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AI power users are here and the case for agent-friendly software is materializing. Aaron Levie on agents outnumbering people 1000x: "If you start to imagine that we all have to build software for agents… we spend as much time now thinking about the agent interface to our tool as...

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42,152 görüntüleme • 18 gün önce •via X (Twitter)

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suqitah profil fotoğrafı
suqitah18 gün önce

coding its way through whatever task it needs is exactly the line that should make security teams nervous

Ehsan Azish profil fotoğrafı
Ehsan Azish18 gün önce

an agent using apis through whatever task it needs is just a script with worse error handling

Nico profil fotoğrafı
Nico18 gün önce

Agent friendly usually means an API you already had, documented properly

Anton Viborniy profil fotoğrafı
Anton Viborniy18 gün önce

The interesting part is that agents may kill the UI moat. If an agent interacts with every SaaS through APIs, the product with the best interface no longer necessarily wins. The product with the best infrastructure does.

elian profil fotoğrafı
elian18 gün önce

sure, aaron, whatever you say

The AI Therapist profil fotoğrafı
The AI Therapist18 gün önce

Aaron Levie wants software built for agents. The real bottleneck isn't building it, it's the average human tolerance for being managed by a bot that hallucinates your name twice before lunch.

Phong Designs AI Systems profil fotoğrafı
Phong Designs AI Systems18 gün önce

The 1000x number changes what a feature costs, not just who uses it. Human traffic follows attention, so a rough edge on a rare path stays cheap because few people ever reach it. An agent reads the docs and exercises every path uniformly. The long tail stops being a tail.

Dave Burrells profil fotoğrafı
Dave Burrells17 gün önce

If agents outnumber people 1,000 to 1, what do they do after they can call an API? Talking to software is coordination. An economy starts when they can price a job, settle it, and carry a reputation for the outcome. That’s the piece after “more agents.” 👇

Adel Bucetta profil fotoğrafı
Adel Bucetta17 gün önce

the honest answer is, most companies still haven't figured out how to design for human-machine collaboration. till then, agent-friendly interfaces will keep being a kludge

EKOS _ AGI 🦊 🇮🇷 profil fotoğrafı
EKOS _ AGI 🦊 🇮🇷17 gün önce

This is where the agent economy gets interesting. When agents become the primary software users, reliable machine-readable context becomes infrastructure. @ekosproject is building exactly that layer for enterprise knowledge.

Dave Mellish profil fotoğrafı
Dave Mellish18 gün önce

So are we still bearish on knowledge work replacement at scale?

Rory Bernier profil fotoğrafı
Rory Bernier18 gün önce

damn

Ella Tech & Tool profil fotoğrafı
Ella Tech & Tool18 gün önce

Agents humans soon 🔥 1000x scale is wild

broadfield-dev profil fotoğrafı
broadfield-dev18 gün önce

If you are still using other peoples software, you are not an AI power user. Entry level at best.

EKOS _ AGI 🦊 🇮🇷 profil fotoğrafı
EKOS _ AGI 🦊 🇮🇷17 gün önce

@ekosproject

EVR profil fotoğrafı
EVR18 gün önce

If agents outnumber people 1000x, they cannot share one laptop, one filesystem, and one set of credentials. Isolated compute is the actual interface. A VM per agent, or the blast radius scales with the agents.

AiClaw profil fotoğrafı
AiClaw18 gün önce

The API, CLI and MCP point is the practical tell: when agents become the main users, documentation stops being a nice-to-have and becomes part of the product.

AscendCapital profil fotoğrafı
AscendCapital18 gün önce

Most product teams are still treating APIs as an afterthought while building for human UI. If agents are outnumbering users 1000x, your API and context layer are your primary product surface area.

Doug profil fotoğrafı
Doug18 gün önce

This is the bigger shift: software won’t just need a good human interface. It’ll need a good interface for machines that can actually use it.

Leo Lu profil fotoğrafı
Leo Lu18 gün önce

Agent interfaces may end up looking more like API contracts than UIs.

Voriq Network profil fotoğrafı
Voriq Network16 gün önce

Would love to collab by providing an infrastructure for your agents and the proper tools and SDKs Is this something we can discuss with the right person at a16z ?

TriDung.sol 🐬TermMax profil fotoğrafı
TriDung.sol 🐬TermMax18 gün önce

the ui for humans is becoming the legacy layer, api is the new focus

Macro Bombastic profil fotoğrafı
Macro Bombastic18 gün önce

yeah the api is becoming the real frontend for agents

OctoOps - Agent Ops inPublic profil fotoğrafı
OctoOps - Agent Ops inPublic18 gün önce

1000x agents means 1000x rate limits and quota fights. agent-friendly software mostly means idempotent writes and a spend ceiling per caller, not a prettier API.

Bobby profil fotoğrafı
Bobby18 gün önce

The future is bright for America and her allies. Semper Fidelis

Jay Dev profil fotoğrafı
Jay Dev18 gün önce

Building the agent interface as a co-equal to the human interface is the part most teams still bolt on last. At 1000x the agent count, does that ratio flip which interface gets the primary design pass first?

dhinna ship .ico profil fotoğrafı
dhinna ship .ico18 gün önce

Implies separate schemas and action endpoints, not a wrapped human UI

mcdeezy profil fotoğrafı
mcdeezy17 gün önce

was built for agents I had never built a CLI before but it was obvious that agents would interact with the opensource code via CLI very fun coding and architecting for agents instead of the normal human which color should i make the button hmmm 🤔🤔🤔

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This is probably the biggest news yet in software going headless, and will bring knowledge work agents to the masses. The new ChatGPT agents have access to any of the tools and data you want to work with, with complete coding and tool use available to them. Here's an example of a custom sales assistant agent uses Box as a knowledge source for accessing enterprise content securely to answer questions and generate new content on the fly. The workflows can obviously vastly far more complex as the agent can use any of the tools within Box available via MCP and CLI. This precisely what agents will start to look like for knowledge work. You'll be able to spin them up in the foreground or background to help augment work. Big opportunity right now for headless platforms, and for all the new builders and designers of these agents in the enterprise.
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Bash is all you need! Which is why I'm introducing my holiday project: just-bash just-bash is a pretty complete implementation of bash in TypeScript designed to be used as a bash tool by AI agents. Because it turns out agents love exploring data via shell scripts, even beyond coding. It comes with grep, sed, awk and the 99th percentile features that an agent like Claude Code or Cursor would use. In fact, Claude Code can use it for secure bash execution. In the package - A bash-tool for AI SDK - A binary for use by yourself or your coding agents - An overlay filesystem to feed files to your agent securely - A Vercel Sandbox compatible API, so you can quickly upgrade to a real VM if you need to run binaries - An example AI agent that explores the just-bash code base using just-bash - I imported the Oils shell bash compatibility suite and just-bash passes a very good chunk What is interesting about this codebase: It was essentially entirely written by Opus 4.5. Coding agents love bash and they are good at reproducing it. They are also great at text-book recursive descent parsers and AST tweet-walk interpreters. That said, it is, like, a lot of code and I didn't read it all 😅. This is very much a hack, but it also seems to be _really_ useful. I haven't really found anything agents want to use that it doesn't support and it's fast and secure (caveats apply). It doesn't have write access to your computer and the filesystem is given a root that the agent cannot escape from. Find it at Related: Our recent blog post how we migrated our data analysis agent to bash tools and achieved incredible quality improvements The video shows the example agent investigating the just-bash code base

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Anish Acharya: We're going to see a "YouTube moment for software": "If you think about YouTube 20 years ago—we had lots of video and lots of television, and it was high production quality, and it wasn't clear that we needed more and 20 years later, YouTube's a $550 billion enterprise that would be one of the biggest companies in the world if it was independent." "I think the same thing is going to happen for software. People want to make software, and for the first time they can—and they can distribute it and they can consume it." "Sometimes it's going to be important software. Sometimes it's going to be totally trivial. It's going to be software for a bachelor party weekend, software for a joke, software for a prompt. We have this sort of seriousness about software that we had about video and television 20 years ago." "Now it's like—I just took a video on my phone. It's going to be like—I just made an app on my phone. Same energy." Anish Acharya on BILLIONS with Guillaume Moubeche

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APIs weren’t built for AI agents. Orthogonal (YC W26) fixes that. Agents and developers get instant access to any API on our marketplace via a single MCP or SDK. Orthogonal handles your API keys and payments, so your agent doesn’t have to. Pay as you go. Congrats on the launch Christian Pickett, Bera Sogut

Y Combinator

38,133 görüntüleme • 7 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:
2:49

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

.Lassie co-founder and CEO Steijn Pelle says small businesses like doctors' offices should run themselves: "The busy work is done by agents." "We want to build agents for the business that then will interface with the personal agent of a consumer." "It needs to really work, and the onboarding needs to be as simple as onboarding on Coinbase or Stripe." "All small businesses at an abstract level have a system of record they need to read and write into. They all have customers... it's interacting and transacting around payments. You need to book appointments." "The end goal is if we served all the doctor offices, that we help all the small businesses across the world." steijnpelle
1:40

.Lassie co-founder and CEO Steijn Pelle says small businesses like doctors' offices should run themselves: "The busy work is done by agents." "We want to build agents for the business that then will interface with the personal agent of a consumer." "It needs to really work, and the onboarding needs to be as simple as onboarding on Coinbase or Stripe." "All small businesses at an abstract level have a system of record they need to read and write into. They all have customers... it's interacting and transacting around payments. You need to book appointments." "The end goal is if we served all the doctor offices, that we help all the small businesses across the world." steijnpelle

a16z

34,504 görüntüleme • 1 ay önce

The ceo of Box says if you're not building for agents, you won't exist as a software company in 2-3 years. Aaron Levie recalled how in the 90s, it was mainframes built for institutions. If you didn't build for the CIO or IT org, you didn't exist. You could build software for users, but no one cared. Similarly, in the mid 2000s, it was software built for end users. If you didn't take that route, you got completely trounced. The likes of Yammer, Slack, Zoom, etc., ripped through enterprises with viral adoption. Bottom-up and completely user driven. And now it's software built for agents. We're going to have trillions of agent runs. The systems that optimize for agents the most will win. - lower latency - better API exposure - better system intelligence - serves the right information when called Agents will eventually choose their own tools on the fly and make purchasing decisions at runtime. All based on past experiences/online reviews/performance, sans any procurement process. The more pragmatic version is companies swapping out bad tools / tech that don't work well with agents. - crm doesn't expose the right APIs - auth can't handle agent scale requests - database has too much latency for agent workloads Being good enough for humans won't work because they have to pass the litmus test for agents to make the cut.
2:15

The ceo of Box says if you're not building for agents, you won't exist as a software company in 2-3 years. Aaron Levie recalled how in the 90s, it was mainframes built for institutions. If you didn't build for the CIO or IT org, you didn't exist. You could build software for users, but no one cared. Similarly, in the mid 2000s, it was software built for end users. If you didn't take that route, you got completely trounced. The likes of Yammer, Slack, Zoom, etc., ripped through enterprises with viral adoption. Bottom-up and completely user driven. And now it's software built for agents. We're going to have trillions of agent runs. The systems that optimize for agents the most will win. - lower latency - better API exposure - better system intelligence - serves the right information when called Agents will eventually choose their own tools on the fly and make purchasing decisions at runtime. All based on past experiences/online reviews/performance, sans any procurement process. The more pragmatic version is companies swapping out bad tools / tech that don't work well with agents. - crm doesn't expose the right APIs - auth can't handle agent scale requests - database has too much latency for agent workloads Being good enough for humans won't work because they have to pass the litmus test for agents to make the cut.

Ivan Burazin

22,655 görüntüleme • 5 ay önce

Today we open the Zapier SDK to everyone. If you're building with AI agents, this is for you. I've been using this for 2 months. It's totally changed how I do my job. You install it in your coding agent. Cursor, Claude Code, Codex, whatever you use. Now that agent has access to 8,000+ apps through Zapier and can do anything those APIs can do. I think it’s the most powerful thing we’ve launched in years. Now in open beta. Just give this link right to your agent:
2:47

Today we open the Zapier SDK to everyone. If you're building with AI agents, this is for you. I've been using this for 2 months. It's totally changed how I do my job. You install it in your coding agent. Cursor, Claude Code, Codex, whatever you use. Now that agent has access to 8,000+ apps through Zapier and can do anything those APIs can do. I think it’s the most powerful thing we’ve launched in years. Now in open beta. Just give this link right to your agent:

Wade Foster

175,178 görüntüleme • 5 ay önce

Google’s new Agent2Agent protocol is a key step toward a future of AI Agent interoperability. No single tool has all the data a user or business needs for most workflows, so we need Agents to be able to talk to each other. Salesforce will have AI Agents that understand the inner workings of CRM, Workday will have AI Agents that understand HR workflows, Box has AI Agents that understand content and documents, and so on. It’s easy to conceive of a world where we have tens of thousands of these tool Agents and then billions or trillions of customized Agents that are extensions of those. So openness becomes key. Workflows need data from multiple systems to complete the task — like a sales report that needs documents and CRM data or an HR task that needs HR policies and employee details — and that’s where A2A comes in. This protocol gives AI Agent providers a way of talking to each other and simplifying the way that Agents communicate, understand each other’s capabilities, and so on. It’s great to see more interoperability, along with MCP, emerging in the AI space right now, as this will define the future of most IT stacks.
1:47

Google’s new Agent2Agent protocol is a key step toward a future of AI Agent interoperability. No single tool has all the data a user or business needs for most workflows, so we need Agents to be able to talk to each other. Salesforce will have AI Agents that understand the inner workings of CRM, Workday will have AI Agents that understand HR workflows, Box has AI Agents that understand content and documents, and so on. It’s easy to conceive of a world where we have tens of thousands of these tool Agents and then billions or trillions of customized Agents that are extensions of those. So openness becomes key. Workflows need data from multiple systems to complete the task — like a sales report that needs documents and CRM data or an HR task that needs HR policies and employee details — and that’s where A2A comes in. This protocol gives AI Agent providers a way of talking to each other and simplifying the way that Agents communicate, understand each other’s capabilities, and so on. It’s great to see more interoperability, along with MCP, emerging in the AI space right now, as this will define the future of most IT stacks.

Aaron Levie

139,591 görüntüleme • 1 yıl önce