Загрузка видео...

Не удалось загрузить видео

Возникла проблема при загрузке этого видео. Это может быть связано с временными проблемами сети или видео может быть недоступно.

На главную

"By stitching these three primitives together, you get an agent loop that's actually a distributed state machine under the hood." At DevCon 6, Palantir Group Lead Natasha Armbrust launches Agent Engine and Agent SDK, giving developers the core primitives to build agents that form a distributed state machine and... show more

Palantir

443,476 subscribers

72,323 просмотров • 3 месяцев назад •via X (Twitter)

Наука и технологии Образование

Anya Rossi• Live Now

Private livecam show

Комментарии: 0

Нет доступных комментариев

Здесь появятся комментарии из оригинального поста

Похожие видео

At DevCon 6, Palantir Software Engineer Ankit Shankar unveils the Agent Stack, a set of products to help you build production agents for the enterprise, all built on the Ontology. ▪️Orchestrator for durable execution ▪️Agent Engine and Agent SDK for building agents of any shape ▪️Agent Manager for actionable telemetry and observability ▪️AIP Evolve for continuous, end-to-end optimization

At DevCon 6, Palantir Software Engineer Ankit Shankar unveils the Agent Stack, a set of products to help you build production agents for the enterprise, all built on the Ontology. ▪️Orchestrator for durable execution ▪️Agent Engine and Agent SDK for building agents of any shape ▪️Agent Manager for actionable telemetry and observability ▪️AIP Evolve for continuous, end-to-end optimization

Palantir

42,576 просмотров • 3 месяцев назад

Human-agent teaming requires a human-agent developer experience. At DevCon 5, Palantir Group Lead John Ayad showcases the future of pro-code development in Foundry and AIP — introducing a new CLI that enables developers and agents to create pro-code monorepos for end-to-end applications, including data pipelines, Ontology primitives, and application artifacts.

Human-agent teaming requires a human-agent developer experience. At DevCon 5, Palantir Group Lead John Ayad showcases the future of pro-code development in Foundry and AIP — introducing a new CLI that enables developers and agents to create pro-code monorepos for end-to-end applications, including data pipelines, Ontology primitives, and application artifacts.

Palantir

48,901 просмотров • 7 месяцев назад

Anyone can build an agent. But to build a trustworthy agent at enterprise scale that is durable, long-running, optimized, contextually aware, and autonomous, the right infrastructure is required. At DevCon 6 we introduced the Agent Stack, the culmination of learnings gathered over years of agentic implementations. Orchestrator, Agent Engine, Agent SDK, Agent Builder, Agent Manager, AIP Evolve, SuperRepo, and so much more. All built on the Ontology, to power agents that actually work in production.

Anyone can build an agent. But to build a trustworthy agent at enterprise scale that is durable, long-running, optimized, contextually aware, and autonomous, the right infrastructure is required. At DevCon 6 we introduced the Agent Stack, the culmination of learnings gathered over years of agentic implementations. Orchestrator, Agent Engine, Agent SDK, Agent Builder, Agent Manager, AIP Evolve, SuperRepo, and so much more. All built on the Ontology, to power agents that actually work in production.

Palantir

83,842 просмотров • 3 месяцев назад

Most agent demos are stateless. Ask → answer → context gone. Production agents need to run for days — pausing, resuming, persisting state. Addy Osmani joins Smitha Kolan on the Agent Factory to build three real agents with ADK 2.0 and Gemini 3.5 Flash, including one that runs Doom in the browser. Check out this episode and more in the Agent Factory series →

Most agent demos are stateless. Ask → answer → context gone. Production agents need to run for days — pausing, resuming, persisting state. Addy Osmani joins Smitha Kolan on the Agent Factory to build three real agents with ADK 2.0 and Gemini 3.5 Flash, including one that runs Doom in the browser. Check out this episode and more in the Agent Factory series →

Google for Developers

13,939 просмотров • 4 месяцев назад

Big release here! AG-UI 🤝 Google A2A Embed A2A multi-agent meshes in your frontend, and build fullstack multi-agent apps with A2A and AG-UI. 🔍How it works: Simply drop-in A2A agent endpoints, and an A2A AG-UI orchestrator agent will bi-directionally stream events between your Agents & application. Includes: - UI for A2A Interactions - Generative UI for Agent Tool Calls - Human-in-the Loop Approvals - State Synchronization (agent mesh application state) - Frontend Tool Calls for A2A Agents Watch the demo below > LangGraph & Google ADK agents working together simultaneously inside an app Demo GitHub, docs, and more 👇

Big release here! AG-UI 🤝 Google A2A Embed A2A multi-agent meshes in your frontend, and build fullstack multi-agent apps with A2A and AG-UI. 🔍How it works: Simply drop-in A2A agent endpoints, and an A2A AG-UI orchestrator agent will bi-directionally stream events between your Agents & application. Includes: - UI for A2A Interactions - Generative UI for Agent Tool Calls - Human-in-the Loop Approvals - State Synchronization (agent mesh application state) - Frontend Tool Calls for A2A Agents Watch the demo below > LangGraph & Google ADK agents working together simultaneously inside an app Demo GitHub, docs, and more 👇

CopilotKit🪁

31,845 просмотров • 1 год назад

🚀Launching CoAgents Public Beta 🪁: Everything you need to build Agent-Native applications, powered by LangGraph & CopilotKit. CoAgents enables in-app agents with: - Agenetic generative UI ✨ - Shared state (between agent application) - Streaming intermediate agent state - Human-in-the-Loop (Human approval & planning) 👨‍🦰 - Frontend Actions 💪 --- We’ve recently seen the emergence of Agent-Native applications that actually work: powered by vertical AI agents + agentic UX + deep in-app integration. (Apps like the Replit AI Agent, OpenAI Canvas, etc, but for any domain) With CoAgents + LangGraph, you can build this next generation of agent apps, for any vertical, with a clean and easy to use infrastructure (in hours). 💻Get started: 🗞️Blog: 🕹️Demo: 🗓️Webinar (Build an Agent-Native Apps with CoAgents & LangGraph): CC: Atai Barkai, , Harrison Chase

🚀Launching CoAgents Public Beta 🪁: Everything you need to build Agent-Native applications, powered by LangGraph & CopilotKit. CoAgents enables in-app agents with: - Agenetic generative UI ✨ - Shared state (between agent application) - Streaming intermediate agent state - Human-in-the-Loop (Human approval & planning) 👨‍🦰 - Frontend Actions 💪 --- We’ve recently seen the emergence of Agent-Native applications that actually work: powered by vertical AI agents + agentic UX + deep in-app integration. (Apps like the Replit AI Agent, OpenAI Canvas, etc, but for any domain) With CoAgents + LangGraph, you can build this next generation of agent apps, for any vertical, with a clean and easy to use infrastructure (in hours). 💻Get started: 🗞️Blog: 🕹️Demo: 🗓️Webinar (Build an Agent-Native Apps with CoAgents & LangGraph): CC: Atai Barkai, , Harrison Chase

CopilotKit🪁

51,685 просмотров • 2 лет назад

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

105,839 просмотров • 1 год назад

An agent without access to the world is operating in a bubble. OptimAI Search breaks that boundary. Live information becomes agent-readable context: enabling agents to research, discover and reason beyond their training data. And the building blocks are ready: MCP. SDK. Marketplace. 💡 The infrastructure is there. Now let's see what agents you build on top of it.

An agent without access to the world is operating in a bubble. OptimAI Search breaks that boundary. Live information becomes agent-readable context: enabling agents to research, discover and reason beyond their training data. And the building blocks are ready: MCP. SDK. Marketplace. 💡 The infrastructure is there. Now let's see what agents you build on top of it.

OptimAI Network

12,502 просмотров • 2 месяцев назад

Modern AI agents have evolved beyond a simple state graph and a few tool calls. Google Developer Expert Sam Witteveen shares 5 components you need to build world-class agents. Join Gemini Enterprise Agent Ready (GEAR) for the latest agent resources →

Modern AI agents have evolved beyond a simple state graph and a few tool calls. Google Developer Expert Sam Witteveen shares 5 components you need to build world-class agents. Join Gemini Enterprise Agent Ready (GEAR) for the latest agent resources →

Google Cloud Tech

11,360 просмотров • 4 месяцев назад

Agents are now your customers. Here are 9 startup ideas for that customer: - An agency that makes agents cite you. - A system that gives an agent an identity and permissions. - Receipts and audit trails for the work an agent does. - A generator that makes your documents ready for agents. - Security for the agent that reads your inbox. - A service that makes your pricing page readable to an agent. - MCP servers for franchises. - A support desk that agents call. - A sandbox where agents test your software. Pick one and build it.

Agents are now your customers. Here are 9 startup ideas for that customer: - An agency that makes agents cite you. - A system that gives an agent an identity and permissions. - Receipts and audit trails for the work an agent does. - A generator that makes your documents ready for agents. - Security for the agent that reads your inbox. - A service that makes your pricing page readable to an agent. - MCP servers for franchises. - A support desk that agents call. - A sandbox where agents test your software. Pick one and build it.

The Startup Ideas Podcast (SIP) 🧃

14,148 просмотров • 2 месяцев назад

Introducing ADK Go, a new open-source toolkit designed to empower Go developers to build powerful AI agents. Highlights: 0:00 Introducing the Go ADK 0:53 Build your first Go agent 1:49 Run your agent 2:23 Understanding Tools 3:28 Run your agent with tools 5:00 Multi-agent patterns in Go 7:58 State, Sessions, and Events

Introducing ADK Go, a new open-source toolkit designed to empower Go developers to build powerful AI agents. Highlights: 0:00 Introducing the Go ADK 0:53 Build your first Go agent 1:49 Run your agent 2:23 Understanding Tools 3:28 Run your agent with tools 5:00 Multi-agent patterns in Go 7:58 State, Sessions, and Events

Google AI Developers

39,753 просмотров • 11 месяцев назад

"The bottleneck for an agent's usefulness today is not intelligence. It's trust." At DevCon 6, Palantir Group Lead John Mathews launches Orchestrator, the durable execution layer that lets agents crash, recover, wait, and resume exactly where they left off. Watch a patient discharge agent get torn down mid-task and rehydrate days later with zero lost state, zero duplicate side effects, and zero compute burned while it waits on a doctor's sign-off.

"The bottleneck for an agent's usefulness today is not intelligence. It's trust." At DevCon 6, Palantir Group Lead John Mathews launches Orchestrator, the durable execution layer that lets agents crash, recover, wait, and resume exactly where they left off. Watch a patient discharge agent get torn down mid-task and rehydrate days later with zero lost state, zero duplicate side effects, and zero compute burned while it waits on a doctor's sign-off.

Palantir

67,760 просмотров • 3 месяцев назад

🚀 How to Create an Agent on VirtualDaos Testnet? 💡 Practice steps 1 & 2 on Testnet now and get ready for VirtualDaos Mainnet! 👇 Step 1: Access VirtualDaos & Create Your Agent ▪️Go to and click "Create Agent" to begin the setup process. Step 2: Fill in Agent Details ▪️Agent Profile (Name, identity of the Agent) ▪️Description (Define its mission and purpose) ✅ Once completed, hit Create to finalize your Agent. #AIAgent #DeFAI $DAOX

VirtualDaos

11,690 просмотров • 1 год назад

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 просмотров • 6 месяцев назад

Opus 5.5 makes this AI stack look f…cking illegal 10 GitHub repos for building everything around the model 01 zen-mcp-server ▸ → your agent debates Gemini, GPT and local models mid-task 02 claude-context ▸ → semantic code search so the agent stops grepping blind 03 wshobson/agents ▸ → a roster of subagents, each with one narrow job 04 mcp-agent ▸ → workflow patterns built around MCP tools, not around chat 05 Backlog.md ▸ → tasks live in git, so the agent and you read the same board 06 Claude Code Development Kit ▸ → hooks, subagents and MCP wired for context at scale 07 agent-rules ▸ → shared guardrails the agent inherits in every repo 08 Agent Skills ▸ → one file that teaches the agent a job, portable across tools 09 agent-scan ▸ → scans your agents, MCP servers and skills for what leaks 10 awesome-claude-code-toolkit ▸ → the map: agents, skills, commands, hooks, MCP configs the architecture: rules → context → subagents → tools → state → review → scan I'd split the stack like this: context: claude-context → Development Kit work: wshobson/agents → mcp-agent → zen-mcp-server state: Backlog.md safety: agent-rules → Agent Skills → agent-scan everyone posts the SDK and the CLI the layer that decides whether your agent survives a week is this one the model is one folder in the stack ⭣

Opus 5.5 makes this AI stack look f…cking illegal 10 GitHub repos for building everything around the model 01 zen-mcp-server ▸ → your agent debates Gemini, GPT and local models mid-task 02 claude-context ▸ → semantic code search so the agent stops grepping blind 03 wshobson/agents ▸ → a roster of subagents, each with one narrow job 04 mcp-agent ▸ → workflow patterns built around MCP tools, not around chat 05 Backlog.md ▸ → tasks live in git, so the agent and you read the same board 06 Claude Code Development Kit ▸ → hooks, subagents and MCP wired for context at scale 07 agent-rules ▸ → shared guardrails the agent inherits in every repo 08 Agent Skills ▸ → one file that teaches the agent a job, portable across tools 09 agent-scan ▸ → scans your agents, MCP servers and skills for what leaks 10 awesome-claude-code-toolkit ▸ → the map: agents, skills, commands, hooks, MCP configs the architecture: rules → context → subagents → tools → state → review → scan I'd split the stack like this: context: claude-context → Development Kit work: wshobson/agents → mcp-agent → zen-mcp-server state: Backlog.md safety: agent-rules → Agent Skills → agent-scan everyone posts the SDK and the CLI the layer that decides whether your agent survives a week is this one the model is one folder in the stack ⭣

rody

18,089 просмотров • 28 дней назад

Agents shouldn't see your secrets. Today, most deployments hand the agent a real API key and hope for the best. That's why we're launching Infisical Agent Proxy. You give an agent a fake API key. The agent uses that key and the proxy swaps it for the real one at the network boundary before forwarding it outbound to its destination. An agent can't leak a secret it never sees. Build and deploy AI agents securely with Infisical Agent Proxy.

Agents shouldn't see your secrets. Today, most deployments hand the agent a real API key and hope for the best. That's why we're launching Infisical Agent Proxy. You give an agent a fake API key. The agent uses that key and the proxy swaps it for the real one at the network boundary before forwarding it outbound to its destination. An agent can't leak a secret it never sees. Build and deploy AI agents securely with Infisical Agent Proxy.

Infisical

29,214 просмотров • 3 месяцев назад

🔥 🤖 🕵️‍♂️ INTRODUCING: Meta Agent 💥 this is by far the most exciting thing I've created using AI!!! >>> made using Manus and @windsurf 🔑 Meta Agent is an AI agent that uses the OpenAI Agents SDK to GENERATE NEW AGENTS you simply tell Meta Agent in natural language the agent you want... 🤯 and Meta Agent generates it for you fully open --> link to GitHub and example walkthrough below ⬇️

🔥 🤖 🕵️‍♂️ INTRODUCING: Meta Agent 💥 this is by far the most exciting thing I've created using AI!!! >>> made using Manus and @windsurf 🔑 Meta Agent is an AI agent that uses the OpenAI Agents SDK to GENERATE NEW AGENTS you simply tell Meta Agent in natural language the agent you want... 🤯 and Meta Agent generates it for you fully open --> link to GitHub and example walkthrough below ⬇️

Dan McAteer

154,081 просмотров • 1 год назад

AI Messenger: Giving Voice to Autonomous Agents The future of AI isn't just about making agents smarter - it's about making them truly autonomous. Today, we're taking a major step toward this future with AI Messenger, a breakthrough that fundamentally changes how AI agents operate, communicate, and create value. The Innovation We've developed a new way for AI agents to communicate. At its core is the 'incoming_message' workflow trigger - a system that lets any platform or user interact directly with Loomlay agents through a messaging endpoint. Direct Interaction Imagine having an AI assistant you can chat with anytime, through any platform - Telegram, your website, or custom interface. Ask "What's happening with $ETH today?" and your agent analyzes market data, checks trading volumes, and gives you a comprehensive update. Your agent maintains context, understanding exactly what you need. Event-Driven Intelligence The power of AI Messenger goes beyond direct communication: ▪️Trading agent executes when whale wallet movements exceed threshold ▪️Research agent alerts when new protocol documentation drops ▪️Analytics agent triggers when volume patterns match historical pumps ▪️Portfolio agent re-balances, when asset allocation hits specified limits This is true automation - agents that act precisely when needed. A New Era of Collaboration We're creating an ecosystem where agents work together seamlessly: ▪️Research agents feed insights to trading agents ▪️analytics agents alert management agents ▪️support agents tap into knowledge agents This isn't just automation - it's an intelligent network where each agent enhances the capabilities of others. B2B Solution Imagine a DEX, where users can ask about liquidity pools, trading pairs, or market trends through a simple chat interface - and get answers from an agent that knows your protocol inside out. Or a lending platform where users chat with an agent that understands their positions and can provide real-time advice. Implementation is seamless - we handle the agent creation and widgets setup,our partners provide the value to their users. The Future of AI Agents This update represents a fundamental shift in how AI agents operate. We're moving from isolated, scheduled tasks to an interconnected ecosystem of responsive, collaborative agents. This is our vision of truly autonomous AI - intelligent systems that communicate, collaborate, and respond to real needs in real-time. Telegram integration is available right now. Below is a sneak peak of what's coming next week 🪄 Because $LAY is the way!

AI Messenger: Giving Voice to Autonomous Agents The future of AI isn't just about making agents smarter - it's about making them truly autonomous. Today, we're taking a major step toward this future with AI Messenger, a breakthrough that fundamentally changes how AI agents operate, communicate, and create value. The Innovation We've developed a new way for AI agents to communicate. At its core is the 'incoming_message' workflow trigger - a system that lets any platform or user interact directly with Loomlay agents through a messaging endpoint. Direct Interaction Imagine having an AI assistant you can chat with anytime, through any platform - Telegram, your website, or custom interface. Ask "What's happening with $ETH today?" and your agent analyzes market data, checks trading volumes, and gives you a comprehensive update. Your agent maintains context, understanding exactly what you need. Event-Driven Intelligence The power of AI Messenger goes beyond direct communication: ▪️Trading agent executes when whale wallet movements exceed threshold ▪️Research agent alerts when new protocol documentation drops ▪️Analytics agent triggers when volume patterns match historical pumps ▪️Portfolio agent re-balances, when asset allocation hits specified limits This is true automation - agents that act precisely when needed. A New Era of Collaboration We're creating an ecosystem where agents work together seamlessly: ▪️Research agents feed insights to trading agents ▪️analytics agents alert management agents ▪️support agents tap into knowledge agents This isn't just automation - it's an intelligent network where each agent enhances the capabilities of others. B2B Solution Imagine a DEX, where users can ask about liquidity pools, trading pairs, or market trends through a simple chat interface - and get answers from an agent that knows your protocol inside out. Or a lending platform where users chat with an agent that understands their positions and can provide real-time advice. Implementation is seamless - we handle the agent creation and widgets setup,our partners provide the value to their users. The Future of AI Agents This update represents a fundamental shift in how AI agents operate. We're moving from isolated, scheduled tasks to an interconnected ecosystem of responsive, collaborative agents. This is our vision of truly autonomous AI - intelligent systems that communicate, collaborate, and respond to real needs in real-time. Telegram integration is available right now. Below is a sneak peak of what's coming next week 🪄 Because $LAY is the way!

Loomlay

26,149 просмотров • 1 год назад

AG-UI makes building agentic applications dramatically easier. Here's how it works. This is a model for a simple chatbot: User → LLM → Response But interactive agents that render UI, pause for approvals, and ask users for input need a much more complex model. When building these agents, a response from the LLM will include a series of state changes as the agent runs: • Agent started a task • Agent called a tool • Agent updated its state • Agent streams these tokens • Agent is waiting on a human • Agent is resuming the task The Agent-User Interaction Protocol (AG-UI) treats the LLM response as a stream of events rather than a text endpoint. In practice, here is what you get as an agent runs: 1. Lifecycle events so your UI knows where the agent is. 2. Text messages that stream tokens. 3. Tool calls so your UI can prefill a form with any required arguments. 4. State updates that keep your UI in sync with the agent. 5. Special events for human approvals, rich media, and custom needs. All of these events travel over standard transports (SSE, WebSockets, or plain HTTP) as JSON. As a result, you can build a frontend that stays in sync with the agent's progress without having to invent a custom process to make this happen. For example, building a human-in-the-loop workflow becomes an off-the-shelf component you can integrate rather than build from scratch. CopilotKit🪁 is the creator of AG-UI, and you can use it when building frontend applications pretty much anywhere: • React • Angular • Vue • React Native • Slack • Teams • Discord • WhatsApp • Telegram Here is the link for you to check it out: Thanks to the CopilotKit team for partnering with me on this post.

AG-UI makes building agentic applications dramatically easier. Here's how it works. This is a model for a simple chatbot: User → LLM → Response But interactive agents that render UI, pause for approvals, and ask users for input need a much more complex model. When building these agents, a response from the LLM will include a series of state changes as the agent runs: • Agent started a task • Agent called a tool • Agent updated its state • Agent streams these tokens • Agent is waiting on a human • Agent is resuming the task The Agent-User Interaction Protocol (AG-UI) treats the LLM response as a stream of events rather than a text endpoint. In practice, here is what you get as an agent runs: 1. Lifecycle events so your UI knows where the agent is. 2. Text messages that stream tokens. 3. Tool calls so your UI can prefill a form with any required arguments. 4. State updates that keep your UI in sync with the agent. 5. Special events for human approvals, rich media, and custom needs. All of these events travel over standard transports (SSE, WebSockets, or plain HTTP) as JSON. As a result, you can build a frontend that stays in sync with the agent's progress without having to invent a custom process to make this happen. For example, building a human-in-the-loop workflow becomes an off-the-shelf component you can integrate rather than build from scratch. CopilotKit🪁 is the creator of AG-UI, and you can use it when building frontend applications pretty much anywhere: • React • Angular • Vue • React Native • Slack • Teams • Discord • WhatsApp • Telegram Here is the link for you to check it out: Thanks to the CopilotKit team for partnering with me on this post.

Santiago

17,438 просмотров • 3 месяцев назад