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"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:

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Docker

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17,044 просмотров • 12 дней назад •via X (Twitter)

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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:

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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 🤔

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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 просмотров • 4 месяцев назад

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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

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28,646 просмотров • 21 дней назад

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.
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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.

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