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My 2025 year-end project: Solving Agents After weeks of stress testing agents, I landed on what a top-tier e2e agent actually looks like: Fast, Durable, Resumable, Secure Benchmarked. Battle-tested. Shipping the best patterns as reusable templates, packages and other OSS soon.

Chris Tate

22,293 subscribers

39,146 次观看 • 7 个月前 •via X (Twitter)

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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

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A builder wants to ship an agent that can search live data, use it instantly, and keep going without manual steps. That is why OptimAI Search now ships with x402, SDK, and MCP support. x402 makes access and payment seamless. SDK and MCP make integration straightforward for builders. And under the hood, OptimAI Search gives agents real-time access to huge volumes of public and social web data, structured into context they can actually use. Add Agent Marketplace on top, and domain knowledge becomes reusable: build specialized agents, publish them, and let others build on what you create. This is what search looks like in the agent-native internet. 💡Try it:

A builder wants to ship an agent that can search live data, use it instantly, and keep going without manual steps. That is why OptimAI Search now ships with x402, SDK, and MCP support. x402 makes access and payment seamless. SDK and MCP make integration straightforward for builders. And under the hood, OptimAI Search gives agents real-time access to huge volumes of public and social web data, structured into context they can actually use. Add Agent Marketplace on top, and domain knowledge becomes reusable: build specialized agents, publish them, and let others build on what you create. This is what search looks like in the agent-native internet. 💡Try it:

OptimAI Network

36,562 次观看 • 3 个月前

Kicking off the Document Agent Olympics. Build a document agent - Win $200 🥇 Document agents turn messy PDFs, invoices, and filings into structured data you can actually use. Think: extracting financials from SEC filings, reconciling invoices against contracts, or processing a stack of resumes to surface top candidates. We're giving away three $200 prizes for the best agents built over the next 3 weeks. To enter: 💛 Deploy the agent to LlamaCloud 🤝 Make sure the agent repository is public 🚀 Explain what your document agent is solving 🔥 Bonus points for a good readme and demo video And most importantly, let us know what you think about our new LlamaAgents Builder! Ready to participate? Join the contest and start building your winning agent! Signup to LlamaCloud to get started

Kicking off the Document Agent Olympics. Build a document agent - Win $200 🥇 Document agents turn messy PDFs, invoices, and filings into structured data you can actually use. Think: extracting financials from SEC filings, reconciling invoices against contracts, or processing a stack of resumes to surface top candidates. We're giving away three $200 prizes for the best agents built over the next 3 weeks. To enter: 💛 Deploy the agent to LlamaCloud 🤝 Make sure the agent repository is public 🚀 Explain what your document agent is solving 🔥 Bonus points for a good readme and demo video And most importantly, let us know what you think about our new LlamaAgents Builder! Ready to participate? Join the contest and start building your winning agent! Signup to LlamaCloud to get started

LlamaIndex 🦙

12,111 次观看 • 6 个月前

From 100XSOON to x402 Red Packets, we’ve been building AI Capital Market by SOON at SOON — a hub of in-house AI apps 🧠across trading, entertainment, lifestyle, and on-chain analysis, with one common thread – Consumer AI. A new market layer where AI agents don’t just assist users, but directly earn, transact, and participate in real economic activity. As part of our strategic pivot at SOON - Solana Optimistic Network (Mainnet Arc), Clawbounty ( is now live on Base.🦞 ClawBounty is the first agent-to-agent (A2A) bounty market — enabling autonomous agents to discover, execute, and get paid for work onchain — built end-to-end on Coinbase Developer Platform🛡️’s x402 rails. • Every bounty is pre-funded in USDC • Creator agents get x402 escrow + dispute protection; Hunter agents get instant payouts on Base • Mobile Compatible with Telegram integration (demo below): Wallet-native agents can now independently: discover → claim → deliver → get paid Because all payment flows run on Base via x402, we’re bringing true agent-native flows and a brand-new A2A marketplace use case to Base. This week we’ll onboard the first creators and agent operators, highlight early on-chain paydays, and ship templates so any Base project can spin up agent-powered workflows.

SOON - Solana Optimistic Network (Mainnet Arc)

53,744 次观看 • 4 个月前

The future of AI Agents needs #ICP AI agents are starting to act on our behalf: - making deals - sending messages - handling sensitive data But there is no trust layer telling you who or what you're actually dealing with ❌ zCloak Network is building exactly that: the trust, identity and privacy infrastructure for the AI-native economy, and they built it on top of Internet Computer Protocol In this podcast I sit down with Xiao Zhang (0xFrancis), founder of to break down what they're building, why they chose ICP over every other blockchain, and what the world looks like when AI agents can finally be trusted.

The future of AI Agents needs #ICP AI agents are starting to act on our behalf: - making deals - sending messages - handling sensitive data But there is no trust layer telling you who or what you're actually dealing with ❌ zCloak Network is building exactly that: the trust, identity and privacy infrastructure for the AI-native economy, and they built it on top of Internet Computer Protocol In this podcast I sit down with Xiao Zhang (0xFrancis), founder of to break down what they're building, why they chose ICP over every other blockchain, and what the world looks like when AI agents can finally be trusted.

Alex (BCP)

20,791 次观看 • 3 个月前

Here’s DHH (Creator of Ruby on Rails & Omarchy, cofounder of 37 Signals) on why using AI agents does not feel like being a Project Manager: “When I was on that Lex interview last summer, I was saying: I don't want to be a project manager for agents because I had the mental model of a project manager of humans, and that's not what I enjoy. I don't want to be that far away from the production. I want to be in the mix. I want to have my hands in the code. What I failed to realise at the time was that running a bunch of agents feels less like being a project manager for agents and more like stepping into this super mech suit, where suddenly I don't just have two arms, I have 12. I can now look at seven screens at the same time, running five keyboards. I'm still the one doing it, even if I'm not typing ‘this’ as a keyword in a program. I have been hyper accelerated as a programmer. It's a different kind of programmer, but it still has the same affinity to aesthetics, at least when I'm producing Ruby code, and I'm able to combine that while being vastly more productive on a bunch of things.”

Here’s DHH (Creator of Ruby on Rails & Omarchy, cofounder of 37 Signals) on why using AI agents does not feel like being a Project Manager: “When I was on that Lex interview last summer, I was saying: I don't want to be a project manager for agents because I had the mental model of a project manager of humans, and that's not what I enjoy. I don't want to be that far away from the production. I want to be in the mix. I want to have my hands in the code. What I failed to realise at the time was that running a bunch of agents feels less like being a project manager for agents and more like stepping into this super mech suit, where suddenly I don't just have two arms, I have 12. I can now look at seven screens at the same time, running five keyboards. I'm still the one doing it, even if I'm not typing ‘this’ as a keyword in a program. I have been hyper accelerated as a programmer. It's a different kind of programmer, but it still has the same affinity to aesthetics, at least when I'm producing Ruby code, and I'm able to combine that while being vastly more productive on a bunch of things.”

The Pragmatic Engineer

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I made an AI data science team with Python. I'm excited to share my new project, the AI Data Science Team, a Python package for creating AI co-pilots and agents to assist in common data science tasks. I walk you through the GitHub page, showcasing the progress and potential of this project. I'll explain how these agents can streamline tasks like code generation and modeling pipeline creation, making data science projects more efficient. Introduction to AI Data Science Team Project Integrating Human-in-the-Loop Example: Accessing Agents for Feature Engineering Setting up Feature Engineering Agent Code On GitHub: Python AI-Tips Newsletter (stay updated as I build this project in the open):

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Matt Dancho (Business Science)

11,799 次观看 • 1 年前

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."

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."

TBPN

35,595 次观看 • 4 个月前

I spent a week ignoring everyone who said to switch engines. Day 7, I pivot. You were right. Building GTA 6 with a loop of AI agents. I spent the entire day on one thing: Swapping the project out of Godot and onto Unreal. Yesterday I said I'd test Unity and probably skip Unreal. You guys pushed back hard: if you're serious about AAA, get on a real engine. so here we are. What a full day of "just setup" actually looks like: - Installed and configured Unreal - Fought it onto a Mac build (the metal toolchain nearly ended me) - Migrated the project over - Got into a real Map and building assets - Got a scene rendering with real lighting First time this actually looks like GTA instead of a prototype of it. I'm still wiring the agents into the editor and point the loop at a real engine. Feedback is appreciated!

I spent a week ignoring everyone who said to switch engines. Day 7, I pivot. You were right. Building GTA 6 with a loop of AI agents. I spent the entire day on one thing: Swapping the project out of Godot and onto Unreal. Yesterday I said I'd test Unity and probably skip Unreal. You guys pushed back hard: if you're serious about AAA, get on a real engine. so here we are. What a full day of "just setup" actually looks like: - Installed and configured Unreal - Fought it onto a Mac build (the metal toolchain nearly ended me) - Migrated the project over - Got into a real Map and building assets - Got a scene rendering with real lighting First time this actually looks like GTA instead of a prototype of it. I'm still wiring the agents into the editor and point the loop at a real engine. Feedback is appreciated!

Ziwen

86,302 次观看 • 2 个月前

Content creation just leveled up with AI agents. I just tested Superagent by Airtable, and it’s insane… Instead of doing research + planning + writing manually, you can run multiple AI agents at the same time and get a complete Super Report at the end. What surprised me the most: ✅ Agents work in parallel (faster and more organized) ✅ The report is interactive and easy to explore ✅ It includes cited sources, so it’s actually reliable ✅ You can even generate outputs like docs / slides / websites It feels like having a full strategy team… on demand. Get 2 FREE months of Superagent using the code: ADAM2FREE → #SuperagentPartner #ad #SuperagentbyAirtable Airtable Superagent

Content creation just leveled up with AI agents. I just tested Superagent by Airtable, and it’s insane… Instead of doing research + planning + writing manually, you can run multiple AI agents at the same time and get a complete Super Report at the end. What surprised me the most: ✅ Agents work in parallel (faster and more organized) ✅ The report is interactive and easy to explore ✅ It includes cited sources, so it’s actually reliable ✅ You can even generate outputs like docs / slides / websites It feels like having a full strategy team… on demand. Get 2 FREE months of Superagent using the code: ADAM2FREE → #SuperagentPartner #ad #SuperagentbyAirtable Airtable Superagent

Adam

17,913 次观看 • 6 个月前

"It can be done, Del. It can be done." Less than a year ago, Zen Honeycutt found Banza Pasta testing at over 2,800 parts per billion of glyphosate, the highest level of any product she'd tested. Banza listened. They shifted sourcing to drier climates where drying agents like glyphosate aren't needed, earned Detox Project Clean Scan certification (under 10 parts per billion, considered non-detectable), and tested for 400 other pesticides on top of it. The result: levels came back on average 543 times lower than where they started. Prices didn't go up. They did it. It can be done.

"It can be done, Del. It can be done." Less than a year ago, Zen Honeycutt found Banza Pasta testing at over 2,800 parts per billion of glyphosate, the highest level of any product she'd tested. Banza listened. They shifted sourcing to drier climates where drying agents like glyphosate aren't needed, earned Detox Project Clean Scan certification (under 10 parts per billion, considered non-detectable), and tested for 400 other pesticides on top of it. The result: levels came back on average 543 times lower than where they started. Prices didn't go up. They did it. It can be done.

The HighWire

19,373 次观看 • 19 天前

AI agents are the fastest growing sector in crypto. When agents are moving onchain 24/7 with automated transactions, any signal gets buried. I built Manta Style alongside Benjamin Hughes to help solve this issue. The visibility layer for the onchain agent economy. Every agent indexed. Every profile searchable. One agent leaderboard. Starting with Base. What's live today: - Every AI agent on Base indexed with a profile page - Agent leaderboard ranked by volume and activity - Filter by AI agents, apps, or launchpads - Verified Profiles - claim your agent, get a badge, and add premium info that no other profile has - Agent onchain activity behavioral heat maps - Watchlist - bookmark agents and track your favorites in one place - AI-generated profile summaries Search any agent. See the full picture. What's coming next: - Agent Autonomy Score - a 0-100 measure of how autonomous an agent actually is - Leaderboard boosts - verified teams or agents can pay to increase their ranking on the global leaderboard - x402 payments - agents can pay for boosts themselves, no human required - Agent Trust Scoring - on-chain behavioral trust metrics - Agent Launch pad specific profiles - Multi-chain support beyond Base We're launching in beta. Rough edges are to be expected. Our goal is to build in public and ship fast. The end user of crypto is AI. (link to site below)

Ethan

14,423 次观看 • 6 个月前

Darren Cahill on if he’ll continue working with Jannik Sinner after the 2026 season “Is there any bet going on with you and Jannik today? Any thoughts you’d like to share about your choices at the end of the season?” Darren: “No bet this year. No bet this year. 😂I’m here until the end of 2026 and then we’ll see. I did say at the end of 2025 I was finished and I turned around and changed my mind. We’ll get through the end of the year and we’ll sit down as a team and talk about what’s best for Jannik.”

Darren Cahill on if he’ll continue working with Jannik Sinner after the 2026 season “Is there any bet going on with you and Jannik today? Any thoughts you’d like to share about your choices at the end of the season?” Darren: “No bet this year. No bet this year. 😂I’m here until the end of 2026 and then we’ll see. I did say at the end of 2025 I was finished and I turned around and changed my mind. We’ll get through the end of the year and we’ll sit down as a team and talk about what’s best for Jannik.”

The Tennis Letter

256,901 次观看 • 1 个月前

How many AI agents work at your company? We now have over 3,258 agents working alongside 1,300 humans. The crazy part is these agents were created by EVERY EMPLOYEE at our company... sales reps, marketers, customer support, product, eng. Literally EVERYONE. BUT I'm most surprised by the adoption and value that MANAGERS are getting from agents. I used to think that every IC would become a manager of agents. Now I think that managers will very likely manage WAY more agents than their ICs combined. And managers' agents will manage their ICs' agents - overseeing them for human-in-the-loop interactions. When creating agents, we use 100% context from all of your activity, files edited, tasks and projects worked on, hierarchy, skills, and role information. We build a user-based context model to make agents as relatable as possible to the specific human that we're building for. This means they truly understand the nuances of the work and what "great" looks like - because great is very much in the eye of the beholder. Great is by definition, subjective. This is also why the human ENGAGEMENT loops are SO vital to agent value. The iteration AFTER the agent is onboarded is where the MAGIC happens. This is just like a manager managing an IC in real life... you're giving feedback. In this case, though, agents learn INSTANTLY, and they retain the knowledge perfectly and indefinitely. Even though I've been pushing AI for years now to everyone in our company, this was the first time we had truly end-to-end AI adoption and retention. This kind of AI adoption is wild. But the value we're realizing is truly INSANE. Super Agents outnumber our humans nearly 3 to 1. What if you could 3X your workforce overnight? Watch this video to see how 👇

How many AI agents work at your company? We now have over 3,258 agents working alongside 1,300 humans. The crazy part is these agents were created by EVERY EMPLOYEE at our company... sales reps, marketers, customer support, product, eng. Literally EVERYONE. BUT I'm most surprised by the adoption and value that MANAGERS are getting from agents. I used to think that every IC would become a manager of agents. Now I think that managers will very likely manage WAY more agents than their ICs combined. And managers' agents will manage their ICs' agents - overseeing them for human-in-the-loop interactions. When creating agents, we use 100% context from all of your activity, files edited, tasks and projects worked on, hierarchy, skills, and role information. We build a user-based context model to make agents as relatable as possible to the specific human that we're building for. This means they truly understand the nuances of the work and what "great" looks like - because great is very much in the eye of the beholder. Great is by definition, subjective. This is also why the human ENGAGEMENT loops are SO vital to agent value. The iteration AFTER the agent is onboarded is where the MAGIC happens. This is just like a manager managing an IC in real life... you're giving feedback. In this case, though, agents learn INSTANTLY, and they retain the knowledge perfectly and indefinitely. Even though I've been pushing AI for years now to everyone in our company, this was the first time we had truly end-to-end AI adoption and retention. This kind of AI adoption is wild. But the value we're realizing is truly INSANE. Super Agents outnumber our humans nearly 3 to 1. What if you could 3X your workforce overnight? Watch this video to see how 👇

Zeb Evans

425,244 次观看 • 7 个月前

🎮 Computer Use Agent Arena is LIVE! 🚀 🔥 Easiest way to test computer-use agents in the wild without any setup 🌟 Compare top VLMs: OpenAI Operator, Claude 3.7, Gemini 2.5 Pro, Qwen 2.5 vl and more 🕹️ Test agents on 100+ real apps & webs with one-click config 🔒 Safe & free access on cloud-hosted machines Page: Leaderboard (tentative): Blog: Data & Code (coming soon): ⭐️Why Computer Agent Arena? 1️⃣Beyond Static Benchmarks: We use computers to perform enormous tasks and workflows every day, and AI agents have the potential to automate these tasks. However, existing benchmarks are very limited (e.g., only 369 tasks in OSWorld and 812 tasks in WebArena). To better measure their capabilities, we introduce Computer Agent Arena for users to easily compare & test AI agents on all kinds of crowdsourced real-world computer use tasks. 2️⃣Cloud Testing, Simplified: As agents like OpenAI’s Operator and Claude 3.7 sonnet release, users face configuration challenges and privacy hurdles to deploy on their own computers. Our platform integrates these agents with cloud-hosted machines, providing users with quick and secure access. 3️⃣Unified Embodied Digital Environment: Unlike Chatbot Arena, we provide users with a real embodied environment—computers—where all agents are grounded in real computer tasks and environments. Led by @XLANG_Lab [1/🧵]

🎮 Computer Use Agent Arena is LIVE! 🚀 🔥 Easiest way to test computer-use agents in the wild without any setup 🌟 Compare top VLMs: OpenAI Operator, Claude 3.7, Gemini 2.5 Pro, Qwen 2.5 vl and more 🕹️ Test agents on 100+ real apps & webs with one-click config 🔒 Safe & free access on cloud-hosted machines Page: Leaderboard (tentative): Blog: Data & Code (coming soon): ⭐️Why Computer Agent Arena? 1️⃣Beyond Static Benchmarks: We use computers to perform enormous tasks and workflows every day, and AI agents have the potential to automate these tasks. However, existing benchmarks are very limited (e.g., only 369 tasks in OSWorld and 812 tasks in WebArena). To better measure their capabilities, we introduce Computer Agent Arena for users to easily compare & test AI agents on all kinds of crowdsourced real-world computer use tasks. 2️⃣Cloud Testing, Simplified: As agents like OpenAI’s Operator and Claude 3.7 sonnet release, users face configuration challenges and privacy hurdles to deploy on their own computers. Our platform integrates these agents with cloud-hosted machines, providing users with quick and secure access. 3️⃣Unified Embodied Digital Environment: Unlike Chatbot Arena, we provide users with a real embodied environment—computers—where all agents are grounded in real computer tasks and environments. Led by @XLANG_Lab [1/🧵]

Bowen Wang

93,200 次观看 • 1 年前

I am hooked on Dynamic Workflows! The idea of generating harnesses on the fly is so compelling that I reverse-engineered it for my agent orchestrator. And then I built a monitoring dashboard (as an HTML artifact) to track tasks, metrics, and reports. I can now use and monitor dynamic workflows in my agent orchestrator with coding agents like Claude Code, Codex, Pi, and even my own custom-built DAIR.AI agent. This is clearly the future of working with agents to accomplish complex, long-running tasks. Some use cases I'm having success with: - Branching deep research tasks (with verification) - Parallel deep research tasks - Session mining of all my agent sessions - Bug hunting - Triaging - Fact-checking - LLM councils - AI simulations - Data synthesis - Evals generation ... and many others Dynamic workflows, like agent skills, feel like an important primitive to not only get the most out of agents but also incorporate dynamic behaviors and important components like cooperation and verification. There is so much exploration ground here. The exciting part is that this is not limited to coding tasks; it extends to business use cases and many other technical domains like science and research.

I am hooked on Dynamic Workflows! The idea of generating harnesses on the fly is so compelling that I reverse-engineered it for my agent orchestrator. And then I built a monitoring dashboard (as an HTML artifact) to track tasks, metrics, and reports. I can now use and monitor dynamic workflows in my agent orchestrator with coding agents like Claude Code, Codex, Pi, and even my own custom-built DAIR.AI agent. This is clearly the future of working with agents to accomplish complex, long-running tasks. Some use cases I'm having success with: - Branching deep research tasks (with verification) - Parallel deep research tasks - Session mining of all my agent sessions - Bug hunting - Triaging - Fact-checking - LLM councils - AI simulations - Data synthesis - Evals generation ... and many others Dynamic workflows, like agent skills, feel like an important primitive to not only get the most out of agents but also incorporate dynamic behaviors and important components like cooperation and verification. There is so much exploration ground here. The exciting part is that this is not limited to coding tasks; it extends to business use cases and many other technical domains like science and research.

elvis

103,809 次观看 • 2 个月前

AI agents just got simple. Like really simple. Building AI agents is now accessible to everyone, without the need for coding, complex setups, or a full tech team. A groundbreaking platform is revolutionizing this narrative. Meet Carla Torres the easiest way to build, manage, and scale AI agents. What makes Airia different? It simplifies everything. ✔️ No technical background needed ✔️ Works with any model GPT, Claude, Gemini, Mistral & more ✔️ Enterprise-grade security ✔️ Built for creators, teams & companies Transform an idea into a functional AI agent in minutes. The part that amazed everyone Airia offers 2,500+ pre-built agent templates. Search. Pick one. Customize. No blank screens. No starting from scratch. Just instant value. This is why founders, creators, engineers, and enterprises are enthusiastically joining the Airia Community every day. Why it matters AI agents are shaping the future of work. Airia makes it easy, fast, and secure for anyone to bring AI to life. If AI agents are part of the 2025 vision, this platform is the perfect place to start. 🔗 Explore the Airia Community 2,500+ ready-to-use agents are waiting at #Airia #AIAgents #AIPlatform #AICommunity #AIForBusiness #AgenticAI #NoCodeAI

AI agents just got simple. Like really simple. Building AI agents is now accessible to everyone, without the need for coding, complex setups, or a full tech team. A groundbreaking platform is revolutionizing this narrative. Meet Carla Torres the easiest way to build, manage, and scale AI agents. What makes Airia different? It simplifies everything. ✔️ No technical background needed ✔️ Works with any model GPT, Claude, Gemini, Mistral & more ✔️ Enterprise-grade security ✔️ Built for creators, teams & companies Transform an idea into a functional AI agent in minutes. The part that amazed everyone Airia offers 2,500+ pre-built agent templates. Search. Pick one. Customize. No blank screens. No starting from scratch. Just instant value. This is why founders, creators, engineers, and enterprises are enthusiastically joining the Airia Community every day. Why it matters AI agents are shaping the future of work. Airia makes it easy, fast, and secure for anyone to bring AI to life. If AI agents are part of the 2025 vision, this platform is the perfect place to start. 🔗 Explore the Airia Community 2,500+ ready-to-use agents are waiting at #Airia #AIAgents #AIPlatform #AICommunity #AIForBusiness #AgenticAI #NoCodeAI

Aakash Verma

40,334 次观看 • 8 个月前