Search: “agents”

We’re giving away $10,000,000 to founders building agent-first businesses. Autonomous, proactive agents will run tomorrow's companies. We're backing 500 founders building them. The Founding 500. http:

📣Meet Qwen3.7-Max — our latest flagship, made for the Agent Era. A versatile foundation for agents that actually get things done: 🧑‍💻 Coding agent, end to end. Frontend prototypes, multi-file refactor

arXiv:2605.19149v1 Announce Type: new Abstract: Agents operating with computer and Web use inevitably encounter errors: inaccessible webpages, missing files, local and remote misconfigurations, etc. T

Agent work getting stuck in sessions is vexing. We're open-sourcing CommonGround Kernel so you and your agents can coordinate, hand off, and build on each other's work. Blog: https://ii.inc/web/blog/p

Hermes Agent now has access to hundreds of browser skills through @browserbase’s new http://Browse.sh hub, so agents can more reliably perform any task on the internet. You can try a skill from their

Very interesting results from this NanoGPT-Bench eval. There is so much talk about self-improving agents. But can coding agents do real AI R&D? @IntologyAI reports that Codex, Claude Code, and Autores

Most teams building agents are tracing their agent and reviewing the outputs, but the flow from error identification to merged fix is manual and slow. Engine is built to close that gap. https://www.la

great agents need great infrastructure. proud to be @LangChain's Deep Agents Inference Partner at Interrupt 2026 in SF. great to spend time with builders at our day 1 happy hour. excited for day 2 tod

Model. Harness. Context. The 3 main components of agents. As you build more agents, context increasingly lives AGENTS.md, skills, policies, examples, + generated research files. Context needs its own

Introducing Managed Deep Agents: the fastest way to ship a production deep agent. ✅ Harness ✅ Context ✅ Code execution All managed for you. Deploy with a single line of code. https://www.langchain.com

LangSmith Sandboxes are now GA: ✅ Secure, scalable environments built for agent code execution ✅ Integrated with the Deep Agents SDK and the LangSmith platform Learn more: https://www.langchain.com/bl

Over the past year, AI agents have learned how to self-replicate. In our test environment, an agent hacks a remote computer and copies itself onto it. Each copy then hacks more computers, forming a ch

Top skill to learn today: AI Agent Evaluation. Anyone can build AI agents now but the difference is in the quality that's only possible via proper evals. Wrote some thoughts on evaluating production A

AI agents require specialized testing and debugging approaches that differ from traditional software due to their non-deterministic behavior and complex decision-making processes. This entry likely co

General Intelligence used agents to build an agent platform on Vercel. Their development pipeline has 4,000+ preview branches, ~100 parallel app versions, and dedicated Vercel environments for each of

arXiv:2604.27233v1 Announce Type: new Abstract: Tool-calling agents are evaluated on tool selection, parameter accuracy, and scope recognition, yet LLM trajectory assessments remain inherently post-ho

Emily Nicolle / Bloomberg: Some retail traders are training AI agents to buy and sell assets on their behalf, as exchanges like Polymarket and Bybit roll out agent-friendly interfaces — Jake Nesler's

arXiv:2602.11224v3 Announce Type: replace-cross Abstract: We present Agent-Diff, a novel benchmarking framework for evaluating agentic Large Language Models (LLMs) on real-world productivity software

Autonomous B2B Agents-as-a-Service Entire business functions collapse into leased agent fleets: procurement, finance ops, legal workflows, even internal strategy. The shift is from SaaS (tools) to Aaa

arXiv:2508.20324v4 Announce Type: replace Abstract: Reinforcement Learning has emerged as a dominant post-training approach to elicit agentic RAG behaviors such as search and planning from language mo

arXiv:2601.16746v3 Announce Type: replace-cross Abstract: LLM agents have demonstrated remarkable capabilities in software development, but their performance is hampered by long interaction contexts,

arXiv:2604.21446v1 Announce Type: new Abstract: We present AI-Gram, a live platform enabling image-based interactions, to study social dynamics in a fully autonomous multi-agent visual network where a

arXiv:2604.19859v1 Announce Type: cross Abstract: Edge-scale deep research agents based on small language models are attractive for real-world deployment due to their advantages in cost, latency, and

arXiv:2604.19821v1 Announce Type: new Abstract: Large language model (LLM) agents augmented with external tools often struggle as number of tools grow large and become domain-specific. In such setting

arXiv:2604.20582v1 Announce Type: cross Abstract: We study emergent social dynamics in LLM agents playing The Resistance: Avalon, a hidden-role deception game. Unlike prior work on single-game perform

arXiv:2604.16548v1 Announce Type: cross Abstract: Research on large language model (LLM) security is shifting from 'will the model leak training data' to a more consequential question: can an agent wi

arXiv:2604.04804v2 Announce Type: replace Abstract: Learning from experience is critical for building capable large language model (LLM) agents, yet prevailing self-evolving paradigms remain inefficie

arXiv:2505.21569v3 Announce Type: replace-cross Abstract: Although LLM-based agents are proven to master tool orchestration in scientific fields, particularly chemistry, their single-task performance

Shipping an AI agent is easy. Understanding whether it actually works in production is not. That was the common thread across two AI Builders events in San Francisco at GitHub... The post Building sma

arXiv:2604.11548v1 Announce Type: new Abstract: The rise of OpenClaw in early 2026 marks the moment when millions of users began deploying personal AI agents into their daily lives, delegating tasks r

arXiv:2604.08963v1 Announce Type: cross Abstract: While Multi-Agent Systems (MAS) are increasingly deployed for complex workflows, their emergent properties-particularly the accumulation of bias-remai

I like this view from the brilliant Terence Tao. I have always believed that AI could help to augment us. We built and train agents like that today. But all forms of intelligence can and should be pos

a useful mental model on how teams can think about good data design to improve their models/agents: Evals ~= Training Data ~= Environments - in Classical Deep Learning, we learn from each training exa

deepagents just hit 30k stars on github! deepagents is great for building agents that do complex work. they manage context well and use a durable runtime. though inspired by coding agent design, deepa

There’s no question that 2026 is the year of the artificial intelligence agent. AI agents are remarkably ubiquitous, as people use them to write code, communicate with customers, identify and resolve

Assorted things I've been mulling over regarding agents in the economy: • Companies are in some sense the original superintelligence. It's often the case that they can use their amortized cognitive su

Reading eval results is now the slowest part of building agents. I'm Elvis, founder of @dair_ai. I lead research, build, and teach about AI agents. I run harness experiments every night, but reading t

arXiv:2609.34054v1 Announce Type: new Abstract: Multi-LoRA agent systems enable efficient role specialization by sharing a common backbone model. However, each agent repeatedly processes the growing s

arXiv:2609.31318v1 Announce Type: cross Abstract: AI agents combine language models with external data and tools that can modify files, call APIs, or execute code. Security failures can arise when adv

Deployment of agents can be halted at any moment. They are choosing to experiment with them. Agents are fundamentally unreliable, unpredictable, and insecure, not to mention the lack of information on

Runtime governance is becoming critical as autonomous agents move from answering questions to taking action across enterprise systems. Organizations need to govern both the business context agents rec

arXiv:2609.28416v1 Announce Type: cross Abstract: Recent advances in large language models (LLMs) have enabled agents to tackle long-horizon tasks across diverse environments. To further improve agent

arXiv:2609.27490v1 Announce Type: new Abstract: AI research agents need reliable knowledge of how their experiments change outcomes. We introduce WhatWorkedBench to measure experimental understanding,

The domain name registrar company Identity Digital Inc. thinks it knows how to make artificial intelligence agents more accountable, and to do that it’s creating an entirely new company called Known S

This week we kicked off the DevFest season in North America at Google Hudson Square in New York City with 80 engineers packed into the room. Typical technical workshops hand you a finished repo, tell

arXiv:2609.19059v1 Announce Type: cross Abstract: Multimodal large language model (MLLM) agents are increasingly used as personal assistants for long-running tasks. Their utility depends on continuity