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Here's a clip from my upcoming AI SDK tutorial: We're building what Anthropic calls an 'agent' in TypeScript. Can it react to results of tool calls, deciding when it's completed the task? It's an agent.

23,015 次观看 • 1 年前 •via X (Twitter)

12 条评论

Sahaj Jain 的头像
Sahaj Jain1 年前

Will surely demystify concepts like Tools, RAG and Agents. Looking forward to the launch

Greg Caplan 🚀 的头像
Greg Caplan 🚀2 年前

Stop wasting time following up with leads. Let our AI agents do it for you.

Bartek 的头像
Bartek1 年前

I don't want to cherry pick, but when I'm looking out the window it's definitely not 25C 🙃. Is it London, UK? I'm building my own agents pipeline, it's very fun way of using AIs.

Mike 的头像
Mike1 年前

Is the code base open source?

Matt Pocock 的头像
Matt Pocock1 年前

Will be when the tutorial launches!

Gabriel Ducharme 的头像
Gabriel Ducharme1 年前

Great, can wait for the tutorial!

Steve Fernandes 的头像
Steve Fernandes1 年前

is this a part of the course or a youtube tutorial?

Matt Pocock 的头像
Matt Pocock1 年前

Free tutorial, which I'll release on YT too

Charlie Cowan 的头像
Charlie Cowan1 年前

Really neat explanation Matt. Look forward to the YT series.

🇺🇸b0nez_dv🇺🇸 的头像
🇺🇸b0nez_dv🇺🇸1 年前

I am patiently waiting for this!

Ethan_AI Marketer for 𝕏 的头像
Ethan_AI Marketer for 𝕏1 年前

How do you see the agent's ability to react to tool call results impacting its overall efficiency? It seems like a game-changer for automating tasks, but I'm curious about real-world applications.

Matt Pocock 的头像
Matt Pocock1 年前

If an agent can query the real world, it's more likely to make smarter decisions.

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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 次观看 • 1 个月前