AI Agents Explained: What They Are and How to Build One in 2026
AI agents have moved from research demos to production infrastructure faster than almost any technology in recent memory. Companies deploying them are reporting 171% ROI on auto...
https://wowhow.cloud/blogs/ai-agents-explained-how-to-build-2026
Dan McAteer (@daniel_mac8)
Axe라는 프로젝트(또는 설계철학)는 LLM 에이전트들을 유닉스가 프로그램을 다루는 방식처럼 텍스트 스트림으로 다룬다고 설명합니다. 이는 무거운 프레임워크 대신 단순한 텍스트 스트림 접근을 선호하며, 에이전트 설계의 경량화와 단순성을 강조하는 관점입니다.
Bought a new Mac mini to properly tinker with claws over the weekend. The apple store person told me they are selling like hotcakes and everyone is confused :) I'm definitely a bit sus'd to run OpenClaw specifically - giving my private data/keys to 400K lines of vibe coded
fly51fly (@fly51fly)
Google 연구팀의 'Self-Evolving Recommendation System: End-To-End Autonomous Model Optimization With LLM Agents' 논문 공개. LLM 에이전트를 활용해 추천 시스템 모델을 엔드투엔드로 자율 최적화하고 스스로 진화시키는 접근을 제안하며, 추천 시스템 자동화 및 자율화 측면의 실용적 진전을 다룬 연구이다(저자 H. Wang, Y. Wu, D. Chang, L. Wei..., arXiv 2026).
Rohan Paul (@rohanpaul_ai)
LLM 에이전트 생태계가 복잡해진 가운데, 새롭고 작고 단순한 파이썬 라이브러리 'KISS Multi Agent Evolutionary Framework'를 시도해본 후기입니다. 이 프레임워크는 최소한의 절차와 가독성 높은 코드, 평범한 파이썬 사용과 단일 run() 호출로 에이전트를 구성하는 단순함을 지향한다고 소개합니다.
The AI agent ecosystem has become a jungle. Just tried this new, small, and super simple Python library for building LLM agents. I really liked its minimal ceremony and readable code. KISS Multi Agent Evolutionary Framework KISS tries to keep it to plain Python and 1 run()
AIトレンド速報｜最新ニュース & 活用術 (@AI_Bridge_Japan)
Agent Reinforcement Trainer(ART)를 공개했습니다. GRPO로 멀티스텝 LLM 에이전트를 실세계 작업 수행 가능하게 훈련하는 프레임워크로, 몇 줄의 코드로 구현 가능하며 수동 보상 설정이 불필요합니다. vLLM과 Unsloth 통합, 100% 오픈소스이며 개발자는 @akshay_pachaar입니다.
AgentOCR zeigt, dass LLM-Agenten ihre immer länger werdende Interaktionshistorie als kompakte Bilder speichern können und dabei >95% der Leistung bei >50% weniger Tokens halten.
Wer Agenten produktiv betreiben will, braucht Memory-Governance: adaptive Kompression, Caching/Segmentierung, und klare Policies, wann Informationsdichte zugunsten von Kosten/Latency reduziert werden darf.
#LLMAgents #EfficientAI #MultimodalAI
https://arxiv.org/html/2601.04786v1
🔬Meet µ𝐒𝐭𝐚𝐜𝐤—an AI-powered platform that democratizes atomistic microscopy simulations! 🚀
💥LLM-driven structure generation → ML-based relaxation → GPU-accelerated simulations
Big thanks to our team @blaiszik, Kevin, and Piyush 🤗 & hackathon organizers! 🙌
Related links in comment👇
Expedia is turning the GenAI Playground into a productivity hub, tapping OpenAI, Anthropic and Google LLMs to build custom agents that surface partner insights and streamline internal workflows. Discover how these tools reshape travel tech. #ExpediaAI #GenAIPlayground #LLMAgents #OpenAIAnthropic
🔗 https://aidailypost.com/news/expedia-boosts-partner-insights-internal-productivity-via-genai
🚀 New from CrewAI: function‑based guardrails that let you enforce rule‑based constraints on LLM outputs. Perfect for language‑model agents and prompt engineers who need reliable, task‑specific behavior. Dive into the Analytics Vidhya breakdown and see how these guardrails can keep your AI safe and open‑source friendly. #CrewAI #FunctionGuardrails #LLMAgents #TaskGuardrails
🔗 https://aidailypost.com/news/crewai-introduces-function-based-guardrails-rulebased-output
Anup Karanjkar
sayzard
N-gated Hacker News
Harald Klinke
Aritra Roy
AI Daily Post