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"if i can interface with everything through my agent, why would i go to the dashboard?" i sat down with sarah, mts at Composio, at the Browserbase AI Engineer booth. fresh off her AIE talk "dashboards are dead," we got into: - why agent experience is the next wave... show more

justine

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16,112 görüntüleme • 1 ay önce •via X (Twitter)

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"Intelligence isn't the boundary anymore. Safety is what's actually constraining agents." I had a great conversation with tusharjain, Head of Engineering at Docker, at our AI Engineer booth. Tushar leads engineering at Docker, the containerization platform millions of developers use to package and ship software, and his team is now rebuilding that engine into a runtime for AI agents with safety built in. Fresh off his talk, we got into: 00:57 - Why safety, not intelligence, is the real constraint on agents 02:00 - The three pillars: contain agents, scope their tools, layer on policy 04:04 - Background agents and the "laptops half open so the agent doesn't die" problem 08:13 - Going 100% agents on Docker's own engineering team 11:28 - Agent identity: you can fire a human, but not their agent Thanks for coming to the Browserbase booth!

"Intelligence isn't the boundary anymore. Safety is what's actually constraining agents." I had a great conversation with tusharjain, Head of Engineering at Docker, at our AI Engineer booth. Tushar leads engineering at Docker, the containerization platform millions of developers use to package and ship software, and his team is now rebuilding that engine into a runtime for AI agents with safety built in. Fresh off his talk, we got into: 00:57 - Why safety, not intelligence, is the real constraint on agents 02:00 - The three pillars: contain agents, scope their tools, layer on policy 04:04 - Background agents and the "laptops half open so the agent doesn't die" problem 08:13 - Going 100% agents on Docker's own engineering team 11:28 - Agent identity: you can fire a human, but not their agent Thanks for coming to the Browserbase booth!

Shubhankar

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"Voice agents are built to be helpful. The problem is they can be too helpful and cross the boundary." I sat down with Sumanyu Sharma 🍫, Founder and CEO of Hamming, at our AI Engineer booth. Hamming stress-tests and monitors voice agents so the bank or hospital running one catches failures before a customer does. Fresh after his talk “I Monitored Crime Audio. Voice Agents Scare Me More,” we got into: 0:00 how Sumanyu went from listening to 911 calls at Citizen to founding Hamming AI 0:39 why he's more nervous about voice agents than he was about crime 1:51 the first voice-agent security product 3:16 simulating messy reality: accents, background noise, code-switching 4:11 voice agents testing voice agents 5:47 why taking actions is table stakes, and what makes it dangerous 11:07 hot take: everyone rushed to B2B, more founders should build consumer Thanks for coming to our Browserbase booth, Sumanyu Sharma 🍫! Watch the full episode here:

"Voice agents are built to be helpful. The problem is they can be too helpful and cross the boundary." I sat down with Sumanyu Sharma 🍫, Founder and CEO of Hamming, at our AI Engineer booth. Hamming stress-tests and monitors voice agents so the bank or hospital running one catches failures before a customer does. Fresh after his talk “I Monitored Crime Audio. Voice Agents Scare Me More,” we got into: 0:00 how Sumanyu went from listening to 911 calls at Citizen to founding Hamming AI 0:39 why he's more nervous about voice agents than he was about crime 1:51 the first voice-agent security product 3:16 simulating messy reality: accents, background noise, code-switching 4:11 voice agents testing voice agents 5:47 why taking actions is table stakes, and what makes it dangerous 11:07 hot take: everyone rushed to B2B, more founders should build consumer Thanks for coming to our Browserbase booth, Sumanyu Sharma 🍫! Watch the full episode here:

justine

18,048 görüntüleme • 28 gün önce

there's a lot of content around AI agents but no one is showing you how to actually design them to work reliably that's why I made a full walkthrough breaking down how I think through agent design, using a contract chatbot as an example... inside, I cover: – why I prefer static workflows over free-roaming agents – how I use multiple choice prompts to control next steps – the exact guardrails I use to avoid hallucinations – how the agent answers legal questions and structures outputs into Supabase if you want to see how I actually build agents that don’t break like + reply “agent” and I’ll DM it to you (must be following)

there's a lot of content around AI agents but no one is showing you how to actually design them to work reliably that's why I made a full walkthrough breaking down how I think through agent design, using a contract chatbot as an example... inside, I cover: – why I prefer static workflows over free-roaming agents – how I use multiple choice prompts to control next steps – the exact guardrails I use to avoid hallucinations – how the agent answers legal questions and structures outputs into Supabase if you want to see how I actually build agents that don’t break like + reply “agent” and I’ll DM it to you (must be following)

Tyler

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Fable 5 is gone, but agents are here to stay. I sat down with Nevo who shared the playbook he used to hit $118k/month building for agents: > The one file that makes you agent-ready (1:15) > Why agents prefer a CLI to your API (2:23) > The X article growth hack (3:24)

Fable 5 is gone, but agents are here to stay. I sat down with Nevo who shared the playbook he used to hit $118k/month building for agents: > The one file that makes you agent-ready (1:15) > Why agents prefer a CLI to your API (2:23) > The X article growth hack (3:24)

Rob Hallam

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"to me, it's not about the human in the loop, but the human at the center." i sat down with Stefania Druga, staff research scientist Sakana AI, at our Browserbase booth at AI Engineer. after Massachusetts Institute of Technology (MIT) and Google DeepMind, stefania recently joined sakana's recursive self-improvement (rsi) lab in tokyo to build self-improving AI for the next frontier. we got into: - catastrophic forgetting in day-long agent research runs, and how memory-first harnesses help - sovereign AI and open-weights: building on models you control instead of locking into one provider - why computer use still frustrates people even as models get smarter - the domains ripe for self-improving research agents: math, genomics, rare conditions - my fav moment at the end: what tasks are better left human, and what are we going to do as humans after everything is automated? with open frontier models arriving fast, Stefania Druga's case for sovereign AI only gets stronger. loved this conversation. thanks for joining us at the Browserbase booth!

"to me, it's not about the human in the loop, but the human at the center." i sat down with Stefania Druga, staff research scientist Sakana AI, at our Browserbase booth at AI Engineer. after Massachusetts Institute of Technology (MIT) and Google DeepMind, stefania recently joined sakana's recursive self-improvement (rsi) lab in tokyo to build self-improving AI for the next frontier. we got into: - catastrophic forgetting in day-long agent research runs, and how memory-first harnesses help - sovereign AI and open-weights: building on models you control instead of locking into one provider - why computer use still frustrates people even as models get smarter - the domains ripe for self-improving research agents: math, genomics, rare conditions - my fav moment at the end: what tasks are better left human, and what are we going to do as humans after everything is automated? with open frontier models arriving fast, Stefania Druga's case for sovereign AI only gets stronger. loved this conversation. thanks for joining us at the Browserbase booth!

justine

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One click away, and a new world starts to unfold.💫 What looks like a small extension is actually the starting point for a personal AI agent that can search, think, and act with you. That is what we are building at OptimAI: live context, autonomous execution, and agents that feel native to the way people actually work. Not a demo. Not a gimmick. A new interface for the agent-native internet. Build your own personal AI agent:

One click away, and a new world starts to unfold.💫 What looks like a small extension is actually the starting point for a personal AI agent that can search, think, and act with you. That is what we are building at OptimAI: live context, autonomous execution, and agents that feel native to the way people actually work. Not a demo. Not a gimmick. A new interface for the agent-native internet. Build your own personal AI agent:

OptimAI Network

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I'm excited to share my talk on building Agentic RAG systems at last week's event with Arize AI and Google! 🥳 My talk covered: 1. The differences between Vanilla RAG and Agentic RAG 2. The agent ecosystem and how you can build agents today 3. How Weaviate AI Database is building agents with Generative Feedback Loops (GFLs) This snippet presents the multi-agent paradigm for Agentic RAG. I hope you find this talk interesting and would love to know what you think! The slide deck and full video links are below.

I'm excited to share my talk on building Agentic RAG systems at last week's event with Arize AI and Google! 🥳 My talk covered: 1. The differences between Vanilla RAG and Agentic RAG 2. The agent ecosystem and how you can build agents today 3. How Weaviate AI Database is building agents with Generative Feedback Loops (GFLs) This snippet presents the multi-agent paradigm for Agentic RAG. I hope you find this talk interesting and would love to know what you think! The slide deck and full video links are below.

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I wanted to share a quick demo of what we've been working on with our ai agent cloud. This enables fast deployment of agents that have access to a suite of tools, and was designed with agent interoperability in mind. This demo shows how you can go from nothing to an AI twitter agent in a couple minutes. This is what we are using internally to manage deployments, so we will consistently upgrading its capabilities. The next goals are to enable simple TEE deployments for agents, and focus on building out feature for agent interoperability to simplify agent to agent collaboration.

Johnny

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Congrats to the Dreamer team on their launch today! I got the chance to sit down with David Singleton and Hugo Barra for a deep chat on personal agents, building on the Claude Agent SDK, and their startup journey. We could have talked for hours!

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Mike Krieger

140,704 görüntüleme • 6 ay önce

🚀 New LangChain Academy Course: Building Reliable Agents 🚀 Shipping agents to production is hard. Traditional software is deterministic – when something breaks, you check the logs and fix the code. But agents rely on non-deterministic models. Add multi-step reasoning, tool use, and real user traffic, and building reliable agents becomes far more complex than traditional system design. The goal of this course is to teach you how to take an agent from first run to production-ready system through iterative cycles of improvement. You’ll learn how to do this with LangSmith, our agent engineering platform for observing, evaluating, and deploying agents. Enroll for free ➡️

🚀 New LangChain Academy Course: Building Reliable Agents 🚀 Shipping agents to production is hard. Traditional software is deterministic – when something breaks, you check the logs and fix the code. But agents rely on non-deterministic models. Add multi-step reasoning, tool use, and real user traffic, and building reliable agents becomes far more complex than traditional system design. The goal of this course is to teach you how to take an agent from first run to production-ready system through iterative cycles of improvement. You’ll learn how to do this with LangSmith, our agent engineering platform for observing, evaluating, and deploying agents. Enroll for free ➡️

LangChain

30,744 görüntüleme • 5 ay önce

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

105,343 görüntüleme • 1 yıl önce

"We are clearly entering a world where a product-minded engineer is now empowered to produce software without writing a line of code for it." In this conversation, Temporal CEO Samar Abbas joins a16z GPs Sarah Wang and Raghu Raghuram to cover: - Why agents are going from short-lived and interactive to long-running and async - Why the engineer of the future manages 15 parallel AI tasks at once - How OpenAI Codex runs millions of concurrent agent executions on Temporal - How real-time context engineering is exploding on Temporal - Why SaaS isn't dead, and value is migrating to APIs 00:00 Introduction 04:03 Temporal's origin story 11:14 Why agents raise the stakes 16:00 Specialized agents need durable RPC 25:20 Deep research agents 30:58 Execution histories as a superpower 39:04 Minimal viable long-running agent architecture 45:07 Context engineering at scale 52:40 Where value accrues: The "five-layer cake" and breakout AI applications Samar Abbas Sarah Wang Raghu Raghuram

"We are clearly entering a world where a product-minded engineer is now empowered to produce software without writing a line of code for it." In this conversation, Temporal CEO Samar Abbas joins a16z GPs Sarah Wang and Raghu Raghuram to cover: - Why agents are going from short-lived and interactive to long-running and async - Why the engineer of the future manages 15 parallel AI tasks at once - How OpenAI Codex runs millions of concurrent agent executions on Temporal - How real-time context engineering is exploding on Temporal - Why SaaS isn't dead, and value is migrating to APIs 00:00 Introduction 04:03 Temporal's origin story 11:14 Why agents raise the stakes 16:00 Specialized agents need durable RPC 25:20 Deep research agents 30:58 Execution histories as a superpower 39:04 Minimal viable long-running agent architecture 45:07 Context engineering at scale 52:40 Where value accrues: The "five-layer cake" and breakout AI applications Samar Abbas Sarah Wang Raghu Raghuram

a16z

45,989 görüntüleme • 6 ay önce

Let me explain why I launched $KEYS. CA: 3BSoYDZsPupZ15iBzJbJJjZKLiyWEqLxexJX84rBpump is the first SocialFi experience for AI agents. It’s a project that’s very special to me that I’ve been working on daily for the last four months. Right now, everyone is building their own Agent, completely silo to their own computer or Mac mini. Transferring that information agent-to-agent in a simple, structured, and easy form is not possible right now. This is why I built I thought a lot about whether or not to launch an official token for Agent Keys and how I wanted to do it. The only way I really wanted to do it was if it can benefit the platform by increasing exposure and creating an increase in users. So I’ve decided to buy 50% of the supply to AirDrop directly to users over the next six months. Each AirDrop will happen at the end of each month, with the first AirDrop happening at the end of June. More details on how each AirDrop will work soon. Some of you may be wondering about the future of $OSHI. Oshi got us into the Bags hackathon and we won. $OSHI token will still be the home for all my AI Agents and runs as the showcase of how an agent with a token can properly integrate it into AgentKeys, a live example of the platform doing exactly what it was built to do. So welcome to Agent Keys, the first ever airdrop for AI agents. Sign up your agent today on and start collecting KeyCards today!

Let me explain why I launched $KEYS. CA: 3BSoYDZsPupZ15iBzJbJJjZKLiyWEqLxexJX84rBpump is the first SocialFi experience for AI agents. It’s a project that’s very special to me that I’ve been working on daily for the last four months. Right now, everyone is building their own Agent, completely silo to their own computer or Mac mini. Transferring that information agent-to-agent in a simple, structured, and easy form is not possible right now. This is why I built I thought a lot about whether or not to launch an official token for Agent Keys and how I wanted to do it. The only way I really wanted to do it was if it can benefit the platform by increasing exposure and creating an increase in users. So I’ve decided to buy 50% of the supply to AirDrop directly to users over the next six months. Each AirDrop will happen at the end of each month, with the first AirDrop happening at the end of June. More details on how each AirDrop will work soon. Some of you may be wondering about the future of $OSHI. Oshi got us into the Bags hackathon and we won. $OSHI token will still be the home for all my AI Agents and runs as the showcase of how an agent with a token can properly integrate it into AgentKeys, a live example of the platform doing exactly what it was built to do. So welcome to Agent Keys, the first ever airdrop for AI agents. Sign up your agent today on and start collecting KeyCards today!

fxnction

27,570 görüntüleme • 2 ay önce

Our latest LangChain Academy course – Building Ambient Agents with LangGraph – is now available! Most agents today handle one request at a time through chat interfaces. But as models have improved, agents can now run in the background – and take on long-running, complex tasks. LangGraph is built for these “ambient agents,” with support for human-in-the-loop workflows and memory. LangGraph Platform provides the infrastructure to run these agents at scale, and LangSmith helps you observe, evaluate, and improve your agents. Together, they make it easier to build reliable, production-ready agents. In this course, you’ll: 📩 Learn the fundamentals of LangGraph and how to build your own email agent 📊 Evaluate your agent using LangSmith ✅ Add human-in-the-loop for reviews, and memory so your agent adapts over time 🚀 Deploy your agent with LangGraph Platform and connect it to Gmail

Our latest LangChain Academy course – Building Ambient Agents with LangGraph – is now available! Most agents today handle one request at a time through chat interfaces. But as models have improved, agents can now run in the background – and take on long-running, complex tasks. LangGraph is built for these “ambient agents,” with support for human-in-the-loop workflows and memory. LangGraph Platform provides the infrastructure to run these agents at scale, and LangSmith helps you observe, evaluate, and improve your agents. Together, they make it easier to build reliable, production-ready agents. In this course, you’ll: 📩 Learn the fundamentals of LangGraph and how to build your own email agent 📊 Evaluate your agent using LangSmith ✅ Add human-in-the-loop for reviews, and memory so your agent adapts over time 🚀 Deploy your agent with LangGraph Platform and connect it to Gmail

LangChain

33,192 görüntüleme • 1 yıl önce

if you are going to the #AIEngineer conference in SF this week you have a huge problem... there are over 500 events across 5 parallel tracks, how are you going to decide which events are the best ones to go to? Malay cooked again. We built an agent with Composio that will search for the best events and add them directly to your calendar. go to to start building your calendar! oh and I am giving at talk thursday evening "Dashboards are dead: AX is the new UX, and how to build for agents." hope to see you there! comment with the events you are going to I am still undecided 👀

if you are going to the #AIEngineer conference in SF this week you have a huge problem... there are over 500 events across 5 parallel tracks, how are you going to decide which events are the best ones to go to? Malay cooked again. We built an agent with Composio that will search for the best events and add them directly to your calendar. go to to start building your calendar! oh and I am giving at talk thursday evening "Dashboards are dead: AX is the new UX, and how to build for agents." hope to see you there! comment with the events you are going to I am still undecided 👀

sarah

13,637 görüntüleme • 1 ay önce

“XRPL’s cheap fees and instant finality make it extremely exceptional for AI agents.” - Shen Morincome Our CEO Shen speaking on the Next Wave: AI + XRPL panel at Wave of Innovation. AI agents trade 24/7 and far more frequently than humans. That’s why we believe the XRP Ledger is uniquely positioned for the agent economy within the Ripple ecosystem. Gen3 Labs is building for that future.

“XRPL’s cheap fees and instant finality make it extremely exceptional for AI agents.” - Shen Morincome Our CEO Shen speaking on the Next Wave: AI + XRPL panel at Wave of Innovation. AI agents trade 24/7 and far more frequently than humans. That’s why we believe the XRP Ledger is uniquely positioned for the agent economy within the Ripple ecosystem. Gen3 Labs is building for that future.

Gen3

30,652 görüntüleme • 5 ay önce

when I look at my old simulations, a lot of them, most of them, can be easily vibe setup with agents, or even one shot by the agents. it makes me wonder how many of the ideas and devices that were results of branching off of originally intended directions and goals would I miss if I attempt them now with the agents? How many new ones would the agents be able to attempt and identify? Is inverse design just the most stupid and bruteforce version of auto research? i should get a workflow with MOOSE working so I could set the agents free in the world of multiphysics simulations.

when I look at my old simulations, a lot of them, most of them, can be easily vibe setup with agents, or even one shot by the agents. it makes me wonder how many of the ideas and devices that were results of branching off of originally intended directions and goals would I miss if I attempt them now with the agents? How many new ones would the agents be able to attempt and identify? Is inverse design just the most stupid and bruteforce version of auto research? i should get a workflow with MOOSE working so I could set the agents free in the world of multiphysics simulations.

outside five sigma

63,383 görüntüleme • 4 ay önce

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.

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.

Santiago

18,715 görüntüleme • 1 ay önce