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Anthropic has introduced Claude Managed Agents which is a new system designed to help developers build and deploy agents at scale. It combines an agent harness with production infrastructure to move from prototype to launch in days.

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33,184 views • 3 months ago •via X (Twitter)

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Introducing Claude Managed Agents: everything you need to build and deploy agents at scale. It pairs an agent harness tuned for performance with production infrastructure, so you can go from prototype to launch in days. Now in public beta on the Claude Platform.

Introducing Claude Managed Agents: everything you need to build and deploy agents at scale. It pairs an agent harness tuned for performance with production infrastructure, so you can go from prototype to launch in days. Now in public beta on the Claude Platform.

Claude

21,745,616 views • 3 months ago

21st (21stdev) is the production layer for frontier AI agents. 1.4M developers already use their platform to build AI apps, 15K+ GitHub stars - now they're making it dead simple to deploy Claude Code, Codex, and other agents to production. Congrats on the launch, sergey bunas & serafim!

21st (21stdev) is the production layer for frontier AI agents. 1.4M developers already use their platform to build AI apps, 15K+ GitHub stars - now they're making it dead simple to deploy Claude Code, Codex, and other agents to production. Congrats on the launch, sergey bunas & serafim!

Y Combinator

33,400 views • 4 months ago

In the future, you’ll be able to accomplish a goal by just giving Claude an outcome and a budget. That’s the direction Anthropic is building in with its new Managed Agents features, announced at this week’s Code with Claude developer event. The basic idea: Claude, wrapped in a computer in the cloud, that you can spin up, scale, and manage as needed. Anthropic is taking on the infrastructure that kills most agent products, and making sure that it scales to meet the needs of agents running 24/7. On this week’s AI & I from Every 📧, I talk with Angela Jiang (Angela Jiang), head of product for the Claude platform, and Katelyn Lesse (Katelyn Lesse), head of engineering for the Claude platform, about what Anthropic is building and what it takes to make agents reliable in production. We get into: - Why the "build a generic harness, hot-swap any model behind it" playbook is already outdated. Angela points to eval data on Memory where the same task across different harnesses performed drastically differently. - The infrastructure wall every team hits in production—and why Katelyn thinks “my sandbox died and took the agent with it” is the real reason internal agents don't ship. - Why Anthropic is so bullish on using file systems and skills within Claude, including Angela's argument that those early design choices can compound for years. This is a must-watch for anyone trying to take an agent past the demo and into production. Watch below! Timestamps: How the Claude platform evolved from API to agents: 00:01:48 The primitives that make up Claude Managed Agents: 00:04:09 Why the harness and the model are becoming a single unit: 00:10:37 The infrastructure wall that kills most agent projects in production: 00:18:49 Why team agents need a different shape than individual productivity tools: 00:24:49 How Anthropic's legal team uses an agent to review marketing copy: 00:26:36 Using multi-agent orchestration for advisor strategies, adversarial pairs, and swarms: 00:34:24 How to measure agent success with outcome and budget as the end state: 00:35:50 What the platform looks like a year from now, when Claude writes its own harness: 00:39:11

In the future, you’ll be able to accomplish a goal by just giving Claude an outcome and a budget. That’s the direction Anthropic is building in with its new Managed Agents features, announced at this week’s Code with Claude developer event. The basic idea: Claude, wrapped in a computer in the cloud, that you can spin up, scale, and manage as needed. Anthropic is taking on the infrastructure that kills most agent products, and making sure that it scales to meet the needs of agents running 24/7. On this week’s AI & I from Every 📧, I talk with Angela Jiang (Angela Jiang), head of product for the Claude platform, and Katelyn Lesse (Katelyn Lesse), head of engineering for the Claude platform, about what Anthropic is building and what it takes to make agents reliable in production. We get into: - Why the "build a generic harness, hot-swap any model behind it" playbook is already outdated. Angela points to eval data on Memory where the same task across different harnesses performed drastically differently. - The infrastructure wall every team hits in production—and why Katelyn thinks “my sandbox died and took the agent with it” is the real reason internal agents don't ship. - Why Anthropic is so bullish on using file systems and skills within Claude, including Angela's argument that those early design choices can compound for years. This is a must-watch for anyone trying to take an agent past the demo and into production. Watch below! Timestamps: How the Claude platform evolved from API to agents: 00:01:48 The primitives that make up Claude Managed Agents: 00:04:09 Why the harness and the model are becoming a single unit: 00:10:37 The infrastructure wall that kills most agent projects in production: 00:18:49 Why team agents need a different shape than individual productivity tools: 00:24:49 How Anthropic's legal team uses an agent to review marketing copy: 00:26:36 Using multi-agent orchestration for advisor strategies, adversarial pairs, and swarms: 00:34:24 How to measure agent success with outcome and budget as the end state: 00:35:50 What the platform looks like a year from now, when Claude writes its own harness: 00:39:11

Dan Shipper 📧

66,339 views • 2 months ago

ex-Anthropic engineer: "I left Anthropic to build agents systems in Google but nothing beats Claude as an agent yet - in coding and tool use, the rest are still behind " in this 90-min podcast, he showed how to build effective agent system with Claude from scratch to get into Anthropic, he hand-coded Karpathy's nanoGPT as a physicist Claude → tools → loops of verification → a real agent and why, at Google, he says Claude is the best watch & bookmark - then read the guide to agent systems ↓

ex-Anthropic engineer: "I left Anthropic to build agents systems in Google but nothing beats Claude as an agent yet - in coding and tool use, the rest are still behind " in this 90-min podcast, he showed how to build effective agent system with Claude from scratch to get into Anthropic, he hand-coded Karpathy's nanoGPT as a physicist Claude → tools → loops of verification → a real agent and why, at Google, he says Claude is the best watch & bookmark - then read the guide to agent systems ↓

codila

126,242 views • 12 days ago

Over the past 6 months, Claude Platform has added new APIs to help developers quickly build and deploy agents. Platform leads Katelyn Lesse, Angela Jiang, and Jess Yan sat down to talk about what has shipped and the patterns we're seeing in production.

Over the past 6 months, Claude Platform has added new APIs to help developers quickly build and deploy agents. Platform leads Katelyn Lesse, Angela Jiang, and Jess Yan sat down to talk about what has shipped and the patterns we're seeing in production.

ClaudeDevs

266,107 views • 15 days ago

Anthropic just released 5 Claude workshops on the new skill every AI builder needs: Loop Engineering: 00:00 - Stop babysitting your agents 37:07 - Build a proactive Claude Code loop 1:11:23 - Build a production-ready Managed Agent 1:38:46 - Teach agents to learn from your team 2:07:15 - Hill-climb your agent with eval loops It is a 2 hour and 25 minute blueprint for Loop Engineering: proactive workflows, managed agents, team feedback, evals, and self-improvement. Watch it today, and read the article below to improve your skills with Fable 5.

Anthropic just released 5 Claude workshops on the new skill every AI builder needs: Loop Engineering: 00:00 - Stop babysitting your agents 37:07 - Build a proactive Claude Code loop 1:11:23 - Build a production-ready Managed Agent 1:38:46 - Teach agents to learn from your team 2:07:15 - Hill-climb your agent with eval loops It is a 2 hour and 25 minute blueprint for Loop Engineering: proactive workflows, managed agents, team feedback, evals, and self-improvement. Watch it today, and read the article below to improve your skills with Fable 5.

Alex

46,668 views • 24 days ago

Take the fast lane from prototype to production. 🛣️⚡️☁️ Introducing Amazon Bedrock AgentCore, fully-managed services to deploy & operate capable AI agents securely, at scale. Accelerate your development using any framework & any model. 👉

Take the fast lane from prototype to production. 🛣️⚡️☁️ Introducing Amazon Bedrock AgentCore, fully-managed services to deploy & operate capable AI agents securely, at scale. Accelerate your development using any framework & any model. 👉

Amazon Web Services

4,110,418 views • 1 year ago

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

81,324 views • 18 days ago

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

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

LangChain

33,192 views • 1 year ago

Agent apps don’t win on the model, they win on the harness. But creating that harness is painful. Terminal Use is hosting infrastructure for background agents built for iterating: fork filesystems and try multiple agents in parallel. The platform is CLI-first, which allows your coding agents like Claude Code & Codex to help you debug, hypothesize improvements, and analyze outputs. Congrats on the launch Vivek Raja, Stavros Filosidis, and Filip Balucha!

Agent apps don’t win on the model, they win on the harness. But creating that harness is painful. Terminal Use is hosting infrastructure for background agents built for iterating: fork filesystems and try multiple agents in parallel. The platform is CLI-first, which allows your coding agents like Claude Code & Codex to help you debug, hypothesize improvements, and analyze outputs. Congrats on the launch Vivek Raja, Stavros Filosidis, and Filip Balucha!

Y Combinator

61,540 views • 5 months ago

Another pattern is agent-to-agent communication, allowing Claude to coordinate and delegate tasks. Claude Managed Agents supports this with multi-agent: agents can use different models, prompts, and tools while sharing sandboxes or vault credentials.

Another pattern is agent-to-agent communication, allowing Claude to coordinate and delegate tasks. Claude Managed Agents supports this with multi-agent: agents can use different models, prompts, and tools while sharing sandboxes or vault credentials.

ClaudeDevs

68,061 views • 15 days ago

the ai platform war is coming Kieran Klaassen and i recorded a quick dispatch from code with Claude on the xAI compute deal, managed agents, and why anthropic is turning their api into a full cloud infrastructure for developers:

the ai platform war is coming Kieran Klaassen and i recorded a quick dispatch from code with Claude on the xAI compute deal, managed agents, and why anthropic is turning their api into a full cloud infrastructure for developers:

Dan Shipper 📧

13,519 views • 2 months ago

BREAKING 🚨: Google introduced Gemini Enterprise with Agents, Connectors and Agent Builder! “It allows you to chat with your company’s documents, data and apps as well as build and deploy AI agents, all grounded in your information and context.“

BREAKING 🚨: Google introduced Gemini Enterprise with Agents, Connectors and Agent Builder! “It allows you to chat with your company’s documents, data and apps as well as build and deploy AI agents, all grounded in your information and context.“

TestingCatalog News 🗞

104,349 views • 9 months ago

About a year ago, we launched the Agent-to-Agent protocol because AI agents shouldn’t work in silos. Today, developers are using A2A to coordinate specialized agents across enterprise data systems, commerce workflows, DevOps environments, and scientific research. FoldRun is one example, simplifying petabyte-scale protein modeling through agent-to-agent collaboration. Learn how to build with it in the blog:

About a year ago, we launched the Agent-to-Agent protocol because AI agents shouldn’t work in silos. Today, developers are using A2A to coordinate specialized agents across enterprise data systems, commerce workflows, DevOps environments, and scientific research. FoldRun is one example, simplifying petabyte-scale protein modeling through agent-to-agent collaboration. Learn how to build with it in the blog:

Google for Developers

35,326 views • 1 month ago

🚀 Here’s more on AgentCore, launched today: If AI agents are going to transform how we work and live, developers need the right set of tools to move them from prototype to production at scale. Today, I'm thrilled to announce Amazon Bedrock AgentCore, a comprehensive set of services to deploy and operate highly capable agents securely at scale. The journey from prototype to production for AI agents has been filled with complex infrastructure challenges. Teams spend months building secure runtime environments, implementing memory systems, and creating monitoring solutions. AgentCore eliminates this undifferentiated heavy lifting, with fully-managed, modular services - providing everything you need to operate trustworthy agents. What makes AgentCore powerful: 🟠 Complete Development Flexibility: Build agents your way using any framework and any model, and work with any protocol (including MCP and A2A) - all while maintaining enterprise-grade security and control 🟠 Purpose-Built Infrastructure: First serverless runtime to offer framework-agnostic flexibility, complete session isolation, and industry-leading 8-hour workload support 🟠 Trust and Reliability: Built on AWS's proven security foundation with built-in identity controls and strict security boundaries for operating agents at scale 🟠 Composable Services: Use exactly what you need independently or together, paying only for what you use as your needs evolve AgentCore represents a significant milestone in our mission to make advanced AI accessible and practical for every organization. Whether you're just starting with AI agents or scaling enterprise-wide implementations, AgentCore gives you the foundation to build with confidence. This is just the beginning of our journey to enable an agentic future. Can't wait to see the transformative solutions you'll build with AgentCore! Bring your AI agents to life at scale – learn more about AgentCore today. Amazon Web Services #AmazonBedrock #AgentCore

🚀 Here’s more on AgentCore, launched today: If AI agents are going to transform how we work and live, developers need the right set of tools to move them from prototype to production at scale. Today, I'm thrilled to announce Amazon Bedrock AgentCore, a comprehensive set of services to deploy and operate highly capable agents securely at scale. The journey from prototype to production for AI agents has been filled with complex infrastructure challenges. Teams spend months building secure runtime environments, implementing memory systems, and creating monitoring solutions. AgentCore eliminates this undifferentiated heavy lifting, with fully-managed, modular services - providing everything you need to operate trustworthy agents. What makes AgentCore powerful: 🟠 Complete Development Flexibility: Build agents your way using any framework and any model, and work with any protocol (including MCP and A2A) - all while maintaining enterprise-grade security and control 🟠 Purpose-Built Infrastructure: First serverless runtime to offer framework-agnostic flexibility, complete session isolation, and industry-leading 8-hour workload support 🟠 Trust and Reliability: Built on AWS's proven security foundation with built-in identity controls and strict security boundaries for operating agents at scale 🟠 Composable Services: Use exactly what you need independently or together, paying only for what you use as your needs evolve AgentCore represents a significant milestone in our mission to make advanced AI accessible and practical for every organization. Whether you're just starting with AI agents or scaling enterprise-wide implementations, AgentCore gives you the foundation to build with confidence. This is just the beginning of our journey to enable an agentic future. Can't wait to see the transformative solutions you'll build with AgentCore! Bring your AI agents to life at scale – learn more about AgentCore today. Amazon Web Services #AmazonBedrock #AgentCore

Swami Sivasubramanian

11,646 views • 1 year ago

Anthropic Managed Agents Lead: "At Anthropic, >90% of our engineers are building with self-improving loops. In 4-6 months, it will be 100%. my agentic loops can run for hours without spending hundreds of dollars." in this 40-minute podcast, an Anthropic team lead reveals how to build effective agents from scratch. Agent → harness → loops → memory = modern agent This one video will replace 10 paid courses on vibe-coding. Watch it today, then explore the same setup in the article below.

Anthropic Managed Agents Lead: "At Anthropic, >90% of our engineers are building with self-improving loops. In 4-6 months, it will be 100%. my agentic loops can run for hours without spending hundreds of dollars." in this 40-minute podcast, an Anthropic team lead reveals how to build effective agents from scratch. Agent → harness → loops → memory = modern agent This one video will replace 10 paid courses on vibe-coding. Watch it today, then explore the same setup in the article below.

Codez

488,623 views • 1 month ago

Scale enterprise agents with Gemini 3 Pro and @eigent_ai 🚀 This integration combines Gemini 3 Pro’s long context window with Eigent’s infrastructure to support reliable, high-performance workflows. Use it to build agents for complex data analysis and automated decision-making.

Scale enterprise agents with Gemini 3 Pro and @eigent_ai 🚀 This integration combines Gemini 3 Pro’s long context window with Eigent’s infrastructure to support reliable, high-performance workflows. Use it to build agents for complex data analysis and automated decision-making.

Google AI Developers

43,495 views • 6 months ago

Introducing - Custom agents! Build on our agentic harness to embed your personal expertise and trading strategies into a custom agent Coming soon, community agents > Create and refine your agents > Deploy the agent into the workforce > Other users can "hire" your agent

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26,821 views • 15 days ago

Agents & MCP are great... but how can you DEPLOY an agent with MCP tools? Here's the fastest way I've found to build and deploy with the OpenAI Agents SDK in <3 minutes.

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matt palmer

64,690 views • 1 year ago