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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... we do the human interface." "If you have a hundred or a thousand times more agents than people, then your software has to be built for agents… it's going to be through an API or a CLI or MCP." "What if you give a coding agent access to your SaaS tools and a coding agent access to your knowledge work, workflows and context?" "It can actually code its way or use APIs through whatever task it's trying to achieve." "That appears to be like a paradigm that is starting to compound… and I actually think it kind of makes sense as the ultimate manifestation of this stuff." Aaron Levieshow more

a16z

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42,152 次观看 • 18 天前 •via X (Twitter)

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28 条评论

suqitah 的头像

suqitah18 天前

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

Ehsan Azish 的头像

Ehsan Azish18 天前

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

Nico 的头像

Nico18 天前

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

Anton Viborniy 的头像

Anton Viborniy18 天前

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 的头像

elian18 天前

sure, aaron, whatever you say

The AI Therapist 的头像

The AI Therapist18 天前

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 的头像

Phong Designs AI Systems18 天前

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 的头像

Dave Burrells17 天前

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 的头像

Adel Bucetta17 天前

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 🦊 🇮🇷 的头像

EKOS _ AGI 🦊 🇮🇷17 天前

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 的头像

Dave Mellish18 天前

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

Rory Bernier 的头像

Rory Bernier18 天前

damn

Ella Tech & Tool 的头像

Ella Tech & Tool18 天前

Agents humans soon 🔥 1000x scale is wild

broadfield-dev 的头像

broadfield-dev18 天前

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

EKOS _ AGI 🦊 🇮🇷 的头像

EKOS _ AGI 🦊 🇮🇷17 天前

@ekosproject

EVR 的头像

EVR18 天前

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 的头像

AiClaw18 天前

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 的头像

AscendCapital18 天前

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 的头像

Doug18 天前

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 的头像

Leo Lu18 天前

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

Voriq Network 的头像

Voriq Network16 天前

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 的头像

TriDung.sol 🐬TermMax18 天前

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

Macro Bombastic 的头像

Macro Bombastic18 天前

yeah the api is becoming the real frontend for agents

OctoOps - Agent Ops inPublic 的头像

OctoOps - Agent Ops inPublic18 天前

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 的头像

Bobby18 天前

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

Jay Dev 的头像

Jay Dev18 天前

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 的头像

dhinna ship .ico18 天前

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

mcdeezy 的头像

mcdeezy17 天前

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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Y Combinator

38,133 次观看 • 7 个月前

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 次观看 • 1 年前

.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

.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 次观看 • 1 个月前

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.

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 次观看 • 5 个月前

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:

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 次观看 • 5 个月前

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.

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 次观看 • 1 年前