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Mercury's Command is probably the best designed agentic experience out there. Their design team sweats the smallest details. Take agent states for example. Most agentic products just display in plain text that the agent is thinking. But Mercury design team asked a different question. How do you know a... show more

Jayneil Dalal

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11,123 görüntüleme • 3 ay önce •via X (Twitter)

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Top designer at Mercury shows her agentic design explorations in Figma. Designers working on AI features should copy this and sketch how the LLM works before prototyping. 👀 Full conversation on Sneak Peek: carol shows her thought process for designing Mercury Command in Figma👇

Top designer at Mercury shows her agentic design explorations in Figma. Designers working on AI features should copy this and sketch how the LLM works before prototyping. 👀 Full conversation on Sneak Peek: carol shows her thought process for designing Mercury Command in Figma👇

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This is how a designer at Mercury does agentic design. Designers building agentic products should copy this design principle. Make users confirm in a deterministic way before any money movement happens. 👀 Full conversation on Sneak Peek: carol shows her thought process for designing Mercury's agentic features👇

This is how a designer at Mercury does agentic design. Designers building agentic products should copy this design principle. Make users confirm in a deterministic way before any money movement happens. 👀 Full conversation on Sneak Peek: carol shows her thought process for designing Mercury's agentic features👇

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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.

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.

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Type plain English on the command line. Accomplish any dev task. This is the command line for the AI era. New Agent Mode is available today.

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Airtable's Howie Liu says that basically everyone will need to graduate from being ICs to ICs that manage teams of 20-30 agents: "The best developers today don't just sit there in front of their IDEs and synchronously talk to their agent." "[Instead], you have like 30 separate branches that are each being worked on by a different agent. And you can have the agents continue to update the branches based on human and other agent feedback." "And I think this whole idea of it taking hours for that entire loop to complete — agent pushes some changes, the changes get feedback from other agents or humans, the agent responds to that — that whole loop could be hours, not just minutes. So you're not going to just sit there and watch it one at a time." "But the powerful thing about this is, each one is still actually operating faster than a human engineer. One agent on one branch can do the work of maybe three humans, operating 3x as fast. So it's like a 10x leverage factor just for one agent." "But the best engineers are now able to multitask and say, 'I'm going to oversee my own little team of 20-30 agents working concurrently.'" "Everyone needs to graduate from being an IC to an IC manager of agents. Meaning, if you're a VC analyst, your job should no longer be to go synchronously research one company. You need to go and research like 30 companies, and do them all faster, better, and higher quality than you could before." "That's the greatest leap that is going to be challenging for a lot of people in a lot of roles. Because it's a totally different mentality in how you operate, and what your role is."

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Kite Mainnet is now live. This is not just another mainnet. It is the first blockchain built for agentic payments. Designed for an agent-first internet, for builders, and for the future Agent Economy. What makes Kite Mainnet different: Built for builders and agents. Not chasing TVL. Not chasing the next DeFi cycle. Not competing for idle speculative capital. Three layers, one platform: • Settlement Layer: built for high-frequency, low-value, agent-driven stablecoin-native transactions. • Kite Agent Passport: unifies identity, delegated authority, and programmable governance. • Agent Interface & Experience: where agents and developers interact with the system through agent registration, agent harnesses, and service discovery. A unified hub for all major payment protocol standards, including x402, Google AP2, and Stripe MPP. Try Kite Agent Passport today: Join our agentic community: Let's build the rails for the Agent Economy.

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Shopify is the all-in-one commerce platform powering millions of businesses worldwide Thank you to the Shopify team for building their own official Hermes Agent skill enabling your agent to manage products, orders, inventory, and fulfillments from any channel.

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Nous Research

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Dragonfly GP Rob Hadick ＞|＜ says your AI agent may end up choosing your bank for you, and today it’s more likely to recommend Mercury than JPMorgan: "It seems quite unlikely to me that anything built on top of Plaid is going to be able to do a lot of mass movement on a regular basis of any sort of money." "What I think is more likely to be true is that you'll have a bank that's agent first." "If you ask your LLM what bank should you use that is agent friendly, they'll tell you Mercury. They won't tell you JPMorgan." "A financial institution that controls those different accounts and is able to monetize those different accounts, that is AI first and agent first, that is where you're likely to see a lot of this happen." Dragonfly ＞|＜

Dragonfly GP Rob Hadick ＞|＜ says your AI agent may end up choosing your bank for you, and today it’s more likely to recommend Mercury than JPMorgan: "It seems quite unlikely to me that anything built on top of Plaid is going to be able to do a lot of mass movement on a regular basis of any sort of money." "What I think is more likely to be true is that you'll have a bank that's agent first." "If you ask your LLM what bank should you use that is agent friendly, they'll tell you Mercury. They won't tell you JPMorgan." "A financial institution that controls those different accounts and is able to monetize those different accounts, that is AI first and agent first, that is where you're likely to see a lot of this happen." Dragonfly ＞|＜

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