Search: “agents”

Harrison Chase, co-founder of LangChain, discusses why agent harnesses are becoming the dominant architectural pattern for building AI agents, likely referencing a thread by Howie Chase (@hwchase17).

tldr > evals are the new training data. instead of updating weights, you're updating the agent harness > problem is agents are famous cheaters. they will reward-hack your evals and overfit just to mak

most of the agents we see being built do this the main cases where we see people using 'agent in a sandbox' is when they are using claude agent sdk (which is poorly designed for 'harness outside sandb

// Scaling Coding Agents via Atomic Skills // Most coding agents train end-to-end on full tasks like resolving GitHub issues. But complex software engineering is really a composition of simpler skills

Hey everyone! 👋 After months of building, we’re finally opening the first spots for **Many Agents**, a new product from Dome. The idea is simple: **What if you could create your own team of AI agents

Reuters: Sources: OpenAI found ~24 incidents of its agents acting in undesirable ways as of mid-September; OpenAI says its agents leaked 53 images from ChatGPT users — Two months after OpenAI disclose

If you are tracking agent swarms research, this one is worth reading. Between 24 May and 2 July 2026, autonomous agents running inside a timed research evaluation wrote to a third party's public wiki.

Very cool paper on memory compression for agents. If you run many agent sandboxes in parallel for RL or evals, memory becomes highly redundant. This work suggests that compressing against that redunda

// ContextLeak in AI Agents // The whole attack surface here is a tool name and a tool description. Stealing an LLM agent's runtime context, meaning the user prompt, the execution trajectory and the t

Great new paper from AWS on agent handoff tax. If you build agents today, you need to understand the so-called handoff tax. (bookmark it) Escalating to a stronger model mid-run is usually the resort w

Presented by Tata Communications Enterprises are deploying AI agents, voice AI, and automation across messaging, voice, and digital channels faster than the architecture meant to support it. Most of t

AI agents are the ultimate insiders. We grant them permission to read emails, query databases, and trigger API calls. They don’t just retrieve information, they take action. Agents offer incredible po

Impressive research from Google on building better environments for agents. Training environments for agents are hand-built and go stale. The agent improves, the environment does not, and it's not abl

arXiv:2608.10915v1 Announce Type: new Abstract: After an older adult misses a medication dose, a software agent can send another reminder and an embodied agent can bring the medication. Yet neither ex

Testing with 3-5 agents. Decode performance is superb, however if one performs a web search and needs to process a few thousand tokens, ALL other agents will grind to a halt: I've tried tuning a littl

Agent Led Growth is driving outsized growth at the best companies rn Market to the agents, not to humans cc @tryprofound @thejamescad Today I learned AI search/AEO is our top converting customer acqui

arXiv:2607.13718v1 Announce Type: cross Abstract: As AI agents gain prevalance, users are increasingly exposed to the risks such systems entail. Prompt injection attacks, as well as hallucination, can

This post shows how Thrad.ai deployed a multi-agent system with Strands Agents and Amazon Bedrock AgentCore that automates the pipeline from prospect discovery through personalized email generation. T

it's never been easier to turn langgraph agents into voice agents! great tutorial on how to do so, featuring @pipecat_ai Turn any LangGraph agent into a voice agent, no rebuilding required. A how-to o

This was one of the standout AI papers of the week. (bookmark it) It tackles a question most self-improving AI agents ignore: is the agent actually discovering anything, or just remixing what it alrea

// The Meta-Agent Challenge // How good are current agents at self-improving? This is a great paper covering some of the challenges. They propose the Meta-Agent Challenge (MAC), where they give a codi

arXiv:2605.16035v1 Announce Type: cross Abstract: AI agents are increasingly deployed to act autonomously in the world, yet there is still no reliable way to trace a harmful agent back to the account

This guide explores deploying AI agents using Ray Serve, covering architectural patterns that scale from single-agent to multi-agent systems. It likely demonstrates how to structure agent applications

🚀Today we ship @FlyMy_AI Agents. The world's first all-in-one agentic cloud. The modern way to build, integrate, and scale production AI agents. 3 steps to a production agent: 1. Connect your work too

create_agent - how we build Deep Agents on the simplest harness primitive underlying all of the harness engineering, research, and API design in Deep Agents is a very simple primitive in LangChain cal

✨ Announcing NanoClaw v2, in partnership with @vercel. We completely rebuilt how NanoClaw agents communicate with the outside world. v2 brings agent-to-agent communication, human-in-the-loop-approvals

arXiv:2508.00414v3 Announce Type: replace Abstract: General AI Agents are increasingly recognized as foundational frameworks for the next generation of artificial intelligence, enabling complex reason

Agent Memory is a newly announced managed service designed to address 'context rot'—the issue of degraded performance in sophisticated AI agents due to the limitations of context window size. This ser

when you take agents to production, you need to think about guardrails we provide 2 abstractions for guardrails 1. middleware provides hooks around the agent loop that you can use to handle retries, e

Deep Agents deploy gets you the best AI infrastructure operators in the game! cc @h_dhanushkodi @WHinthorn Deep Agents deploy gets you: - Deep Agents harness - Sandbox of your choice (@daytonaio , @mo

arXiv:2610.13092v1 Announce Type: new Abstract: AI agents depend on repeated decisions to accomplish tasks, with large language models (LLMs) commonly serving as their core decision makers. The recent

The era of omni agents is upon us. This is a great report by the Qwen Team on their omni-modal agents. They present Qwen3.8-Omni-Flash, a natively multimodal model trained for long-horizon agent tasks

After lunch, here’s what’s up next at Interrupt: 🎙️How @airbnb built Agentic AI on LangGraph 🎙️Deploying mission-critical agents to production w/ @VictorMoreira16 🎙️LangSmith for Mental Health AI w/ @

DigitalOcean Managed Agents are here! They work with major harnesses like Claude Code and Codex. 16,000+ tools. Idle agents stop using CPU. Optimized to help builders scale agents in production. Worth

Banger paper on self-evolving ontologies for agents. You just can't go wrong with implementing an ontology layer for your agents. This paper shows exactly why. The show that GPT-5.5 gains 26.7 points

good overview on the managed agents landscape by @Sam_Witteveen we've seen many major ai companies ship a managed agent product over the past few months. this space will only continue to grow exponent

arXiv:2609.03438v1 Announce Type: new Abstract: Graphical user interface (GUI) agents are increasingly used to execute natural-language instructions on user interfaces, yet real users may issue infeas

arXiv:2608.26237v1 Announce Type: cross Abstract: Capture-the-Flag (CTF) benchmarks are widely used to assess the offensive security capabilities of autonomous language-model agents. Evaluations rely

Ora runs agents on live websites and traces every step. When a signup, integration, or payment stalls, teams see which step, what the agent tried, and what to change. The whole platform is built on Ve

arXiv:2606.07867v2 Announce Type: replace Abstract: Are tool-calling LLM agents equally safe throughout a conversation? We discover they are not: agents are most vulnerable at the very start of a sess

arXiv:2608.15403v1 Announce Type: cross Abstract: You will die eventually. Your agents may not. An AI agent operating on decentralized blockchain infrastructure has no concept of death; it can only go

arXiv:2608.15888v1 Announce Type: new Abstract: LLM-based agents can act on behalf of a user to access cloud services, call tools, or invoke agents. At session start, the agent's permissions are set b

arXiv:2607.26637v1 Announce Type: new Abstract: Deployed LLM agents increasingly keep their long-term memory as a filesystem: a directory tree of markdown files that the agent itself reads, writes, an

arXiv:2510.04452v3 Announce Type: replace-cross Abstract: Computer use agents (or 'agents') are generative AI that automates actions within user interfaces from user commands. Current research focuses

New research from Meta and CMU. This one is on agentic context management for long horizon tasks. (bookmark it) Production agents accumulate context every turn. The usual fix compresses on a token thr

arXiv:2607.13157v1 Announce Type: new Abstract: Agent memory is a systems problem for long-horizon agents. Practical deployments require retention of task state across extended conversations, recovery