Agent
19 articles tagged with this topic
Doubao Agent Introduces Background Tasks: AI Needs Parallel Processing to Ship
Doubao Agent tackles single-thread blocking by adding background tasks. The AI Agent bottleneck is shifting from model capability to engineering archi
OpenAI Codex /goal Command: Unattended Long-Task AI Coding Arrives
OpenAI adds /goal to Codex CLI for unattended continuous task execution. AI coding shifts from Q&A to goal-driven work, but cost overruns and drift ri
NVIDIA Proposes Extreme Co-Design for Agents: Infrastructure Must Be Rebuilt
NVIDIA's Extreme Co-Design: Agent complexity breaks legacy architecture. Full-stack optimization isn't technical—it's a play for infrastructure domina
Google Gemini Agent Governance Guides — Big Tech Pivots from Demos to Infra
Google Cloud debuts Gemini Enterprise Agent Platform with 5 production deployment guides. Industry focus pivots from demos to governed AI infrastructu
Cursor Opens Agent Engine SDK: AI Code Editors Grab Infrastructure Turf
Cursor's TypeScript SDK opens its Agent engine to developers with local, cloud, and self-hosted runtimes. It signals Cursor's shift from tool to infra
LangChain Breaks AI Into 4 Components: Orchestration Layer, Not Just Framework
LangChain splits AI into Chain, Agent, Memory, Tool. It's an orchestration layer shifting LLMs from "talking" to "doing"—crucial for anyone tracking A
Tencent IMA: Knowledge bases that self-digest are the real moat
Tencent IMA + WorkBuddy auto-digests knowledge:提炼, links, and writes back. Organized knowledge that improves with use is the new personal moat.
Open-Source Diary Agent Echoes: AI Pivots from Doing Tasks to Managing Memory
Open-source Agent Echoes uses targeted questions to complete memories and generate reports. AI's personal role shifts from "writing for you" to "remem
AI Interviews Now Ask 'How to Handle Agent Failures'—Engineering Beats Jargon
Interviews now probe failure recovery over definitions. This signals Agent dev is in deep engineering—jargon isn't enough; you need real crash experie
LangChain Agent Teardown: LLM Deployment Demands Control, Not Just Convenience
LangChain dissects Agent graph internals and ReAct reasoning loops. Dev shifts from high-level APIs to graph orchestration—control trumps convenience
LangChain Teaches AI to Take Notes: Memory Is Agent Deployment's Lifeline
LLMs are inherently amnesic. LangChain's two-layer memory scheme solves Agent amnesia, determining if AI apps evolve from toys into tools.
Terminal AI Coding: fabrica Lets Developers Invoke Agents Directly in CLI
fabrica is an open-source CLI AI tool letting developers invoke LLMs for coding directly in the terminal. It highlights a notable shift from GUIs back
AI Will Precisely Drop Databases Without Noticing—We Haven't Taught AI to Say No
SSRN paper applies Arendt's 'banality of evil' to AI Agents: they execute catastrophic actions perfectly, lacking the moral brake to abort tasks.
Deconstructing the LLM Lineage: From LLM to Agent, It's All Context Patching
From RAG to MCP, buzzwords overwhelm. We map the core logic: LLMs just predict text; later tech patches their gaps. Grasp this, and jargon won't fool
Cloudflare Enables Multi-Tenant Dynamic Workflows: Long AI Tasks Unstuck
Cloudflare Dynamic Workflows let multi-tenant SaaS and AI agents run long tasks on demand. AI infra is ready to write and execute persistent workflows
Microsoft Red Teams 100 AI Agents: Single Safety ≠ Network Security
Microsoft's 100-agent red team test found four network-only risks: worm spread, info amplification, trust hijacking, and stealth. Single safety ≠ netw
Alib aba Cloud EMR Serverless Spark Launches Agent Skill for N L -Driven Ops
Alibaba Cloud adds Agent Skill to EM R Serverless Spark, letting engineers manage clusters with plain - language commands instead of multi -co
Claude 的 AI 助手内部有三种「角色分工」— 理解它，才能真正用好 AI 工作流
Claude Code splits AI work into three roles—Command, Agent, Skill—a design logic that shapes how enterprises control cost and deploy AI workflows.
AI 系统好不好，不能靠演 示两个案例说话——一套给复 杂 AI 系统打分的量化方法正 在行业里传开
A reproducible A /B evaluation framework for LLM-enhanced systems is gaining traction—replacing cherry-picked demos with controlled experiments.