Search: “agents”

With closed agent platforms like Claude Managed Agents, your agent's memory belongs to them, not you. It's locked behind their API. Agent infra should be open: open harness, open memory, model agnosti

Building agents that patch other agents. This is an interesting approach for self-improving agents that leverages agent outputs. If you run agents in production, you already have the training data for

free agents from Harrison? I’m in 🫡 drop the agent of your dreams below and it’ll (prob) magically exist soon i want an agent that helps me riff on ideas for tweet-length visual essays and researches

Pay attention to this one, AI devs, especially if you're thinking about agentic commerce or any agent network where many agents share hosts. A correct route to a cold agent is still a failed request f

🚀Deep Agents deploy Today we’re launching Deep Agents deploy in beta. Deep Agents deploy is the fastest way to deploy a model agnostic, open source agent harness in a production ready way. Open harnes

Sub-agent Model Selection — Different Tasks, Different Models Your main agent runs Qwen3.6-Plus for quality. But not every subtask needs a flagship model. Now sub-agents can use a different model. Cre

Evals ~= Environments…they’re one of the best investments a team can make for improving agents Step 0: Turn On Tracing for Agents Step 1: Point compute at Traces to understand agent behavior, segment

Harrison Chase demonstrates creating a custom CLI agent using LangChain's deep agent functionality, highlighting the ease and simplicity of building autonomous agents with modern frameworks. The post

Introducing Agent Plugins, an open standard for extending agents. Supports Agent Skills and MCP, with more to come. Built in collaboration with: @awsdevelopers, @code, @cursor_ai, @github, and @openai

This is true of all agents, not just coding agents. Probably the biggest challenge that most companies run into in their agent strategy is getting agents the right constrained context to work with for

Introducing Mesa: the most powerful filesystem ever built, designed specifically for enterprise AI agents. Every team building agents eventually hits the same wall: where do the files live? Not the ch

Track usage and costs across your users, agents and tools. We're also shipping better access controls for your tools & agents: - Set spend limits on agents - Set spend limits for users - Control what

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

introducing momo, the CRM for AI agents. it gives agents its own CRM, like Salseforce/Hubspot for humans. every AI native company will need dev agents, sales agents, customer support agents, HR, legal

There is an asymmetry in most agentic workflows that does not get talked about much: humans have many ways to talk to agents, and almost no standardized way for agents to talk back to humans. You can

We have to be careful to not offload our understanding to agents. I think there is also a good opportunity to build agentic applications that encourage deeper understanding. For example, coding agents

🧠 Paradigm II — Agent Foundation Model: world modeling as agent capability. Single-turn, non-agentic environment prediction → tested directly on multi-turn, tool-calling agent tasks. No agentic RL, no

great write up, evals are hard! here are 2 broad buckets we use to evaluate agents: 1. Measure the State of the World 2. Agent as a Judge on the Trajectory 1. Measure the state of the environment befo

Progressive disclosure in agents doesn't scale. And its benefits seems agent harness dependent. (bookmark this one) Finally there is a proper study on using agent skills and the effect of progressive

create_agent is the easiest way to get started building an agent! it provides an incredibly flexible interface for building agents designed around your use case! we just revamped the create_agent docs

Introducing Kimi K2.6 from @Kimi_Moonshot, a multimodal agentic model with Agent Swarm scaling to 300 sub-agents and long-horizon coding stability. AI natives can now use Kimi K2.6 on Together AI and

LangChain launched **Deep Agents Deploy** in beta as an open-source, model-agnostic alternative to Anthropic's Claude Managed Agents. It is designed to be the fastest way to deploy a model-agnosti...

I wrote an article on the core Agent architecture for Row-Bot. How it uses a @LangChain LangGraph agent at its core for the main agent, background workflows and child agents. Each is a full LangGraph

// Scaling Behavior of Single LLM-Driven Multi-Agent Systems // Does adding more agents actually make a multi-agent system better? It's possible that collective intelligence emerges from interaction d

Deep Agents deploy gets you: - Deep Agents harness - Sandbox of your choice (@daytonaio , @modal , @RunloopDev ) - Short and long term memory - Agents exposed via MCP and A2A Production ready and open

Turn any LangGraph agent into a voice agent, no rebuilding required. A how-to on: ✅ Making LangGraph the LLM layer of a voice agent built with @pipecat_ai ✅ Tracing your entire voice agents – from aud

Progressive disclosure keeps agents focused. Join us at Interrupt May 13-14 for a conversation with @levie about @Box + Deep Agents https://interrupt.langchain.com/ AI agents work better when they bri

Harrison Chase, co-founder of LangChain, discusses the concept of the agent 'harness' as the core architectural component where an agent's decision-making logic and identity reside. The post expresses

The Log is the Agent 它的核心观点是：不要把日志当成 agent 的副产品，而要把 append-only event log 当成 agent 本身。 在 ActiveGraph 里，目标、规则、工具调用、LLM 响应、产物和关系都写入同一条事件日志；当前状态只是日志 replay 后投影出来的一张图。 这样带来三个关键能力： 可确定性重放 可在任意事件处分叉实验 从最终输出

Managed Agents through the Gemini API is @GoogleAI's response to Anthropic Managed Agents Since it's powered by the new Antigravity agent built on Gemini 3.5 Flash, it is the most cost-effective gener

// Multi-Agent Synthesis RAG // Nice paper on improving RAG systems with multiple agents. (bookmark it) The paper introduces MASS-RAG, a multi-agent synthesis framework for retrieval-augmented generat

YT Podcast → LLM Artifact This is now my favorite way to consume podcasts. Knowledge artifacts generated by agents. The agent (Opus 4.7) spots important insights, does deep analysis, and generates tho

// Skill Learning for Autonomous Web Agents // Web agents can navigate a page, but ask them to repeat a checkout flow they already completed, and they start from scratch every time. This work introduc

LangChain co-founder Harrison Chase (@hwchase17) highlighted the ability to deploy a production-ready agent to the web using the same configuration setup — such as an `AGENTS.md` file — used to def...

Vercel is positioning itself as a full-stack 'Agentic Infrastructure' platform, recognizing that weekly deployments have doubled in three months, with over 30% now initiated by coding agents — up ...

I agree with what this AI paper suggests. Self-improving agents should evolve their benchmarks too. (bookmark it) Self-improving agents are one of the most important directions in AI right now, and mo

// An Anatomy of CLI Coding Agent Trajectories // (bookmark it) When your coding agent fails a task, when did the run actually go wrong? Most reliability studies use the final label to answer this. Th

Creating AI voice agents has become ridiculously easy xAI’s Grok Voice Agent Builder lets you deploy a human-like voice agent in under 2 minutes - no coding required It is currently in beta, but the s

New in Deep Agents: Agent Rubrics! Attach a rubric to your agent invocation, and a grader evaluates and self-corrects output until it satisfies all requirements. This is helpful for long/complex tasks

Evals shape agent behavior. Every eval is a vector that shifts the behavior of your agentic system. More evals ≠ better agents. Instead, build targeted evals that reflect desired behaviors in producti

// Agentic World Modeling // Massive 40-author survey just dropped. Cleanest taxonomy of world models in agent research I've seen. (bookmark it) The paper proposes a 'levels × laws' framework. Three c

if you want claude's managed agents w/o model provider lock in, this is the path! `deepagents deploy` lets you deploy an agent built on our model agnostic, open source harness in minutes you can easil

a prompt i like to use when having agents review other agent generated PRs tell the agent your coworker who you don't trust/think is that smart put up the PR, and it'll tend to find improvements/optim

Been going deep in learning @Langchain ‘create_deep_agent’ Turns out create_deep_agent = create_agent (the model <-> tools loop) & a default middleware stack++ 2 nodes do the work, the rest are middle

Managed Deep Agents lets you create a managed Deep Agent without standing up a custom agent server. Our runtime supports: ✅ Durable threads ✅ Streaming runs ✅ Checkpointing ✅ Human-in-the-loop workflo

Replit's Parallel Agents feature enables users to run up to 10 agents simultaneously, with each agent operating on its own independent copy of an application to accelerate development and task complet