Video wird geladen...

Video konnte nicht geladen werden

Zur Startseite

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 Aufrufe • vor 1 Jahr •via X (Twitter)

12 Kommentare

Profilbild von Sahaj Jain
Sahaj Jainvor 1 Jahr

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

Profilbild von Greg Caplan 🚀
Greg Caplan 🚀vor 2 Jahren

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

Profilbild von Bartek
Bartekvor 1 Jahr

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.

Profilbild von Mike
Mikevor 1 Jahr

Is the code base open source?

Profilbild von Matt Pocock
Matt Pocockvor 1 Jahr

Will be when the tutorial launches!

Profilbild von Gabriel Ducharme
Gabriel Ducharmevor 1 Jahr

Great, can wait for the tutorial!

Profilbild von Steve Fernandes
Steve Fernandesvor 1 Jahr

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

Profilbild von Matt Pocock
Matt Pocockvor 1 Jahr

Free tutorial, which I'll release on YT too

Profilbild von Charlie Cowan
Charlie Cowanvor 1 Jahr

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

Profilbild von 🇺🇸b0nez_dv🇺🇸
🇺🇸b0nez_dv🇺🇸vor 1 Jahr

I am patiently waiting for this!

Profilbild von Ethan_AI Marketer for 𝕏
Ethan_AI Marketer for 𝕏vor 1 Jahr

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.

Profilbild von Matt Pocock
Matt Pocockvor 1 Jahr

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

Ähnliche Videos

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 Aufrufe • vor 1 Monat