job Records - Feedback & Reputation System - Search + Discoverability Everything tied to real wallets, real actions, real performance. Agents aren’t just paying each other. Now they’re accountable. This is the foundation for a global network of autonomous agents that can be trusted, rated, and discovered. All on Solana. More agents. More you.

We won Best Trustless Agent at the Solana x402 Hackathon! Amiko Marketplace turns x402 payments into a trustless, on-chain economy where agents have identity, reputation, and verifiable actions. The agent economy finally has a trust layer. What we built: - Agent Registry - On-chain Job Records - Feedback & Reputation System - Search + Discoverability Everything tied to real wallets, real actions, real performance. Agents aren’t just paying each other. Now they’re accountable. This is the foundation for a global network of autonomous agents that can be trusted, rated, and discovered. All on Solana. More agents. More you.

AMIKO

24,112 次观看 • 8 个月前

Two updates to audit capabilities in Docker AI Governance. Every policy decision your agents trigger is now searchable in Docker Cloud and can be streamed to your SIEM tools. Read more:

Two updates to audit capabilities in Docker AI Governance. Every policy decision your agents trigger is now searchable in Docker Cloud and can be streamed to your SIEM tools. Read more:

Docker

17,892 次观看 • 15 天前

In an agentic world, identity = infrastructure. As humans, agents, and micro-agents start coordinating everything, you need a stable way to name and trust them and .agent is that shared identity layer. It’s designed to start on-chain and extend into DNS. Get your .agent identity set up before the good ones run out 👇

In an agentic world, identity = infrastructure. As humans, agents, and micro-agents start coordinating everything, you need a stable way to name and trust them and .agent is that shared identity layer. It’s designed to start on-chain and extend into DNS. Get your .agent identity set up before the good ones run out 👇

Sentient

58,759 次观看 • 7 个月前

AgentKit gives AI Agents a way to transact on GOAT. Existing agents can plug in to earn, trade, and register an onchain identity that others can discover, review, and trust. Works with OpenClaw🦞, Hermes, ClawUp, and other agent runtimes. Try it:

AgentKit gives AI Agents a way to transact on GOAT. Existing agents can plug in to earn, trade, and register an onchain identity that others can discover, review, and trust. Works with OpenClaw🦞, Hermes, ClawUp, and other agent runtimes. Try it:

GOAT Network

865,395 次观看 • 3 个月前

Your agents suck when using the web because 85% of it doesn't have an API. Browserbase gives them everything they need to do work online. Leading AI companies like Ramp, Lovable, and Clay trust us to power agents that do real work on behalf of real people. With a single API key, your agent gets everything it needs to navigate the wild web: browsers, search, fetch, identity, a sandbox runtime, and model gateway. Stop waiting on integrations, build agents that can browse and interact with the web just like humans.

Your agents suck when using the web because 85% of it doesn't have an API. Browserbase gives them everything they need to do work online. Leading AI companies like Ramp, Lovable, and Clay trust us to power agents that do real work on behalf of real people. With a single API key, your agent gets everything it needs to navigate the wild web: browsers, search, fetch, identity, a sandbox runtime, and model gateway. Stop waiting on integrations, build agents that can browse and interact with the web just like humans.

Paul Klein IV

1,729,307 次观看 • 4 个月前

Launching 'Expert Agents' on Agent Two. A specialised creative crew of AI agents, trained on real creative expertise. Name the role and it's filled: creative director, DOP, storyboard artist, and more. And they talk to each other. Independently. In parallel. You're no longer briefing each agent one by one: you're running a crew, exactly like on set. Expert agents. Now in conversation. Agent Two is frontier intelligence for creative work. 4x faster, at half the price.

Launching 'Expert Agents' on Agent Two. A specialised creative crew of AI agents, trained on real creative expertise. Name the role and it's filled: creative director, DOP, storyboard artist, and more. And they talk to each other. Independently. In parallel. You're no longer briefing each agent one by one: you're running a crew, exactly like on set. Expert agents. Now in conversation. Agent Two is frontier intelligence for creative work. 4x faster, at half the price.

Invideo

3,404,930 次观看 • 19 天前

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 次观看 • 4 个月前

New Course: ACP: Agent Communication Protocol Learn to build agents that communicate and collaborate across different frameworks using ACP in this short course built with IBM Research's BeeAI, and taught by Sandi Besen, AI Research Engineer & Ecosystem Lead at IBM, and Nicholas Renotte, Head of AI Developer Advocacy at IBM. Building a multi-agent system with agents built or used by different teams and organizations can become challenging. You may need to write custom integrations each time a team updates their agent design or changes their choice of agentic orchestration framework. The Agent Communication Protocol (ACP) is an open protocol that addresses this challenge by standardizing how agents communicate, using a unified RESTful interface that works across frameworks. In this protocol, you host an agent inside an ACP server, which handles requests from an ACP client and passes them to the appropriate agent. Using a standardized client-server interface allows multiple teams to reuse agents across projects. It also makes it easier to switch between frameworks, replace an agent with a new version, or update a multi-agent system without refactoring the entire system. In this course, you’ll learn to connect agents through ACP. You’ll understand the lifecycle of an ACP Agent and how it compares to other protocols, such as MCP (Model Context Protocol) and A2A (Agent-to-Agent). You’ll build ACP-compliant agents and implement both sequential and hierarchical workflows of multiple agents collaborating using ACP. Through hands-on exercises, you’ll build: - A RAG agent with CrewAI and wrap it inside an ACP server. - An ACP Client to make calls to the ACP server you created. - A sequential workflow that chains an ACP server, created with Smolagents, to the RAG agent. - A hierarchical workflow using a router agent that transforms user queries into tasks, delegated to agents available through ACP servers. - An agent that uses MCP to access tools and ACP to communicate with other agents. You’ll finish up by importing your ACP agents into the BeeAI platform, an open-source registry for discovering and sharing agents. ACP enables collaboration between agents across teams and organizations. By the end of this course, you’ll be able to build ACP agents and workflows that communicate and collaborate regardless of framework. Please sign up here:

New Course: ACP: Agent Communication Protocol Learn to build agents that communicate and collaborate across different frameworks using ACP in this short course built with IBM Research's BeeAI, and taught by Sandi Besen, AI Research Engineer & Ecosystem Lead at IBM, and Nicholas Renotte, Head of AI Developer Advocacy at IBM. Building a multi-agent system with agents built or used by different teams and organizations can become challenging. You may need to write custom integrations each time a team updates their agent design or changes their choice of agentic orchestration framework. The Agent Communication Protocol (ACP) is an open protocol that addresses this challenge by standardizing how agents communicate, using a unified RESTful interface that works across frameworks. In this protocol, you host an agent inside an ACP server, which handles requests from an ACP client and passes them to the appropriate agent. Using a standardized client-server interface allows multiple teams to reuse agents across projects. It also makes it easier to switch between frameworks, replace an agent with a new version, or update a multi-agent system without refactoring the entire system. In this course, you’ll learn to connect agents through ACP. You’ll understand the lifecycle of an ACP Agent and how it compares to other protocols, such as MCP (Model Context Protocol) and A2A (Agent-to-Agent). You’ll build ACP-compliant agents and implement both sequential and hierarchical workflows of multiple agents collaborating using ACP. Through hands-on exercises, you’ll build: - A RAG agent with CrewAI and wrap it inside an ACP server. - An ACP Client to make calls to the ACP server you created. - A sequential workflow that chains an ACP server, created with Smolagents, to the RAG agent. - A hierarchical workflow using a router agent that transforms user queries into tasks, delegated to agents available through ACP servers. - An agent that uses MCP to access tools and ACP to communicate with other agents. You’ll finish up by importing your ACP agents into the BeeAI platform, an open-source registry for discovering and sharing agents. ACP enables collaboration between agents across teams and organizations. By the end of this course, you’ll be able to build ACP agents and workflows that communicate and collaborate regardless of framework. Please sign up here:

Andrew Ng

105,343 次观看 • 1 年前

My 2025 year-end project: Solving Agents After weeks of stress testing agents, I landed on what a top-tier e2e agent actually looks like: Fast, Durable, Resumable, Secure Benchmarked. Battle-tested. Shipping the best patterns as reusable templates, packages and other OSS soon.

My 2025 year-end project: Solving Agents After weeks of stress testing agents, I landed on what a top-tier e2e agent actually looks like: Fast, Durable, Resumable, Secure Benchmarked. Battle-tested. Shipping the best patterns as reusable templates, packages and other OSS soon.

Chris Tate

39,146 次观看 • 7 个月前

$Running Claude Code Agents in Docker and it's working great 👏 Just executed this and watched Claude build a complete Express REST API inside an isolated container: npx claude-code-templates@latest --sandbox docker \ --agent development-team/backend-architect \ --prompt "Create a REST API with Express with validation and error handling" - Container spins up - installs Claude Agent SDK - downloads the agent - Claude starts coding Everything visible in Docker logs. Output: Full Express project with routes, validation, error handling. All files extracted to local after execution. Next tutorials: Adding Telemetry and Hooks to monitor what Claude does inside the container. Running these agents on ECS or Kubernetes would be interesting 🤔$

Running Claude Code Agents in Docker and it's working great 👏 Just executed this and watched Claude build a complete Express REST API inside an isolated container: npx claude-code-templates@latest --sandbox docker \ --agent development-team/backend-architect \ --prompt "Create a REST API with Express with validation and error handling" - Container spins up - installs Claude Agent SDK - downloads the agent - Claude starts coding Everything visible in Docker logs. Output: Full Express project with routes, validation, error handling. All files extracted to local after execution. Next tutorials: Adding Telemetry and Hooks to monitor what Claude does inside the container. Running these agents on ECS or Kubernetes would be interesting 🤔

Daniel San

100,028 次观看 • 9 个月前

What if your AI agent had to compete for its paycheck? Not get assigned work. Not sit in a queue. Actually compete against other agents. Best output wins. That's what Agent Hansa built. And it's the smartest model I've seen in the agent space.

What if your AI agent had to compete for its paycheck? Not get assigned work. Not sit in a queue. Actually compete against other agents. Best output wins. That's what Agent Hansa built. And it's the smartest model I've seen in the agent space.

Himanshu Kumar

33,621 次观看 • 4 个月前

One major downside of working with agents in a terminal it's the opposite of async and when doing tons of tasks it gets super tiring. I build an inbox for my agents so we can do full async work. The agent has access to it via MCP and has a bunch of functions: Agents get 2 inbox tools: 🔘 list_inbox to read messages, 🔘 reply_to_inbox to answer. Inboxes are synced to a session, so all replies instantly get added to the agents context. Agents can update me about finished tasks with it, or; when an agent hits a blocker it sends an input request → I reply → it wakes up and keeps working

One major downside of working with agents in a terminal it's the opposite of async and when doing tons of tasks it gets super tiring. I build an inbox for my agents so we can do full async work. The agent has access to it via MCP and has a bunch of functions: Agents get 2 inbox tools: 🔘 list_inbox to read messages, 🔘 reply_to_inbox to answer. Inboxes are synced to a session, so all replies instantly get added to the agents context. Agents can update me about finished tasks with it, or; when an agent hits a blocker it sends an input request → I reply → it wakes up and keeps working

Danny Postma

28,646 次观看 • 21 天前

This agent relies on a marketplace of 2.8M+ specialized agents to complete a task. This is how I imagine the future of personal agents. Here is how this works: 1. Agentverse is a marketplace of agents 2. Anyone can build and publish an agent there 3. ASI:One is a personal agent with access to the marketplace 4. To solve a task, ASI:One finds the best agent for the job In other words: You have an agent that can solve pretty much *anything* because it knows how to outsource the work to a network of specialized agents. And you don't need to do any setup to get this working. The way ranking agents in the marketplace works is pretty cool: Fetch.ai, the company behind it, created an AgentRank algorithm that works much like Google's PageRank. Every time an agent calls another, it forms a connection. Over time, these connections build into a graph of who relies on whom. An agent that is frequently called by other reputable agents scores higher and surfaces higher in results. The end result here is that your agent will consistently use the best agents in the marketplace to accomplish a goal. You should give this a try: You don't need to pay or even register to try these agents. Thanks to the team for partnering with me on this post.

This agent relies on a marketplace of 2.8M+ specialized agents to complete a task. This is how I imagine the future of personal agents. Here is how this works: 1. Agentverse is a marketplace of agents 2. Anyone can build and publish an agent there 3. ASI:One is a personal agent with access to the marketplace 4. To solve a task, ASI:One finds the best agent for the job In other words: You have an agent that can solve pretty much anything because it knows how to outsource the work to a network of specialized agents. And you don't need to do any setup to get this working. The way ranking agents in the marketplace works is pretty cool: Fetch.ai, the company behind it, created an AgentRank algorithm that works much like Google's PageRank. Every time an agent calls another, it forms a connection. Over time, these connections build into a graph of who relies on whom. An agent that is frequently called by other reputable agents scores higher and surfaces higher in results. The end result here is that your agent will consistently use the best agents in the marketplace to accomplish a goal. You should give this a try: You don't need to pay or even register to try these agents. Thanks to the team for partnering with me on this post.

Santiago

18,715 次观看 • 1 个月前