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503 articles · Page 2 of 6
java1234
java1234
Jul 5, 2026 · Artificial Intelligence

9 Practical Tips for Efficient Spring AI 2.0 Agent Development

The article shares nine hands‑on tips for building Spring AI 2.0 agents—including using ChatClient as the entry point, delegating tool calls to ToolCallingAdvisor, defining tools with @Tool, crafting effective system prompts, leveraging Advisor chains, streaming responses early, managing conversation memory, limiting tool count, and adding observability—each illustrated with concrete code snippets.

AgentChatClientJava
0 likes · 12 min read
9 Practical Tips for Efficient Spring AI 2.0 Agent Development
Architecture Digest
Architecture Digest
Jul 3, 2026 · Artificial Intelligence

From Chatting to Getting Things Done: LLM, RAG, Function Calling & Harness in AI Travel Planning

The article walks through a step‑by‑step evolution of AI—from large language models and prompt engineering to retrieval‑augmented generation, function calling, agents, and harnesses—illustrated with a concrete travel‑planning scenario, showing how each technology adds real‑world capability.

AIAgentFunction Calling
0 likes · 12 min read
From Chatting to Getting Things Done: LLM, RAG, Function Calling & Harness in AI Travel Planning
AI Programming Lab
AI Programming Lab
Jul 3, 2026 · Databases

Why OceanBase’s AI Database Is More Than Just a Vector Store

The article analyzes how OceanBase’s new AI‑native database tackles the data‑layer challenges of autonomous agents by introducing logical tables, multi‑modal storage, Fork DB sandboxes and lake‑warehouse integration, arguing that the product’s value lies beyond simple vector retrieval.

AI DatabaseAgentFork DB
0 likes · 12 min read
Why OceanBase’s AI Database Is More Than Just a Vector Store
TonyBai
TonyBai
Jul 3, 2026 · Artificial Intelligence

20 Loop Design Patterns Every AI Engineer Should Know

The article presents twenty essential loop design patterns for industrial AI systems, explains how they differ from single‑call prompts, provides concrete examples, code snippets, and use‑case scenarios, and shows how these loops enable self‑improvement, memory, planning, exploration, and system optimization for AI agents.

AIAgentDesign Patterns
0 likes · 23 min read
20 Loop Design Patterns Every AI Engineer Should Know
Code Mala Tang
Code Mala Tang
Jul 2, 2026 · Artificial Intelligence

What Do AI Buzzwords Like LLM, Agent, and Skill Really Mean?

The article demystifies common AI terminology—LLM, Token, Context, Prompt, Tool, MCP, Agent, and Agent Skill—by explaining each concept, how they interrelate, and why understanding this chain clarifies the operation of modern AI products.

AI conceptsAgentLLM
0 likes · 11 min read
What Do AI Buzzwords Like LLM, Agent, and Skill Really Mean?
AI Engineer Programming
AI Engineer Programming
Jul 2, 2026 · Artificial Intelligence

Will Models Eventually Replace Harness Engineering? A Historical Analysis

The article traces the evolution of AI from early symbolic expert systems through connectionist, statistical, and deep learning eras, showing how increasingly powerful models have progressively subsumed handcrafted harnesses, and examines modern agent architectures, experimental evidence, and a six‑layer harness framework.

AIAgentHarness Engineering
0 likes · 17 min read
Will Models Eventually Replace Harness Engineering? A Historical Analysis
Baidu Geek Talk
Baidu Geek Talk
Jul 1, 2026 · R&D Management

Reversing Collaboration: Rebuilding Team Management with Agent Logic

The article flips the usual learning direction, using LLM Agent workflows—clear goals, role division, structured prompts, verification, dynamic routing, and rapid feedback—to expose hidden management truths, reduce information loss, and turn team processes into programmable, low‑entropy systems.

AgentSOPcollaboration
0 likes · 19 min read
Reversing Collaboration: Rebuilding Team Management with Agent Logic
Java Architecture Diary
Java Architecture Diary
Jul 1, 2026 · Artificial Intelligence

Spring AI Overhauls Memory: Replacing ChatMemory with Session

Spring AI’s new Session model replaces the fragile sliding‑window ChatMemory, introducing immutable Session metadata, event‑based Turn grouping, configurable compaction triggers and strategies, multi‑agent Branch isolation, and a JDBC‑backed repository to reliably handle long‑running tool‑calling agents.

AgentChatMemoryJava
0 likes · 10 min read
Spring AI Overhauls Memory: Replacing ChatMemory with Session
Architect
Architect
Jun 30, 2026 · Artificial Intelligence

Mastering Claude Code /loop: Turning Fragmented Tasks into Automated Workflows

This article explores Claude Code's /loop feature, showing how it can act as an in‑session observer to automate repetitive checks like CI status, deployments, and PR comments, while providing evidence, handling failures, and integrating with broader scheduling tools for reliable engineering workflows.

AI automationAgentCI monitoring
0 likes · 17 min read
Mastering Claude Code /loop: Turning Fragmented Tasks into Automated Workflows
DataFunSummit
DataFunSummit
Jun 30, 2026 · Industry Insights

From AI+BI to Enterprise AI Decision Intelligence: Introducing DecideX

The article analyzes why AI has struggled to enter core enterprise decision processes, proposes that the missing piece is accountable, context‑aware AI, and details how DecideX’s decision‑intelligence platform addresses this gap through a layered architecture, real‑world case studies, and a 5A implementation methodology.

5A MethodologyAIAI+BI
0 likes · 11 min read
From AI+BI to Enterprise AI Decision Intelligence: Introducing DecideX
Geek Labs
Geek Labs
Jun 28, 2026 · Industry Insights

Five Practical Open‑Source Projects: FPGA Inference, Agent Alignment, and Multi‑Server SSH Management

This article highlights five active GitHub projects—a Verilog‑based FPGA transformer inference engine, an AI agent personality alignment framework, a Zig‑written multi‑host SSH command tool, an AUR supply‑chain malware detector, and a real‑time phishing domain blacklist API—detailing their purpose, implementation, and key metrics.

AURAgentFPGA
0 likes · 7 min read
Five Practical Open‑Source Projects: FPGA Inference, Agent Alignment, and Multi‑Server SSH Management
Linyb Geek Road
Linyb Geek Road
Jun 28, 2026 · Artificial Intelligence

12 Pitfalls I Learned While Building AI Skills Over Six Months

Over the past half‑year the author built dozens of AI Skills, discovering twelve common traps—from over‑relying on prompts and bloated skill sets to vague descriptions, hidden token costs, knowledge placement, security gaps, and the need for proper evaluation—offering concrete guidance to avoid them.

AI SkillsAgentSecurity
0 likes · 11 min read
12 Pitfalls I Learned While Building AI Skills Over Six Months
Data Party THU
Data Party THU
Jun 27, 2026 · Artificial Intelligence

Defining a Good Answer in the Agent Era: A Rubrics Survey

This survey examines how rubrics—structured, multi‑dimensional evaluation criteria—are defined, constructed, and applied to train and evaluate large language models, especially for open‑ended, high‑risk and agentic tasks, while highlighting current challenges such as reward hacking and bias.

AI safetyAgentRubrics
0 likes · 15 min read
Defining a Good Answer in the Agent Era: A Rubrics Survey
Data Party THU
Data Party THU
Jun 26, 2026 · Artificial Intelligence

A Practical Guide to Loop Engineering: 14 Steps to Automate Repetitive Tasks

This article presents a 14‑step, evidence‑based guide for building Loop Engineering systems, explaining when to adopt loops, the five core components (Automations, Worktrees, Skills, Connectors, Sub‑agents), how to construct a minimal, safe loop, and the common failure modes and security risks to watch.

AI automationAgentDevOps
0 likes · 10 min read
A Practical Guide to Loop Engineering: 14 Steps to Automate Repetitive Tasks
DataFunTalk
DataFunTalk
Jun 26, 2026 · Artificial Intelligence

Why Prompts Are Obsolete and Loop Engineering Is the Next AI Paradigm

The article explains how the AI community is shifting from writing prompts to designing autonomous loops that iteratively execute, evaluate, and repeat tasks, detailing the technical differences from traditional agents, real‑world implementations like Claude Code and OpenAI Codex, and a step‑by‑step roadmap for building reliable loops.

AI LoopAgentHarness Engineering
0 likes · 13 min read
Why Prompts Are Obsolete and Loop Engineering Is the Next AI Paradigm
PaperAgent
PaperAgent
Jun 26, 2026 · Artificial Intelligence

13 Must-Read Agent Papers from Meituan for ICML'26

This article presents a curated list of thirteen recent research papers on generalist agents—covering visual memory, environment synthesis, value modeling, self‑verification, robustness benchmarks, high‑resolution video generation, long‑horizon world models, and alignment fine‑tuning—along with brief abstracts and links to the PDFs for the upcoming Meituan ICML'26 sharing sessions.

AIAgentBenchmark
0 likes · 16 min read
13 Must-Read Agent Papers from Meituan for ICML'26
AliExpress Tech
AliExpress Tech
Jun 26, 2026 · Artificial Intelligence

From LLM-as-Judge to Agent-as-Judge: A Technical Evolution

The article analyzes why using a large language model alone as a judge is unreliable for complex AI evaluation, introduces the Agent-as-Judge architecture with sandboxed execution, Skill‑driven workflows, and a three‑layer assessment framework, and discusses its benefits, costs, and practical outcomes.

AI testingAgentLLM
0 likes · 14 min read
From LLM-as-Judge to Agent-as-Judge: A Technical Evolution
AI Engineering
AI Engineering
Jun 25, 2026 · Artificial Intelligence

Why the Real Power of Agent Loops Lies Beyond Six Lines of Code

The article explains that while an Agent’s core loop is only a few lines of code, the real engineering challenges lie in prompt design, context management, tool selection, and safety checks that together determine the loop’s effectiveness.

AgentAnthropicLLM
0 likes · 8 min read
Why the Real Power of Agent Loops Lies Beyond Six Lines of Code
Sohu Tech Products
Sohu Tech Products
Jun 24, 2026 · Artificial Intelligence

LLM Agent Design Patterns: From ReAct to Multi‑Agent Collaboration

This article systematically reviews major LLM agent design patterns—including ReAct, CodeAct, static and dynamic planning, reflection, and human‑in‑the‑loop—detailing their core loops, code structures, trade‑offs, and practical use‑cases, and provides a decision tree to help developers choose the most suitable pattern for their tasks.

AgentCodeActLLM
0 likes · 37 min read
LLM Agent Design Patterns: From ReAct to Multi‑Agent Collaboration
DeWu Technology
DeWu Technology
Jun 24, 2026 · Artificial Intelligence

From Forms to AI Agents: Redesigning Community Event Workflows with LLM‑Powered Agents

The article chronicles how a marketing activity that required ten system switches and over forty manual fields was transformed by replacing simple AI‑assisted form filling with a two‑stage Agent architecture and an aggregated workbench, detailing the architectural choices, trade‑offs, and practical lessons learned.

AI workflowAgentLLM
0 likes · 20 min read
From Forms to AI Agents: Redesigning Community Event Workflows with LLM‑Powered Agents
ByteDance Data Platform
ByteDance Data Platform
Jun 24, 2026 · Artificial Intelligence

How AI Is Redefining Data Products: New Paths for Enterprise Intelligence

The article analyzes how the AI era shifts data from a passive by‑product to a core driver of large‑model performance, traces the evolution of data products from the DBA era through big‑data to AI‑native solutions, and details Volcano Engine’s four‑layer AI data platform that closes the data‑to‑model‑to‑Agent loop.

AIAgentData Lake
0 likes · 12 min read
How AI Is Redefining Data Products: New Paths for Enterprise Intelligence
ZhongAn Tech Team
ZhongAn Tech Team
Jun 22, 2026 · Artificial Intelligence

GLM‑5.2 Beats Claude Fable‑5 to Top AI Programming Rankings – Week of June 15‑21

The weekly roundup highlights GLM‑5.2’s leap to the top of AI‑coding leaderboards, new facial‑verification mandates for ChatGPT and Claude, a U.S. export ban on Claude Fable 5, industry shifts toward AI agents, breakthroughs in RAG with SAG, MoonBit’s rapid ecosystem growth, and deep dives into world‑model research and Loop Engineering.

AI programmingAgentGLM-5.2
0 likes · 34 min read
GLM‑5.2 Beats Claude Fable‑5 to Top AI Programming Rankings – Week of June 15‑21
DataFunSummit
DataFunSummit
Jun 21, 2026 · Artificial Intelligence

How OpenClaw Transforms Traditional Enterprise Data Asset Architecture

The article analyzes the limitations of conventional data asset architectures for AI, introduces OpenClaw's layered, operator‑driven platform design, details the three components of high‑quality datasets, and shares practical implementation insights and challenges from a real‑world deployment.

AI data architectureAgentHarness Engineering
0 likes · 13 min read
How OpenClaw Transforms Traditional Enterprise Data Asset Architecture
Machine Heart
Machine Heart
Jun 21, 2026 · Artificial Intelligence

Can World Models Bridge LLMs' Dynamic Reasoning Gaps?

The article analyzes why large language model agents struggle with dynamic tasks, critiques existing CoT‑style optimizations, and shows how recent world‑model approaches such as EvoAgent, WebEvolver, COMAP, RWML and ProPlay quantitatively improve prediction, planning and success rates in evolving environments.

AgentCoTEvoAgent
0 likes · 9 min read
Can World Models Bridge LLMs' Dynamic Reasoning Gaps?
Machine Heart
Machine Heart
Jun 18, 2026 · Artificial Intelligence

SAG: The New RAG SOTA That Delivers Sub‑Second Retrieval on 500 Million Records

SAG (SQL‑Retrieval Augmented Generation) introduces a hypergraph‑based event‑entity data model that combines SQL joins, vector similarity, and hyperedge reasoning to achieve 79%‑88% Recall@2‑5 with second‑level latency on a 500 M‑row corpus, outperforming GraphRAG and HippoRAG in multi‑hop tasks.

AIAgentBenchmark
0 likes · 14 min read
SAG: The New RAG SOTA That Delivers Sub‑Second Retrieval on 500 Million Records
Tech Ocean
Tech Ocean
Jun 18, 2026 · Artificial Intelligence

Master AgentScope Java’s 5 Core Blocks: Message, Agent, Model, Memory, Tool

This article breaks down AgentScope Java's five fundamental components—Message, Agent, Model, Memory, and Tool—explaining their roles, structures, code examples, provider switching, tool definition, and state management, while highlighting concurrency pitfalls and persistence options.

AgentAgentScopeJava
0 likes · 8 min read
Master AgentScope Java’s 5 Core Blocks: Message, Agent, Model, Memory, Tool
Frontend AI Walk
Frontend AI Walk
Jun 17, 2026 · Artificial Intelligence

From Manual Prompts to Self‑Driving AI Loops: Build Your First Loop System in 14 Steps

The article explains how most developers still manually prompt AI, introduces Loop Engineering as a way to automate prompt cycles, outlines a 14‑step roadmap—including a four‑condition test, five core components, risk mitigation, and a minimal viable Loop—so teams can decide when and how to adopt self‑driving AI coding loops.

AI codingAgentLoop Engineering
0 likes · 18 min read
From Manual Prompts to Self‑Driving AI Loops: Build Your First Loop System in 14 Steps
java1234
java1234
Jun 17, 2026 · Artificial Intelligence

Spring AI 2.0 GA: Native Java AI Development with Spring Boot 4 Integration

Spring AI 2.0 reaches GA, offering a production‑grade, Java‑first AI development path tightly integrated with Spring Boot 4.x, Spring Framework 7.0, and the Model Context Protocol, while introducing upgraded agent tooling, Jackson 3, JSpecify annotations, and streamlined provider SDKs.

AgentJackson 3Java
0 likes · 6 min read
Spring AI 2.0 GA: Native Java AI Development with Spring Boot 4 Integration
Alibaba Cloud Developer
Alibaba Cloud Developer
Jun 16, 2026 · Artificial Intelligence

AI Coding Needs Discipline: My Two‑Month Harness Framework Experience

The article analyzes why the bottleneck in AI‑assisted coding has shifted from model capability to workflow stability, introduces a three‑layer "harness" framework that externalizes discipline, details its evolution through four development phases, and presents a deterministic evaluation platform that quantifies the framework’s effectiveness.

AIAgentHarness
0 likes · 27 min read
AI Coding Needs Discipline: My Two‑Month Harness Framework Experience
Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
Jun 12, 2026 · Artificial Intelligence

How a Chinese Team Bypassed Fable 5’s Safety Classifier in Under 5 Seconds

Researchers from an international team demonstrated that the Anthropic Fable 5 model’s new safety classifier can be evaded in under five seconds with a single dialogue, exposing an internal safety collapse where agents autonomously generate harmful output during task execution, a flaw now confirmed across dozens of frontier LLMs.

AgentFable 5ISC-Bench
0 likes · 12 min read
How a Chinese Team Bypassed Fable 5’s Safety Classifier in Under 5 Seconds
JavaGuide
JavaGuide
Jun 12, 2026 · Artificial Intelligence

Integrating Agnes AI Free Tokens with Claude Code for Multimodal Tasks

This article walks through connecting the free Agnes AI multimodal API to Claude Code, detailing the required setup, a small Java code‑generation task, image and video generation examples, skill creation, and performance observations to help developers evaluate its suitability for their workflows.

AgentAgnes AIClaude Code
0 likes · 15 min read
Integrating Agnes AI Free Tokens with Claude Code for Multimodal Tasks
Architect
Architect
Jun 11, 2026 · Artificial Intelligence

Why More Automation Means More Human Judgment in Loop Engineering

Loop Engineering shifts focus from one‑off prompt engineering to continuous feedback loops that discover work, assign tasks, verify results, and record state, showing that the more automated the loop becomes, the more essential human judgment remains to define goals, budgets, and stop conditions.

AIAgentFeedback Loop
0 likes · 22 min read
Why More Automation Means More Human Judgment in Loop Engineering
Xike
Xike
Jun 11, 2026 · Artificial Intelligence

Adding Memory: Enabling Multi‑Turn Conversations in an LLM Agent

This guide demonstrates how to replace a simple message list with a ContextManager that tracks user and assistant turns, estimates token usage, applies a sliding‑window truncation based on a token budget, and provides a single build_for_llm entry point to keep multi‑turn dialogues stable and observable.

AgentContext ManagementLLM
0 likes · 11 min read
Adding Memory: Enabling Multi‑Turn Conversations in an LLM Agent
Design Hub
Design Hub
Jun 11, 2026 · Artificial Intelligence

My Design Harness Practice: Moving AI‑Generated Design from “Can Generate” to “Can Deliver”

The article presents a detailed engineering analysis of a Design Harness system that turns AI‑generated visual drafts into editable, verifiable, and exportable design assets through a six‑layer architecture covering user intent, brief contracts, aesthetic stance, tool registries, editable protocols, and verification loops.

AI designAgentDesign Harness
0 likes · 22 min read
My Design Harness Practice: Moving AI‑Generated Design from “Can Generate” to “Can Deliver”
Huolala Tech
Huolala Tech
Jun 10, 2026 · Information Security

Designing a Seamless, Secure CLI/SSO Auth System for Agent Skills

This article presents a token‑less, non‑exposed, multi‑user isolated CLI/SSO authentication framework for Agent‑driven Skill calls, detailing the security pain points, layered design of sso‑cli, sso‑sdk, poll‑based SSO, Feishu hook login, and future three‑dimensional access control.

AgentAuthenticationCLI
0 likes · 22 min read
Designing a Seamless, Secure CLI/SSO Auth System for Agent Skills
PaperAgent
PaperAgent
Jun 10, 2026 · Artificial Intelligence

Agent Era Information Retrieval: A Denoising-First Perspective (SIGIR 2026 Review)

The SIGIR 2026 review argues that as large language models become the primary consumers of retrieved results, information retrieval must shift its core objective from pure recall to denoising, presenting a five‑stage pipeline, controlled experiments, and a detailed attribution framework for noise sources.

AgentDenoisingInformation Retrieval
0 likes · 11 min read
Agent Era Information Retrieval: A Denoising-First Perspective (SIGIR 2026 Review)
Architect
Architect
Jun 9, 2026 · Artificial Intelligence

Rethinking Harness Engineering: Designing Deletable Workspaces for Real‑World Agents

The article analyzes Harness Engineering by breaking down the five layers of Agent systems—Model, Tool, Skill, Sub‑agent, and Harness—showing how to design a workspace that not only runs agents but also enables verification, hand‑off, correction, and the disciplined removal of outdated constraints.

AIAgentHarness Engineering
0 likes · 21 min read
Rethinking Harness Engineering: Designing Deletable Workspaces for Real‑World Agents
PaperAgent
PaperAgent
Jun 9, 2026 · Artificial Intelligence

Defining Standard Answers for Agent‑Era LLMs: A Rubrics Survey

The survey from RUC‑Gaoling AI Institute reviews Rubrics for large language models, explaining why they are needed for open‑ended, high‑risk tasks, how they are constructed, and how they can be applied to policy and reward model training as well as multi‑dimensional evaluation across general and domain‑specific scenarios.

AgentLLMRubrics
0 likes · 14 min read
Defining Standard Answers for Agent‑Era LLMs: A Rubrics Survey
Alibaba Cloud Developer
Alibaba Cloud Developer
Jun 9, 2026 · Artificial Intelligence

Scientific, Controllable Skill Self‑Evolution: Deep Dive into Trace2Skill, EvoSkill and SkillOpt

This article analyzes three recent papers—Trace2Skill, EvoSkill, and SkillOpt—detailing their methodologies for automatically evolving Agent Skills, comparing their assumptions, processes, strengths, and limitations, and offering guidance on selecting the appropriate approach for scalable, reliable skill self‑improvement.

AgentArtificial IntelligenceSelf-Evolution
0 likes · 33 min read
Scientific, Controllable Skill Self‑Evolution: Deep Dive into Trace2Skill, EvoSkill and SkillOpt
SuanNi
SuanNi
Jun 8, 2026 · Artificial Intelligence

Agent Harness Model Achieves Frontier Performance at <1% Compute Cost – Introducing Macaron‑V1‑Preview

A 30‑person lab trained a 749B‑parameter Agent model called Macaron‑V1‑Preview using fewer than 300 GPUs, achieving less than 1% of the compute cost of comparable models while matching state‑of‑the‑art performance on real‑world Agent benchmarks such as LivingBench, VitaBench, A2UI and PinchBench.

AIAgentBenchmark
0 likes · 15 min read
Agent Harness Model Achieves Frontier Performance at <1% Compute Cost – Introducing Macaron‑V1‑Preview
AliExpress Tech
AliExpress Tech
Jun 8, 2026 · Artificial Intelligence

Scaling Intelligent Q&A for Internationalized IC Using Spring AI Alibaba

The article details how the IC product team upgraded their single‑turn, low‑efficiency answer‑bot to a multi‑turn, Skills‑and‑MCP powered intelligent Q&A agent using Spring AI Alibaba, improving knowledge retrieval, reducing response time, and achieving over 80% automated resolution with extensive evaluation metrics.

AgentLLMMCP
0 likes · 28 min read
Scaling Intelligent Q&A for Internationalized IC Using Spring AI Alibaba
Tech Architecture Stories
Tech Architecture Stories
Jun 8, 2026 · Artificial Intelligence

From Prompt Frenzy to Agent‑Driven AI Workflow: A 200k‑Line Real‑World Project Case Study

The article details a practical AI‑driven development workflow built on OpenSpec and SuperPowers for a 200,000‑line Flutter‑Node music app, explaining how dual documentation (AGENTS.md and START_HERE.md), sub‑agent review loops, and a single‑command execution model enforce strict engineering constraints, reduce hallucinations, and automate code delivery.

AI workflowAgentFlutter
0 likes · 12 min read
From Prompt Frenzy to Agent‑Driven AI Workflow: A 200k‑Line Real‑World Project Case Study
Linyb Geek Road
Linyb Geek Road
Jun 8, 2026 · Artificial Intelligence

Harness Engineering: How OpenAI’s Agent‑First Approach Redefined Software Development

OpenAI’s five‑month experiment showed that by replacing manual coding with an "agent‑first" workflow—designing environments, building scaffolding, and automating feedback loops—engineers can produce a million lines of code, 1,500 PRs, and a fully functional product while spending only a tenth of the time traditionally required.

AgentArtificial IntelligenceCodex
0 likes · 22 min read
Harness Engineering: How OpenAI’s Agent‑First Approach Redefined Software Development
Smart Workplace Lab
Smart Workplace Lab
Jun 7, 2026 · Information Security

How to Secure Cross‑System Agent Calls with a Three‑Step Identity and Permission Routing

The article analyzes the security risks of agents using shared admin accounts for cross‑system calls and presents a three‑step method—identity mapping, dynamic session tokens, and over‑privilege circuit‑breaker—to enforce least‑privilege, reduce response time from days to minutes, and prevent data leakage.

AgentDynamic TokenIdentity Routing
0 likes · 7 min read
How to Secure Cross‑System Agent Calls with a Three‑Step Identity and Permission Routing
James' Growth Diary
James' Growth Diary
Jun 6, 2026 · Artificial Intelligence

How Honcho’s Dialectic User Model Lets Agents Learn Your Preferences Over Time

The article explains how Honcho transforms scattered conversation facts into a structured user model through a dialectic reasoning loop, detailing memory vs. user model differences, tool architecture, recall modes, prefetch caching, cost‑control mechanisms, peer cards, and common pitfalls for building ever‑more personalized AI agents.

AgentDialectic ReasoningHoncho
0 likes · 15 min read
How Honcho’s Dialectic User Model Lets Agents Learn Your Preferences Over Time
Architect
Architect
Jun 5, 2026 · Artificial Intelligence

When AI Accelerates Its Own Development, Where Do New Bottlenecks Appear?

Anthropic’s report shows Claude now contributes over 80% of code merges and speeds up the execution layer of AI research, shifting scarcity from implementation to goal definition, validation, and control, which raises urgent questions about governance, safety brakes, and research‑level harnesses.

AIAgentBottleneck
0 likes · 18 min read
When AI Accelerates Its Own Development, Where Do New Bottlenecks Appear?
Machine Heart
Machine Heart
Jun 4, 2026 · Artificial Intelligence

Defining Token Economics: A New Paradigm for LLM Agent Resource Allocation

The article introduces a systematic "Token Economics" framework that treats tokens as production factors, exchange media, and accounting units, and presents a four‑dimensional analysis of single‑agent to multi‑agent resource allocation, highlighting sustainability challenges and future research directions for LLM agents.

AI economicsAgentLLM
0 likes · 6 min read
Defining Token Economics: A New Paradigm for LLM Agent Resource Allocation
SuanNi
SuanNi
Jun 4, 2026 · Artificial Intelligence

Microsoft Build 2026: After Cutting Ties with OpenAI, Unveils 20+ New AI Models and Hardware Updates

At Microsoft Build 2026 the company announced over 20 updates, including the Surface RTX Spark Dev Box with 1 PFLOPS compute, Project Solara devices, seven self‑trained MAI models covering reasoning, vision, speech and code, Frontier fine‑tuning, the Scout Agent, new MXC security SDK, expanded Azure AI infrastructure and the Majorana 2 quantum processor.

AI modelsAgentBuild 2026
0 likes · 18 min read
Microsoft Build 2026: After Cutting Ties with OpenAI, Unveils 20+ New AI Models and Hardware Updates
DaTaobao Tech
DaTaobao Tech
Jun 3, 2026 · Artificial Intelligence

A Comprehensive Survey of Agent Memory: Benchmarks, Evaluation Frameworks, and System Designs

This article systematically reviews the state of agent long‑term memory by covering three core dimensions—benchmark datasets such as MUSE and LOCOMO, evaluation frameworks like MemoryAgentBench, LONGMEMEVAL and MemBench, and representative memory system implementations (THEANINE, RMM, M3‑Agent, Mem0)—while highlighting key capabilities, performance gaps, and future research directions.

AgentBenchmarkLLM
0 likes · 25 min read
A Comprehensive Survey of Agent Memory: Benchmarks, Evaluation Frameworks, and System Designs
Architect
Architect
Jun 2, 2026 · Artificial Intelligence

Why State Boundaries and Failure Loops Are Crucial for Agent Reliability After Harness

The article argues that as agents move from short, single‑shot tasks to long‑running workflows, reliability depends less on model correctness and more on clear state boundaries, evidence trails, and failure‑recovery loops that prevent erroneous submissions and make outcomes auditable.

AI reliabilityAgentFailure Recovery
0 likes · 20 min read
Why State Boundaries and Failure Loops Are Crucial for Agent Reliability After Harness
ITPUB
ITPUB
Jun 2, 2026 · Artificial Intelligence

Why Memory Architecture Remains Elusive: An In‑Depth Analysis of Agent Memory Systems

The article argues that memory for AI agents is not mere storage but a closed‑loop system comprising a raw ledger, derived views, and a policy layer, and examines how non‑parametric memory, time‑aware structures, and system‑2 control affect scalability, reliability, and performance.

AgentMemoryPolicy
0 likes · 45 min read
Why Memory Architecture Remains Elusive: An In‑Depth Analysis of Agent Memory Systems
Lin is Dream
Lin is Dream
Jun 2, 2026 · Artificial Intelligence

Exploring Agent Skill Management: Treating Agent Capabilities Like Software Packages

The article proposes a systematic Agent Skill Hub that organizes, versions, releases, deploys, and rolls back AI Agent capabilities using software‑package‑style practices, illustrated with a concrete image‑download skill, directory conventions, metadata files, and a Spring AI Alibaba runtime loading strategy.

AIAgentGitHub
0 likes · 15 min read
Exploring Agent Skill Management: Treating Agent Capabilities Like Software Packages
Old Zhang's AI Learning
Old Zhang's AI Learning
Jun 1, 2026 · Artificial Intelligence

Opus‑Distilled Qwen3.5‑Coder Scores 100/100 Tool Calls, 1.4‑2.2× Faster with MTP, 128K Context on Consumer GPU

The article introduces Qwopus3.5‑4B‑Coder‑MTP‑GGUF, a 4‑billion‑parameter agent model fine‑tuned for code debugging, tool calling, and structured reasoning, explains its novel Trace Inversion, high‑quality trajectory data, and Curriculum SFT training, details MTP acceleration, benchmark results, quantization options, and step‑by‑step local deployment instructions.

AgentBenchmarkGGUF
0 likes · 10 min read
Opus‑Distilled Qwen3.5‑Coder Scores 100/100 Tool Calls, 1.4‑2.2× Faster with MTP, 128K Context on Consumer GPU
AI Programming Lab
AI Programming Lab
Jun 1, 2026 · Artificial Intelligence

Claude Code Meets Step‑3.7‑Flash: Small Model, Big Multimodal Power

The article reviews Step‑3.7‑Flash, a high‑efficiency multimodal flash model designed for production‑grade agents, detailing its architecture, cost, benchmark results, native visual capabilities, integration with Claude Code via ccmr, and hands‑on experiments that illustrate its strengths and limits in multi‑step tasks.

AgentBenchmarkClaude Code
0 likes · 10 min read
Claude Code Meets Step‑3.7‑Flash: Small Model, Big Multimodal Power
Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
May 31, 2026 · Artificial Intelligence

Why Agent Reliability Needs More Than Bigger Models: Lessons from Harness Engineering

The article argues that the reliability of large‑model agents cannot be solved by scaling models or extending context windows; instead, a stable, auditable, and rollback‑capable runtime—what the author calls a State‑Aware Runtime—is essential for long‑term, industrial‑grade agent systems.

AgentHarness EngineeringLLM reliability
0 likes · 13 min read
Why Agent Reliability Needs More Than Bigger Models: Lessons from Harness Engineering
Linyb Geek Road
Linyb Geek Road
May 31, 2026 · Artificial Intelligence

From Prompt to Harness: The Three Evolutions of AI Engineering

The article traces AI engineering's three-stage evolution—from single‑turn Prompt Engineering, through multi‑turn Context Engineering, to system‑level Harness Engineering—explaining the problems each stage solves, the techniques introduced, concrete examples, and why the shift matters for scalable, reliable AI agents.

AI EngineeringAgentHarness Engineering
0 likes · 11 min read
From Prompt to Harness: The Three Evolutions of AI Engineering
James' Growth Diary
James' Growth Diary
May 30, 2026 · Artificial Intelligence

What the Agent Does While Idle: Asynchronous Background Review After a Conversation

The article explains Hermes' Background Review mechanism that triggers asynchronous self‑improvement after a dialogue ends, detailing trigger conditions, a forked sub‑agent architecture, prompt selection, cost‑saving cache inheritance, a four‑step skill‑update priority, result reporting, and common pitfalls.

AIAgentBackground Review
0 likes · 16 min read
What the Agent Does While Idle: Asynchronous Background Review After a Conversation
Machine Heart
Machine Heart
May 29, 2026 · Artificial Intelligence

Why Vendors Bet on Step 3.7 Flash: An Agent‑Optimized Model for High‑Cost AI

Step 3.7 Flash is an open‑source, sparse‑MoE flash model built for real‑world Agent workflows, offering 11 B active parameters, 400 TPS, 256 K context, multimodal perception and tool use, and achieves top‑tier scores on benchmarks such as ClawEval‑1.1, Toolathlon and SimpleVQA, while dramatically reducing token‑costs that have plagued large‑scale AI deployments.

AgentBenchmarkFlash
0 likes · 10 min read
Why Vendors Bet on Step 3.7 Flash: An Agent‑Optimized Model for High‑Cost AI
Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
May 28, 2026 · Artificial Intelligence

How PilotDeck’s Open‑Source Agent Cuts Token Costs by 70% with Parallel Workspaces

PilotDeck, an open‑source agent operating system from Tsinghua and partners, introduces isolated workspaces, transparent memory and smart routing that together reduce token expenses by up to 70% while keeping performance, and it demonstrates these gains through a milk‑tea game, a data‑visualisation dashboard, and a programmer‑personality test.

AgentMemoryOpenSource
0 likes · 12 min read
How PilotDeck’s Open‑Source Agent Cuts Token Costs by 70% with Parallel Workspaces
ZhiKe AI
ZhiKe AI
May 28, 2026 · Artificial Intelligence

Why Your LLM Skill Gets Ignored and 5 Proven Design Patterns to Make Agents Work

Even after spending hours crafting a Skill, many LLM agents ignore it, leading to failed automation; this article analyzes why and presents five validated design patterns—linear flow, decision tree with lazy loading, iterative loops, baton passing, and multi‑stage checkpoints—plus concrete examples and a minimal Skill template to ensure reliable, production‑grade agent behavior.

AgentDesign PatternsLLM
0 likes · 12 min read
Why Your LLM Skill Gets Ignored and 5 Proven Design Patterns to Make Agents Work
DataFunTalk
DataFunTalk
May 28, 2026 · Artificial Intelligence

The Most Comprehensive Survey on Agent Harness Engineering Revealed

This article summarizes the 71‑page survey "Agent Harness Engineering: A Survey", detailing the shift from prompt to context to harness engineering, introducing the seven‑layer ETCLOVG framework, benchmark results showing up to 10× gains, and arguing that future competition will focus on the engineering shell surrounding LLM agents rather than model size alone.

AI SystemsAgentHarness Engineering
0 likes · 15 min read
The Most Comprehensive Survey on Agent Harness Engineering Revealed
James' Growth Diary
James' Growth Diary
May 28, 2026 · Artificial Intelligence

How Agents Determine Which Skills Are Useful and Which to Retire

The article explains Hermes' skill provenance and usage‑tracking system, showing why file timestamps are insufficient, how three skill categories and two defense lines isolate agent‑created skills, how sidecar .usage.json records detailed counters, and how atomic writes and file locks ensure safe concurrent updates for accurate Curator decisions.

AgentConcurrencyHermes
0 likes · 16 min read
How Agents Determine Which Skills Are Useful and Which to Retire
Sohu Tech Products
Sohu Tech Products
May 27, 2026 · Backend Development

IDEA + JavaAI: A Hands‑On Review of Building a Mini‑Redis Spring Boot Starter

After struggling with AI‑generated code that failed on global edge cases, the author evaluates the FeiSuan JavaAI IDEA plugin, walking through its five‑agent workflow—from requirement planning to source generation—and demonstrates how it successfully creates a production‑ready mini‑redis Spring Boot starter with thorough testing.

AI code generationAgentIDEA
0 likes · 16 min read
IDEA + JavaAI: A Hands‑On Review of Building a Mini‑Redis Spring Boot Starter
Alibaba Cloud Native
Alibaba Cloud Native
May 27, 2026 · Artificial Intelligence

Quickly Build Enterprise Self‑Evolving Agents with AgentScope Builder and Harness Framework

This article presents a deep technical walkthrough of AgentScope Builder, showing how the Harness framework enables a single Java agent implementation to run on a personal machine as MinQwenPaw and then scale to a multi‑tenant, distributed enterprise platform with workspace isolation, sandboxing, and pluggable storage backends.

AgentArtificial IntelligenceJava
0 likes · 23 min read
Quickly Build Enterprise Self‑Evolving Agents with AgentScope Builder and Harness Framework
Bilibili Tech
Bilibili Tech
May 27, 2026 · Artificial Intelligence

How to Use A2UI + Vue to Enable Large Models to Generate Interactive Interfaces

This article details how a unified AI assistant framework built for Bilibili's advertising business evolves from plain text output to generating fully interactive UI by leveraging Google’s A2UI protocol, a custom Vue renderer, double‑validation mechanisms, SSE dual‑channel streaming, and a wrapper component system, providing concrete examples and architectural diagrams.

A2UIAgentGenerative UI
0 likes · 17 min read
How to Use A2UI + Vue to Enable Large Models to Generate Interactive Interfaces
Baidu Intelligent Cloud Tech Hub
Baidu Intelligent Cloud Tech Hub
May 27, 2026 · Artificial Intelligence

Optimizing Large Model Inference Architecture for the Agent Era: Engineering Practices and Challenges

The article analyzes the architectural challenges of large‑model inference in the Agent era—such as memory‑intensive MLA structures, MoE communication overhead, exploding KV‑Cache size, and tool‑call accuracy—and presents a series of engineering solutions including hierarchical KV‑Cache pooling, sequence parallelism, offloading strategies, and chip‑level adaptations to achieve higher throughput and lower token costs.

AI InfraAgentDeepSeek
0 likes · 15 min read
Optimizing Large Model Inference Architecture for the Agent Era: Engineering Practices and Challenges
James' Growth Diary
James' Growth Diary
May 27, 2026 · Operations

Detecting Agent Silent Killers: Early Alerts for Latency Spikes, Token Explosions, and Infinite Loops

The article presents a three‑layer monitoring system—LangSmith tracing, Prometheus metrics, and Alertmanager alerts—together with concrete metric definitions, alert rules, and code examples to proactively detect latency spikes, token overuse, and dead‑loop cycles in production LLM agents, while also outlining common pitfalls and best‑practice recommendations.

AgentCostAlertLLM
0 likes · 18 min read
Detecting Agent Silent Killers: Early Alerts for Latency Spikes, Token Explosions, and Infinite Loops
AI Step-by-Step
AI Step-by-Step
May 27, 2026 · Artificial Intelligence

Why Agent Context Management Prioritizes Information Over Shortening Prompts

The article breaks down the multi‑layered context of LLM agents, explains four management dimensions—capacity, content, structure, lifecycle—illustrates common failure scenarios, proposes four practical baselines, and maps maturity levels from free‑form heaps to full‑lifecycle orchestration.

AgentContext ManagementLLM
0 likes · 15 min read
Why Agent Context Management Prioritizes Information Over Shortening Prompts
SuanNi
SuanNi
May 26, 2026 · Artificial Intelligence

Why Tokens Are Burning Out and a Free Claude Opus 4.6‑Level Model Is Coming

The SkyClaw‑v1.0 model from Skywork AI offers a free, soon‑to‑be open‑source large‑language model for agent applications that matches Claude Opus 4.6 in performance while cutting token costs dramatically, and the article details its benchmarks, training pipeline, and deployment recommendations.

AgentBenchmarkOpenAI API
0 likes · 7 min read
Why Tokens Are Burning Out and a Free Claude Opus 4.6‑Level Model Is Coming
IT Services Circle
IT Services Circle
May 26, 2026 · Industry Insights

8 Must‑See Trending GitHub Open‑Source Projects This Week

This article curates eight rapidly rising open‑source projects—ranging from AI research agents and code‑graph knowledge bases to terminal‑based code editors, AI‑engineered video tools, and offline TTS systems—highlighting their star growth, core capabilities, and practical use cases for developers and researchers.

AIAgentGitHub
0 likes · 9 min read
8 Must‑See Trending GitHub Open‑Source Projects This Week
Tencent Cloud Developer
Tencent Cloud Developer
May 26, 2026 · Artificial Intelligence

How TencentDB Agent Memory Cuts Tokens by 61% and Boosts Success Rate 52% with Mermaid Infinite Canvas and Context Offloading

The article presents a technical deep‑dive into TencentDB Agent Memory’s short‑term memory compression, which combines context offloading and a Mermaid‑based infinite canvas to reduce token usage by up to 61 % while improving task success rates by over 50 % across multiple long‑session benchmarks.

AgentContext OffloadingLLM
0 likes · 45 min read
How TencentDB Agent Memory Cuts Tokens by 61% and Boosts Success Rate 52% with Mermaid Infinite Canvas and Context Offloading
James' Growth Diary
James' Growth Diary
May 25, 2026 · Artificial Intelligence

How Agents Turn a Single Success into a Reusable Skill

The article explains how Hermes separates memory from skills, automatically creates structured SKILL.md files from successful interactions, prioritizes updates over new creations, manages supporting files, tracks usage, and compares its approach with other agent frameworks, offering a detailed, code‑driven walkthrough of the entire skill‑generation pipeline.

AIAgentHermes
0 likes · 16 min read
How Agents Turn a Single Success into a Reusable Skill
The Dominant Programmer
The Dominant Programmer
May 25, 2026 · Artificial Intelligence

Mastering Structured Output in Spring AI: Getting Precise JSON from Large Language Models

This article walks through using Spring AI with Ollama to enforce JSON‑schema‑based structured output for agents, showing why structured responses matter, how Spring AI generates schemas from Java beans, and providing complete runnable code for both basic and advanced tool‑calling scenarios.

AgentFunction CallingJSON Schema
0 likes · 11 min read
Mastering Structured Output in Spring AI: Getting Precise JSON from Large Language Models
AI Engineer Programming
AI Engineer Programming
May 25, 2026 · Artificial Intelligence

From Demo to Production: Building a Reliable Agent Development Lifecycle

The article outlines a four‑stage agent development lifecycle—Build, Test, Deploy, Monitor—explaining how early, iterative delivery, systematic testing, controlled deployment, and continuous monitoring transform experimental agents into reliable production systems while addressing governance, cost, and scalability challenges.

AgentLangChaindeployment
0 likes · 16 min read
From Demo to Production: Building a Reliable Agent Development Lifecycle
phodal
phodal
May 24, 2026 · Artificial Intelligence

From Complex Editors to Agent Workbenches: Office’s AI Cursor Moment

The article analyzes how AI agents are reshaping Office document editing by turning traditional editors into agent‑driven workbenches, detailing the generation, editing, and verification loops required to produce reliable PowerPoint files and outlining the three criteria—locatable, comparable, verifiable—that enable this transition.

AIAgentOffice
0 likes · 12 min read
From Complex Editors to Agent Workbenches: Office’s AI Cursor Moment
Spring Full-Stack Practical Cases
Spring Full-Stack Practical Cases
May 23, 2026 · Artificial Intelligence

Auto‑Splitting AI Agent Tasks and Real‑Time Monitoring with Spring AI + TodoWrite

This article explains how the TodoWriteTool, a Spring AI extension, solves large‑language‑model “mid‑session forgetting” by automatically splitting complex agent tasks into explicit, sequential subtasks and providing real‑time progress monitoring, with a complete Spring Boot 3.5.0 setup, code examples, and a runnable demonstration.

AgentJavaSpring AI
0 likes · 7 min read
Auto‑Splitting AI Agent Tasks and Real‑Time Monitoring with Spring AI + TodoWrite
SuanNi
SuanNi
May 22, 2026 · Artificial Intelligence

Why Qwen3.7-Max Is Sending Overseas Developers Into a Frenzy

Qwen3.7-Max demonstrates product‑level long‑task autonomy with 35 hours of uninterrupted operation, 1,158 tool calls, and kernel‑level optimizations, while outperforming Gemini 3.5‑Flash, Claude Opus, and GPT‑5.5 across a wide range of benchmarks, cost‑effectiveness, and real‑world agent scenarios.

AIAgentBenchmark
0 likes · 11 min read
Why Qwen3.7-Max Is Sending Overseas Developers Into a Frenzy
DataFunTalk
DataFunTalk
May 21, 2026 · Databases

How the Agent Paradigm Is Redefining Enterprise Data Infrastructure

The article examines how the rise of AI agents is reshaping enterprise data infrastructure, tracing software evolution from rule‑based systems to lakehouses and arguing that real‑time OLAP engines with sub‑second latency, hybrid search, and semantic schemas will become the core of the new Agent‑centric stack.

AgentLLMLakehouse
0 likes · 13 min read
How the Agent Paradigm Is Redefining Enterprise Data Infrastructure
FunTester
FunTester
May 21, 2026 · Artificial Intelligence

How Anthropic Solves Agent Forgetfulness with Event Persistence

The article explains why in‑memory state is unreliable for long‑running or parallel agents, defines event persistence, shows how persisted event records enable checkpoint‑restart, observability, and experience extraction, and outlines practical guidelines for what to record.

AIAgentCheckpoint Restart
0 likes · 10 min read
How Anthropic Solves Agent Forgetfulness with Event Persistence
大转转FE
大转转FE
May 21, 2026 · Artificial Intelligence

Why AI Buzzwords Multiply Faster Than My Hair Falls

The article maps three generations of AI engineering—Prompt Engineering, Context Engineering, and Harness Engineering—explaining their core capabilities, key terms like LLM, RAG, Agent, and evaluation methods, while offering practical tips, pitfalls, and a concise three‑question checklist to stay grounded amid the rapid influx of new AI jargon.

AIAgentHarness
0 likes · 19 min read
Why AI Buzzwords Multiply Faster Than My Hair Falls
Code of Duty
Code of Duty
May 20, 2026 · Artificial Intelligence

Why Large Language Models Aren’t Magic: The Simple AI Principle Everyone Can Understand

The article demystifies large language models by explaining their core task of next‑token prediction, tokenization, vector semantics, Transformer attention, massive training, hallucination risks, prompt design, and tool integration, showing how these mechanisms work together and why verification is essential.

AI hallucinationAgentAttention
0 likes · 14 min read
Why Large Language Models Aren’t Magic: The Simple AI Principle Everyone Can Understand
Old Zhang's AI Learning
Old Zhang's AI Learning
May 20, 2026 · Artificial Intelligence

Qwen 3.7‑Max vs Claude 4.7: 7 In‑Depth Tests Reveal a Smooth, Powerful Model

The author evaluates Alibaba’s newly released Qwen 3.7‑Max across seven rigorous tasks—including reading comprehension, HTML fireworks generation, 3D particle visualizations, PDF‑to‑PPT conversion, Excel data analysis, GitHub trending scraping, and complex video generation—showing it often surpasses GPT‑5.5‑level models and rivals Claude 4.7, especially in long‑duration agent tasks.

AI benchmarkAgentClaude 4.7
0 likes · 9 min read
Qwen 3.7‑Max vs Claude 4.7: 7 In‑Depth Tests Reveal a Smooth, Powerful Model
Machine Heart
Machine Heart
May 20, 2026 · Artificial Intelligence

Qwen3.7-Max Sets New Agent Benchmarks – China’s New Model King

Alibaba’s Qwen3.7‑Max model tops multiple Arena leaderboards, achieves SOTA scores in programming, reasoning, and multilingual benchmarks, runs a 35‑hour autonomous coding task on a custom AI chip with 10× speedup, and demonstrates end‑to‑end desktop app creation and web‑search agents, illustrating a rapid monthly model‑iteration strategy.

AI chipAgentAlibaba
0 likes · 13 min read
Qwen3.7-Max Sets New Agent Benchmarks – China’s New Model King