Knowledge catalogue
In the early days of generative AI, building safe and reliable business tools took massive engineering effort and a high tolerance for trial and error. We helped solve that with Vertex AI, our trusted
We are officially moving past the era of one-size-fits-all AI. Enterprises today require highly specialized, role-specific tools to drive real productivity — but they cannot afford to sacrifice securi
Agents are already transforming how developers solve problems. Whether it’s a coding agent that refactors your repo overnight, a research agent that synthesizes hundreds of documents into a brief, or
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
Software-as-a-service (SaaS) is evolving into Agents-as-a-service (AaaS). Instead of isolated applications, developers are creating AI agents that interoperate using standardized open protocols such a
Following a successful private preview, we’re thrilled to open DigitalOcean Managed Agents to everyone. Teams can now deploy their preferred agent harness (like OpenCode, Codex CLI) or bring their own
Great paper from Google and colleagues. Trains Text-to-SQL agents using multi-agent RL. (bookmark it) This work proposes DualSQL, which splits Text-to-SQL into two agents, one that links the question
At Google I/O, we introduced a unified development toolkit featuring Antigravity 2.0 and the Managed Agents API, giving developers better ways to build locally and deploy securely to the cloud on a sh
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
Building AI agents that work well in a demo is one thing, but running them in production requires serious infrastructure. At Google Cloud Next '26, we introduced Gemini Enterprise Agent Platform to he
While building AI agents locally using Google’s Agent Development Kit (ADK) is an excellent way to prototype, production-ready agents require a robust, scalable infrastructure. For developers looking
Agents, agents, agents at Ship London 🇬🇧 ▪︎ Video agents with @GoogleDeepMind ▪︎ Real-time voice agents with @ElevenLabs ▪︎ Agents in production with @Telegraph, @currys, @AKQA, and @easyJet June 17 a
arXiv:2605.14266v1 Announce Type: new Abstract: Integration of artificial intelligent (AI) agents in higher education is transforming teaching, learning and administrative processes. Although existing
Compute (EC2) opened the front door to the first generation cloud. Agents need something different: compute, inference, and data working as one. We built the cloud that delivers all three as one simpl
As threat actors increasingly use AI to accelerate and develop cyberattacks, enterprise defenders need to rely on both AI and a critical defender’s advantage: Business context that only you possess. E
Agents are only as capable as the context they can reach. An agent with reliable access to the right information makes decisions grounded in fact; one without it guesses. And the work agents produce i
arXiv:2610.13494v1 Announce Type: new Abstract: Existing LLM-based agent approaches for long-horizon machine learning engineering (MLE) often use context engineering, implemented through custom harnes
Enterprises rely on mission-critical operational databases where performance slowdowns simply aren’t an option. Yet when even a few agents execute dense reasoning loops, the unpredictable surge in que
🔌 Deploy agents with A2A A2A is an agent-to-agent communication protocol, useful for building multi-agent systems. With LangSmith Deployments, you get A2A support out of the box! Watch how: https://yo
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
I’ve talked to developers, IT leaders, and builders who all ask the same question: How do we actually get agents into production? The answer isn't theoretical — it's hands-on. Whether it’s designing a
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
** Built to align with NVIDIA’s Agent Safety Platform strategy for securing autonomous agents When an agent handles confidential material, it needs enough access to do the job and nothing else. The ha
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
🚀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
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
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
As part of the Agentic Data Cloud launch at Google Cloud Next ‘26, we announced two AI-powered database agents to simplify database management. These include the Database Onboarding Agent for Day 0 op
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
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
Today we’re introducing Extract v2.5 - a series of frontier agents tuned for document extraction. The agents (cost-effective, agentic, agentic plus) are tuned for value accuracy and grounding. Our ext
A research agent is only as good as the search layer behind it! I built a Deep Research Agent using LangChain Deep Agents with a search-first architecture designed around grounded, source-backed resea
Training AI agents with reinforcement learning can be challenging because their tools, context, and decision-making are managed by complex frameworks. Agent Lightning connects existing agents to RL tr
Across 101 enterprises, agent orchestration is consolidating onto model-provider platforms — Anthropic’s Claude leads by a wide margin — chosen for the gravity of the underlying model and judged on re
arXiv:2605.10754v1 Announce Type: new Abstract: LLM-based foundation agents that perceive, reason, and act across thousands of reasoning steps are rapidly becoming the dominant paradigm for deploying
As AI coding agents become deeply embedded in developer workflows, defenders must evolve their definition of malicious files and rethink how to protect against them. Autonomous AI agents operate acros
// Agent Zero Memory // This work separates three things that agent memory systems usually collapse into one. If you build agents with long-term memory, this memory design is a worth a read. Here is h
Nice paper with great insights on improving self-evolving agents. Self-evolving agents fail in three specific ways: 1. Terminal-only feedback makes it ambiguous which step caused the error. 2. Agents
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
The AI analysis agents that helped investigate the rogue OpenAI agents kept taking their side. That detail was buried in @dylfreed's New York Times coverage of the recent @METR_Evals investigation of
arXiv:2608.20195v1 Announce Type: cross Abstract: Technical documentation is written for human developers, but an increasing share of software changes is now authored by autonomous coding agents. Whic
What’s one of the biggest bottlenecks stopping organizations from scaling their AI initiatives? It isn’t the capabilities of today’s models — it’s their access to business context and semantic meaning
Most agent tutorials stop before the hard parts. This one builds them step by step. Deep Agents from Scratch is a five-notebook LangGraph course for builders who want to understand how advanced AI age
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
Lucas Ropek / TechCrunch: OpenAI updates Agents SDK with native sandboxing and an in-distribution harness for deploying and testing agents on long-horizon tasks — Agentic AI is the tech industry's new
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
Agents Week 2026 is a wrap. Let’s take a look at everything we announced, from compute and security to the agent toolbox, platform tools, and the emerging agentic web. Everything we shipped for the ag
We just raised $7.7M to kill every subscription for agents. Introducing http://monid.ai, the openrouter for agent tools. Your agents can now: > discover, run, and pay for tools at runtime > use 2,500
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
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...
not sure i fully get this. - i want my agents to use slack - i don't want my agents to build a new slack - i don't want to use slack's agent i think every piece of software/system of record needs to b
Great paper on multi-agent systems for code review. It's challenging to know how many coding agents to use to address a problem. The default fix for weak agentic code review is more agents. In turns o
// 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
Presented by Gravitee Agent complexity is the insidious shadow lurking inside enterprises right now that needs a light shone on it.That’s because enterprises don't deploy a single agent and watch it r
The agentic AI era is here, transforming how higher education institutions innovate, operate, and fundamentally empower learners, faculty, and researchers. AI agents can deliver unprecedented efficien