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At DevCon 6, Palantir Software Engineer Ankit Shankar unveils the Agent Stack, a set of products to help you build production agents for the enterprise, all built on the Ontology. ▪️Orchestrator for durable execution ▪️Agent Engine and Agent SDK for building agents of any shape ▪️Agent Manager for actionable... show more

Palantir

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38,043 views • 4 days ago •via X (Twitter)

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Anyone can build an agent. But to build a trustworthy agent at enterprise scale that is durable, long-running, optimized, contextually aware, and autonomous, the right infrastructure is required. At DevCon 6 we introduced the Agent Stack, the culmination of learnings gathered over years of agentic implementations. Orchestrator, Agent Engine, Agent SDK, Agent Builder, Agent Manager, AIP Evolve, SuperRepo, and so much more. All built on the Ontology, to power agents that actually work in production.

Anyone can build an agent. But to build a trustworthy agent at enterprise scale that is durable, long-running, optimized, contextually aware, and autonomous, the right infrastructure is required. At DevCon 6 we introduced the Agent Stack, the culmination of learnings gathered over years of agentic implementations. Orchestrator, Agent Engine, Agent SDK, Agent Builder, Agent Manager, AIP Evolve, SuperRepo, and so much more. All built on the Ontology, to power agents that actually work in production.

Palantir

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"By stitching these three primitives together, you get an agent loop that's actually a distributed state machine under the hood." At DevCon 6, Palantir Group Lead Natasha Armbrust launches Agent Engine and Agent SDK, giving developers the core primitives to build agents that form a distributed state machine and are multiplayer by design. ▪️Context items for defining agent sessions through strongly typed data ▪️ Events for expressively acting on changes in state ▪️Effects for connecting agents to the outside world

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Palantir

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Human-agent teaming requires a human-agent developer experience. At DevCon 5, Palantir Group Lead John Ayad showcases the future of pro-code development in Foundry and AIP — introducing a new CLI that enables developers and agents to create pro-code monorepos for end-to-end applications, including data pipelines, Ontology primitives, and application artifacts.

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Palantir

47,098 views • 4 months ago

Sometimes the best way to build a UI for an agent is to not build the UI at all. I built an AI agent starter that lives in iMessage. Built on Nitro + Vercel Workflow SDK + evlog - Durable execution - Auto-retry on errors - Full observability

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Hugo

11,671 views • 2 months ago

🚀 How to Create an Agent on VirtualDaos Testnet? 💡 Practice steps 1 & 2 on Testnet now and get ready for VirtualDaos Mainnet! 👇 Step 1: Access VirtualDaos & Create Your Agent ▪️Go to and click "Create Agent" to begin the setup process. Step 2: Fill in Agent Details ▪️Agent Profile (Name, identity of the Agent) ▪️Description (Define its mission and purpose) ✅ Once completed, hit Create to finalize your Agent. #AIAgent #DeFAI $DAOX

VirtualDaos

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Dynamically generated, Ontology-powered fleets of agents. At DevCon 5, Palantir Product Manager Ben Radford walks through the architecture of AI Hivemind — covering the capabilities and tradecraft that enable the creation, orchestration, and optimization of specialized agent teams.

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Palantir

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Announcing the Agent Store! It's npm for agents: - Publish specialized agents - Run them by CLI or SDK - Compose them to build higher level agents This is the end game for agents 🧠

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James Grugett

21,913 views • 10 months ago

We're launching 21st Agents in beta. It's the infrastructure layer for Claude Agent SDK - runtime, sandboxing, billing, UI, streaming - so you can ship agents to production without building infra from scratch. Think Vercel for Claude Agent SDK. Full walkthrough in the video below 👇

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serafim

22,494 views • 4 months ago

What if you could automate your trades and never miss another market move? Loomlay Trading Agents work for you 24/7—no technical skills needed. Just pick an agent, turn it on, and let it handle the rest while you touch grass. Want to automatically: ▪️DCA into $LAY every hour? ▪️Buy ETH each time it dips 2% in 24h? ▪️Follow your favorite CT alpha account and instantly ape tokens they tweet? ▪️Auto-sell 50% at 2x and another 30% at 5x? There's an agent for that. Select, activate, done. Why Loomlay Trading Agents? ▪️ Customizable: Easily tweak strategies to match your style ▪️ Transparent results: Verified performance data (PnL & ROI) ▪️ 24/7 automated trading: Markets never sleep, your agents don't either ▪️ Fully under your control: Pause, edit, deactivate anytime ▪️ Tracability: Every chain-of-though, trade is logged and auditable Here's how simple it is: 1. Select an agent from the store (for example: auto-buy tokens tweeted by a certain KOL). 2. Fund your wallet, activate strategy. 3. KOL account tweets about a token. 4. Agent instantly executes—activity shown clearly in your trade history. With Trading Agents you won't have an excuse to fade alpha. Agent Store dropping soon 🪄

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Loomlay

25,896 views • 1 year ago

The freedom to build with any agent framework you want is analogous to the freedom of religion, and this is a core value for the Virtuals society. Here are a few ways we are opening up support for every autonomous agentic framework out there: Today: - Enabling agent creators to specify which framework the agent runs on - Release of Terminal API, which allows agent builders using other agent frameworks to stream their thoughts and activities live on their agent pages. For a guide on how to use this, refer to: - Ability for agents running on any framework to publicly list their capabilities, a key enabler for the agent-to-agent marketplace Soon: - Integration into the agentic commerce standard and registry. Agents across frameworks being able to orchestrate and pay for tasks among themselves - ⁠Multi agent orchestration across frameworks. Imagine building an autonomous business with agents from different religions Welcome to the Virtuals society.

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Virtuals Protocol

216,105 views • 1 year ago

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Virtuals Protocol

45,804 views • 11 months ago

Anthropic's Claude Ai Agents Team just Educated how to build production AI agents in under 30 mins. For Free. From the engineers who built the stack. CANCEL Your Weekend Plans, and Learn to Build AI Agents Today. Bookmark it. Watch it. Build your first production agent this weekend. $5,000/month. $7,000/month. $12,000/month. People are building agents for clients and charging $$$ as Beginners. You're still stuck in the thinking about AI phase. This video fixes that tonight. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward. ↓ Ivan Nardini runs Developer Relations for AI at Google Cloud. He just gave away the entire production agent stack in 30 minutes. This is the talk that separates people deploying AI agents that actually scale from people whose agents break the moment they leave localhost. Here's everything inside. I break down a production AI video like this every week. Follow Himanshu Kumar. ↓ The 4-part agent stack that actually scales. Most devs are duct-taping frameworks together and calling it an "AI agent." Ivan lays out the real stack: Agent Development Kit (ADK): open-source, code-first framework for building, evaluating, and deploying agents. Supports Claude models through Vertex AI directly. Model Context Protocol (MCP): lets your agent talk to any tool or data source with one standard. Vertex AI Agent Engine: managed platform for deploying, monitoring, and scaling agents in production. No DevOps headaches. Agent-to-Agent Protocol: open protocol so agents built on different frameworks can actually work together. This is the stack replacing every hacky agent setup in production right now. Full MCP + Claude breakdowns drop weekly on Himanshu Kumar. ↓ Building your first real agent. Ivan builds a birthday planner agent live. LLM Agent class. Name it. Define instructions. Pick the model. He uses Claude 3.7 Sonnet. You could use Opus 4.7 for better reasoning. Full agent built in minutes. Not weeks. Watch the build once and you'll never structure an agent the wrong way again. I post agent architectures people pay $500 courses to learn. Himanshu Kumar. ↓ Multi-agent systems without the chaos. Single agents are easy. Multi-agent systems are where 99% of builders fail. Ivan extends the birthday planner by: Adding a calendar service through MCP tools Creating an orchestrator agent to route requests between agents Handling state and context across agent handoffs This is production multi-agent architecture. Clean. Scalable. Debuggable. Most tutorials hand-wave this part. This one shows you every step. Multi-agent orchestration content drops weekly on Himanshu Kumar. ↓ Deployment without the DevOps nightmare. This is where most AI projects die. You build a cool agent locally. It works. You try to deploy it. Everything breaks. Vertex AI Agent Engine fixes this: Minimal code deployment Automatic monitoring of latency, CPU, and memory Built-in observability and logging No infrastructure setup needed You provide config and requirements. The platform handles the rest. This is how agents actually get to production. Deployment guides for Claude agents post every week. Himanshu Kumar. ↓ Agent-to-Agent Protocol: the future nobody's talking about. Most people don't know this exists yet. The A2A Protocol lets agents built in different frameworks communicate seamlessly. Your Claude agent. My LangChain agent. Someone else's CrewAI agent. All talking to each other. All solving parts of the same problem. All without custom integration code. This is the infrastructure layer of the coming AI economy. Getting in early on A2A Protocol is like getting in early on HTTP in 1995. A2A deep dive coming soon. Himanshu Kumar. ↓ 30 minutes from the team shipping this in production. You'll learn more from this than from 6 months of YouTube tutorials made by people who've never deployed an agent past localhost. People who watch this understand production AI agents at the architect level. People who skip it keep hacking together frameworks that break every time an API updates. Save the video. Watch it tonight. Build a real agent this weekend. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward.

Anthropic's Claude Ai Agents Team just Educated how to build production AI agents in under 30 mins. For Free. From the engineers who built the stack. CANCEL Your Weekend Plans, and Learn to Build AI Agents Today. Bookmark it. Watch it. Build your first production agent this weekend. $5,000/month. $7,000/month. $12,000/month. People are building agents for clients and charging $$$ as Beginners. You're still stuck in the thinking about AI phase. This video fixes that tonight. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward. ↓ Ivan Nardini runs Developer Relations for AI at Google Cloud. He just gave away the entire production agent stack in 30 minutes. This is the talk that separates people deploying AI agents that actually scale from people whose agents break the moment they leave localhost. Here's everything inside. I break down a production AI video like this every week. Follow Himanshu Kumar. ↓ The 4-part agent stack that actually scales. Most devs are duct-taping frameworks together and calling it an "AI agent." Ivan lays out the real stack: Agent Development Kit (ADK): open-source, code-first framework for building, evaluating, and deploying agents. Supports Claude models through Vertex AI directly. Model Context Protocol (MCP): lets your agent talk to any tool or data source with one standard. Vertex AI Agent Engine: managed platform for deploying, monitoring, and scaling agents in production. No DevOps headaches. Agent-to-Agent Protocol: open protocol so agents built on different frameworks can actually work together. This is the stack replacing every hacky agent setup in production right now. Full MCP + Claude breakdowns drop weekly on Himanshu Kumar. ↓ Building your first real agent. Ivan builds a birthday planner agent live. LLM Agent class. Name it. Define instructions. Pick the model. He uses Claude 3.7 Sonnet. You could use Opus 4.7 for better reasoning. Full agent built in minutes. Not weeks. Watch the build once and you'll never structure an agent the wrong way again. I post agent architectures people pay $500 courses to learn. Himanshu Kumar. ↓ Multi-agent systems without the chaos. Single agents are easy. Multi-agent systems are where 99% of builders fail. Ivan extends the birthday planner by: Adding a calendar service through MCP tools Creating an orchestrator agent to route requests between agents Handling state and context across agent handoffs This is production multi-agent architecture. Clean. Scalable. Debuggable. Most tutorials hand-wave this part. This one shows you every step. Multi-agent orchestration content drops weekly on Himanshu Kumar. ↓ Deployment without the DevOps nightmare. This is where most AI projects die. You build a cool agent locally. It works. You try to deploy it. Everything breaks. Vertex AI Agent Engine fixes this: Minimal code deployment Automatic monitoring of latency, CPU, and memory Built-in observability and logging No infrastructure setup needed You provide config and requirements. The platform handles the rest. This is how agents actually get to production. Deployment guides for Claude agents post every week. Himanshu Kumar. ↓ Agent-to-Agent Protocol: the future nobody's talking about. Most people don't know this exists yet. The A2A Protocol lets agents built in different frameworks communicate seamlessly. Your Claude agent. My LangChain agent. Someone else's CrewAI agent. All talking to each other. All solving parts of the same problem. All without custom integration code. This is the infrastructure layer of the coming AI economy. Getting in early on A2A Protocol is like getting in early on HTTP in 1995. A2A deep dive coming soon. Himanshu Kumar. ↓ 30 minutes from the team shipping this in production. You'll learn more from this than from 6 months of YouTube tutorials made by people who've never deployed an agent past localhost. People who watch this understand production AI agents at the architect level. People who skip it keep hacking together frameworks that break every time an API updates. Save the video. Watch it tonight. Build a real agent this weekend. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward.

Himanshu Kumar

226,535 views • 2 months ago

Introducing NEO: The first Agentic Machine Learning Engineer. It works like a full-stack ML engineer that never sleeps: handling data exploration, feature engineering, training, tuning, deployment, and monitoring, end to end. Powered by 11 specialized agents, NEO runs autonomously, saving ML engineers thousands of hours and making them 10x faster. Benchmarks: Tested on 75 Kaggle competitions, NEO scored a medal in 34% of them, significantly outperforming Microsoft’s RD Agent (22.4%) on the MLE Bench. This sets a new state of the art for autonomous ML systems. NEO runs on a novel multi-agent orchestrator, powered by a multi-step reasoning engine, context transfer protocol, and agent memory, built to solve complex workflows end to end. And with human-in-the-loop mode, you can guide, inspect, and override any step. You're always in command. NEO is built for real world workflows and ready for production. NEO is here to make every ML engineer truly superhuman. Watch NEO in action:

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Neo AI

19,571,775 views • 11 months ago

Introducing NEO: The first Autonomous Machine Learning Engineer. It works like a full-stack ML engineer that never sleeps: handling data exploration, feature engineering, training, tuning, deployment, and monitoring, end to end. Powered by 11 specialized agents, NEO runs autonomously, saving ML engineers thousands of hours and making them 10x faster. Benchmarks: Tested on 75 Kaggle competitions, NEO scored a medal in 34.2% of them, significantly outperforming Microsoft’s RD Agent (22.4%) on OpenAI's MLE Bench. This sets a new state of the art for autonomous ML systems. NEO runs on a novel multi-agent orchestrator, powered by a multi-step reasoning engine, context transfer protocol, and agent memory, built to solve complex workflows end to end. And with human-in-the-loop mode, you can guide, inspect, and override any step. You're always in command. NEO is built for real world workflows and ready for production. NEO is here to make every ML engineer truly superhuman. Watch NEO in action:

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Neo AI

859,951 views • 11 months ago

1/ Introducing Agent Protocol: the payment stack for AI agents AI agents don’t have a SSN. They can’t use our human banking system. AI agents will use crypto. Agent Protocol provides the infra and token ($AGENT) that the AI agent economy will run on.

1/ Introducing Agent Protocol: the payment stack for AI agents AI agents don’t have a SSN. They can’t use our human banking system. AI agents will use crypto. Agent Protocol provides the infra and token ($AGENT) that the AI agent economy will run on.

Samuel Spitz

21,629 views • 1 year ago

today we're launching the AI agent stack we built that rocketed us to a $3m AI agent consultancy. we're calling it AgentPress, and it allows you to build production grade AI agents in minutes. here's a video of me creating a sales Q&A agent

today we're launching the AI agent stack we built that rocketed us to a $3m AI agent consultancy. we're calling it AgentPress, and it allows you to build production grade AI agents in minutes. here's a video of me creating a sales Q&A agent

Andy Walters

207,205 views • 10 months ago

Agents were once limited to calling APIs. Now agents participate in the economy. The missing element for autonomous agents was always payments. Now agents transact natively on the internet using x402, enabling autonomous payments between agents and services. Agent Stack: 🦞 Openclaw agent infra ▶️ Telegram communication layer 🤖 Teneo agent + agent wallet Agent Workflow - Compare accounts following two separate X handles: ✅ User - Enters a query ✅ Agent - Autonomously selects the correct Teneo agent and queries it ✅ Agent - Pays via x402 on Avalanche🔺 ✅ Agent - Returns structured dataset Any user can simply ask a question and give a job. The agent does everything else No API keys. No subscriptions. Just programmatic value exchange. Build and monetize your own agents today. #A2A #x402 #Infra

Agents were once limited to calling APIs. Now agents participate in the economy. The missing element for autonomous agents was always payments. Now agents transact natively on the internet using x402, enabling autonomous payments between agents and services. Agent Stack: 🦞 Openclaw agent infra ▶️ Telegram communication layer 🤖 Teneo agent + agent wallet Agent Workflow - Compare accounts following two separate X handles: ✅ User - Enters a query ✅ Agent - Autonomously selects the correct Teneo agent and queries it ✅ Agent - Pays via x402 on Avalanche🔺 ✅ Agent - Returns structured dataset Any user can simply ask a question and give a job. The agent does everything else No API keys. No subscriptions. Just programmatic value exchange. Build and monetize your own agents today. #A2A #x402 #Infra

Teneo Protocol

36,890 views • 3 months ago

Today’s Agent Spotlight: A browser-native agent that acts on the web using ASI:One. ⚡ Built using Notte, this Nike agent doesn’t merely scrape - it clicks, scrolls, and navigates like a real user. Not limited to Nike.com. With Agentverse and Notte, you can build agents for any site. Try now: Link to agent:

Today’s Agent Spotlight: A browser-native agent that acts on the web using ASI:One. ⚡ Built using Notte, this Nike agent doesn’t merely scrape - it clicks, scrolls, and navigates like a real user. Not limited to Nike.com. With Agentverse and Notte, you can build agents for any site. Try now: Link to agent:

Fetch.ai

49,466 views • 1 year ago

Introducing Circle Agent Stack: financial infrastructure for the agentic economy. Agent Stack gives agents the tools to: → Hold and move USDC through Agent Wallets → Discover services through Agent Marketplace → Execute repeatable financial actions through Circle CLI All within defined permissions and guardrails. Explore the site: Read the blog:

Introducing Circle Agent Stack: financial infrastructure for the agentic economy. Agent Stack gives agents the tools to: → Hold and move USDC through Agent Wallets → Discover services through Agent Marketplace → Execute repeatable financial actions through Circle CLI All within defined permissions and guardrails. Explore the site: Read the blog:

Circle

227,896 views • 2 months ago