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79 articles · Page 1 of 1
AI Large-Model Wave and Transformation Guide
AI Large-Model Wave and Transformation Guide
Aug 16, 2026 · Artificial Intelligence

Agent Core Capabilities: A Full Breakdown from Interview Question to Agent Architecture

The article explains that interviewers expect a detailed decomposition of an Agent's architecture, covering seven tightly linked capabilities—perception, planning, memory, tool use, action, reflection, and their closed‑loop cooperation—rather than a vague claim of merely calling a large model.

AI architectureAgentMemory
0 likes · 5 min read
Agent Core Capabilities: A Full Breakdown from Interview Question to Agent Architecture
Big Data and Microservices
Big Data and Microservices
Aug 9, 2026 · Artificial Intelligence

Four Core Design Patterns that Power AI Agents

The article explains why simply using a smarter model isn’t enough, showing that applying four fundamental AI‑agent design patterns—Reflection, Tool Use, Planning, and Multi‑Agent Collaboration—can raise GPT‑3.5’s HumanEval success from 48 % to over 95 %, and outlines how each pattern works, their trade‑offs, and practical implementation guidance.

AI AgentDesign Patternsmulti-agent
0 likes · 11 min read
Four Core Design Patterns that Power AI Agents
Linyb Geek Road
Linyb Geek Road
Jul 24, 2026 · Artificial Intelligence

Inside Claude Code’s Harness: How Loops, Planning, Sandboxes, and Memory Power AI Programming

The article dissects Claude Code’s harness control framework, showing how a simple execution loop, planning layer, sandboxed tools, hierarchical delegation, and persistent memory—implemented with CrewAI—enable reliable AI‑driven code fixing, while highlighting trade‑offs, overhead, and future limitations.

AI agentsClaude CodeCrewAI
0 likes · 17 min read
Inside Claude Code’s Harness: How Loops, Planning, Sandboxes, and Memory Power AI Programming
samdeepthink
samdeepthink
Jul 18, 2026 · R&D Management

Three CTOs, Three Lessons: The Key Habits They Taught Me

After working with four CTOs, the author highlights three valuable habits learned from three of them—systematic planning, staying hands‑on with code, and aligning technology decisions with business needs—that continue to shape his management approach today.

CTObusiness alignmenthands-on coding
0 likes · 6 min read
Three CTOs, Three Lessons: The Key Habits They Taught Me
ThinkingAgent
ThinkingAgent
Jul 16, 2026 · Artificial Intelligence

Agent Framework: From Personal Assistants to Process Integration and Enterprise Intelligence

The article explains how AI agents differ from chatbots, outlines four core design patterns—Reflection, Tool Use, Planning, and Multi‑Agent Collaboration—draws on insights from DeepLearning.AI, Anthropic, Google Cloud, LangChain and Microsoft, and provides a step‑by‑step roadmap for evolving agents from personal assistants to enterprise‑wide intelligent systems.

AI AgentAgentic WorkflowEnterprise AI
0 likes · 28 min read
Agent Framework: From Personal Assistants to Process Integration and Enterprise Intelligence
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
ThinkingAgent
ThinkingAgent
Jun 21, 2026 · Artificial Intelligence

The 6‑Layer Architecture of AI Agents: Perception, Planning, Tools, Memory, Execution, and Feedback

This article breaks down the complete cognition‑action system of modern AI agents into six inter‑connected layers—Perception, Planning, Tools, Memory, Execution, and Feedback—explaining their core problems, engineering designs, common pitfalls, and best‑practice metrics with concrete code examples and real‑world use cases.

AI agentsAgent ArchitectureMemory Management
0 likes · 40 min read
The 6‑Layer Architecture of AI Agents: Perception, Planning, Tools, Memory, Execution, and Feedback
samdeepthink
samdeepthink
Jun 18, 2026 · Product Management

Why Most Product Managers Are Just Assistants (And What Real PMs Need)

The article argues that most so‑called product managers are merely product assistants, outlines the true responsibilities of a genuine PM, and lists essential traits such as logical thinking, clear planning, and rigorous PRDs to avoid resource waste and low delivery efficiency.

PM skillsPRDProduct Lifecycle
0 likes · 4 min read
Why Most Product Managers Are Just Assistants (And What Real PMs Need)
Linyb Geek Road
Linyb Geek Road
Jun 11, 2026 · Artificial Intelligence

From Reactive to Self‑Evolving: The Four‑Stage Evolution of AI Agents (2023‑2026)

The article maps the 2023‑2026 evolution of AI agents across four distinct stages—reactive ReAct, workflow‑driven, autonomous, and self‑evolving—while dissecting how the six core modules (Prompt, Planning, Memory, Tools, Workflow, Environment) shift from model‑centric to engineered determinism.

AI agentsAgent ArchitectureMemory
0 likes · 10 min read
From Reactive to Self‑Evolving: The Four‑Stage Evolution of AI Agents (2023‑2026)
Coder Trainee
Coder Trainee
Jun 7, 2026 · Artificial Intelligence

AI Agent Deep Dive: Understanding Planning, Memory, Tools, and Action

This article revisits the AI Agent architecture and provides a detailed analysis of its four core components—Planning, Memory, Tools, and Action—covering mainstream planning strategies, memory types, tool specifications, and execution loops, accompanied by concrete LangChain code examples that demonstrate building a fully integrated multi‑component agent.

AI AgentLangChainMemory
0 likes · 12 min read
AI Agent Deep Dive: Understanding Planning, Memory, Tools, and Action
PaperAgent
PaperAgent
Jun 5, 2026 · Artificial Intelligence

The Most Systematic 102‑Page Review of Agent Harnesses

This article provides a comprehensive overview of the "Code as Agent Harness" paradigm, detailing its three‑layer architecture, the roles of code in reasoning, acting, and environment modeling, the mechanisms that enable reliable long‑term execution, and how multi‑agent systems scale the harness through shared code and feedback loops.

Code as AgentLLMMemory
0 likes · 10 min read
The Most Systematic 102‑Page Review of Agent Harnesses
Alibaba Cloud Developer
Alibaba Cloud Developer
May 22, 2026 · Artificial Intelligence

How Core Agent Concepts and Paradigms Have Evolved and the Rationale Behind Them

The article traces the evolution of AI agents from early ReAct‑style models through workflow‑based systems to autonomous and self‑evolving agents, analyzing six core dimensions—Prompt, Planning, Memory, Tools, Workflow, and Environment—and explains why each paradigm shift occurred, citing recent frameworks and research.

AI agentsMemory ManagementPrompt Engineering
0 likes · 25 min read
How Core Agent Concepts and Paradigms Have Evolved and the Rationale Behind Them
AI Illustrated Series
AI Illustrated Series
Apr 25, 2026 · Artificial Intelligence

How Agents Work: Inside Their Perception, Planning, Action, and Memory

This article breaks down an AI agent's workflow—perception, planning, action, and memory—using a product‑launch example, explains reasoning methods like Chain‑of‑Thought and ReAct, details tool integration, memory types, common failure modes, and why planning and tool ecosystems are essential.

AI AgentMemoryReAct
0 likes · 11 min read
How Agents Work: Inside Their Perception, Planning, Action, and Memory
ZhiKe AI
ZhiKe AI
Apr 19, 2026 · Artificial Intelligence

What Is an AI Agent? A 3‑Minute Beginner’s Guide

An AI Agent is a large‑model system that can perceive its environment, plan steps, invoke tools, and remember past interactions to autonomously achieve user‑specified goals, distinguishing it from simple chatbots that only answer questions.

AI AgentAutomationMemory
0 likes · 6 min read
What Is an AI Agent? A 3‑Minute Beginner’s Guide
Data STUDIO
Data STUDIO
Apr 10, 2026 · Artificial Intelligence

Tree of Thoughts Architecture: Enabling AI to Explore Multiple Reasoning Paths

This article introduces the Tree of Thoughts (ToT) reasoning framework, explains its search‑tree based workflow, demonstrates a full implementation with LangGraph to solve the classic wolf‑goat‑cabbage puzzle, and compares its reliability against a simple Chain‑of‑Thought approach.

AI reasoningLLMLangGraph
0 likes · 19 min read
Tree of Thoughts Architecture: Enabling AI to Explore Multiple Reasoning Paths
Architect
Architect
Mar 27, 2026 · Artificial Intelligence

Turning AI Agents into a Planning‑First Development Engine

The article analyzes Matt Van Horn’s workflow that replaces traditional IDEs with a planning‑first approach using markdown task files, voice input, parallel research agents, and a custom "last30days" engine, showing how AI can orchestrate research, planning, execution, and verification to boost productivity across code and non‑code tasks.

AIAgentsAutomation
0 likes · 24 min read
Turning AI Agents into a Planning‑First Development Engine
AI Engineer Programming
AI Engineer Programming
Mar 25, 2026 · Artificial Intelligence

What Is an AI Agent? Definition, Core Capabilities, and Architecture

The article explains AI agents as autonomous systems that perceive environments, plan, use tools, iterate through action loops, and self‑reflect, contrasting them with traditional chatbots and workflows, and outlines their core abilities, memory types, tool‑use mechanisms, and single‑ versus multi‑agent architectures.

AI AgentMemoryarchitecture
0 likes · 8 min read
What Is an AI Agent? Definition, Core Capabilities, and Architecture
AgentGuide
AgentGuide
Mar 19, 2026 · Artificial Intelligence

What Exactly Is an AI Agent? Complete Interview Guide

This article breaks down the concept of AI agents for interview preparation, covering their definition, core components like planning, memory, and tool use, differences from plain LLM chats, real‑world challenges, typical use cases, detailed component analysis, and a runnable pseudo‑code example.

AI AgentLLMMemory
0 likes · 9 min read
What Exactly Is an AI Agent? Complete Interview Guide
Architect's Ambition
Architect's Ambition
Mar 18, 2026 · Artificial Intelligence

From Zero to a Real AI Agent: Master Its Core Essence, Not Just API Calls

The article explains why an AI Agent is more than a simple LLM API call, outlines its four essential modules—memory, planning, tool use, and feedback—shows how they differ from ordinary models, and offers practical steps and common pitfalls for building a production‑grade single‑agent system.

AI AgentLLMMemory
0 likes · 13 min read
From Zero to a Real AI Agent: Master Its Core Essence, Not Just API Calls
Architect's Ambition
Architect's Ambition
Mar 16, 2026 · Artificial Intelligence

Understanding AI Agents: From Chatting to Getting Things Done

The article explains the four essential components of AI Agents—brain, memory, tool, and planning layers—illustrates their implementation with Python code, compares planning strategies, shares a real-world OOM fault‑diagnosis case, and lists common pitfalls to help newcomers build functional agents.

AI AgentLLMMemory Management
0 likes · 17 min read
Understanding AI Agents: From Chatting to Getting Things Done
Architect
Architect
Feb 13, 2026 · Artificial Intelligence

Cutting Agent Costs: Practical Tips from the ‘Toward Efficient Agents’ Survey

The article analyzes why autonomous LLM agents become expensive, breaks down their cost components, and presents concrete engineering strategies—memory management, tool‑call optimization, and planning constraints—to dramatically reduce token usage and improve reliability while maintaining performance.

LLM Agentscost optimizationplanning
0 likes · 19 min read
Cutting Agent Costs: Practical Tips from the ‘Toward Efficient Agents’ Survey
PaperAgent
PaperAgent
Jan 26, 2026 · Artificial Intelligence

How Efficient Agents Are Redefining Memory, Tool Learning, and Planning in 2026

A joint survey by nine leading Chinese institutions outlines the efficiency crisis of modern AI agents and proposes three strategic directions—efficient memory, tool learning, and planning—detailing concrete mechanisms, representative models, and emerging trends for building high‑performing, low‑cost agents.

2026AI agentsagentic AI
0 likes · 11 min read
How Efficient Agents Are Redefining Memory, Tool Learning, and Planning in 2026
PaperAgent
PaperAgent
Jan 20, 2026 · Artificial Intelligence

How Intrinsic Self‑Critique Boosts LLM Planning Accuracy to 89% %​

Google DeepMind's new "Intrinsic Self‑Critique" method lets large language models iteratively self‑evaluate and rewrite their plans, raising Blocksworld planning accuracy from 49.8% to 89.3% and setting new records across multiple planning benchmarks.

AI researchLLMintrinsic evaluation
0 likes · 5 min read
How Intrinsic Self‑Critique Boosts LLM Planning Accuracy to 89% %​
AI Insight Log
AI Insight Log
Jan 18, 2026 · Artificial Intelligence

8 Actionable Practices from Cursor’s Week‑Long, Million‑Line Coding Experiment

Cursor ran a team of AI coding agents for a week to build a prototype browser, uncovering three major failure modes—drift, collaboration breakdown, and lack of quality signals—and proposing a planner/worker split plus eight concrete tactics that ordinary developers can adopt for long‑running autonomous coding tasks.

AI agentsAutomationCursor
0 likes · 10 min read
8 Actionable Practices from Cursor’s Week‑Long, Million‑Line Coding Experiment
Data Party THU
Data Party THU
Nov 24, 2025 · Artificial Intelligence

Model-Free vs Model-Based RL: Core Concepts and Large-Model Applications

This article explains the fundamental architecture of reinforcement learning, contrasting model‑free and model‑based approaches, detailing environment models, planning, data augmentation, expert iteration, and embedding planning, and then examines how large language models use policy‑based methods such as PPO, DPO, and GRPO for RL‑HF.

Model-BasedModel-freePolicy Optimization
0 likes · 13 min read
Model-Free vs Model-Based RL: Core Concepts and Large-Model Applications
Wu Shixiong's Large Model Academy
Wu Shixiong's Large Model Academy
Oct 24, 2025 · Artificial Intelligence

Can Large Language Models Truly Plan? Unpacking Agent Frameworks

This article explains why most LLM‑based agents only perform pseudo‑planning through prompts or hard‑coded loops, outlines when to rely on prompt‑driven versus program‑driven planning, compares popular frameworks such as ReAct, MRKL, BabyAGI and AutoGPT, and clarifies what true autonomous planning would require.

AgentArtificial IntelligenceAutoGPT
0 likes · 12 min read
Can Large Language Models Truly Plan? Unpacking Agent Frameworks
Tech Stroll Journey
Tech Stroll Journey
Oct 15, 2025 · Artificial Intelligence

From Tools to Autonomous Employees: Understanding AI Agents

This article explains AI Agents by contrasting them with traditional AI tools, detailing their official definition, core components—planning, tool use, memory, action—illustrating a travel‑planning example, outlining agent types, and highlighting their significance for AGI and real‑world applications.

AI AgentMemoryaction
0 likes · 7 min read
From Tools to Autonomous Employees: Understanding AI Agents
Old Zhao – Management Systems Only
Old Zhao – Management Systems Only
Sep 26, 2025 · Operations

Unlock Supply Chain Efficiency: Master Planning, Inventory, Logistics & Receivables

This article reveals why many businesses struggle with supply‑chain efficiency and explains how focusing on four critical links—planning, inventory management, logistics, and receivables—can transform operations, reduce costs, and improve customer satisfaction, offering practical examples and a downloadable template for immediate implementation.

InventoryOperations Managementcash flow
0 likes · 7 min read
Unlock Supply Chain Efficiency: Master Planning, Inventory, Logistics & Receivables
Old Zhao – Management Systems Only
Old Zhao – Management Systems Only
Sep 3, 2025 · Operations

How to Prevent Stock Overload and Material Shortages with Smart Procurement Planning

Learn why many companies suffer from excess inventory yet material shortages, and follow a step‑by‑step procurement planning framework—collecting demand, expanding BOM, calculating net needs, scheduling based on lead times, and continuously adjusting—to keep stock balanced, avoid production delays, and optimize supply chain efficiency.

inventory managementplanningprocurement
0 likes · 5 min read
How to Prevent Stock Overload and Material Shortages with Smart Procurement Planning
Youzan Coder
Youzan Coder
Aug 13, 2025 · Artificial Intelligence

Understanding AI Agents: Core Modules, Planning Strategies, and Evaluation

This article explains what an AI agent is, outlines its four core modules—perception, memory, planning, and action—describes the role of large language models, compares software development generations, discusses memory implementations, planning methods like ReAct and Plan‑and‑Solve, and covers evaluation, cost analysis, and differences between agents and workflows.

AIAgentLLM
0 likes · 15 min read
Understanding AI Agents: Core Modules, Planning Strategies, and Evaluation
JD Tech Talk
JD Tech Talk
Jul 28, 2025 · Artificial Intelligence

8 Key Features of JD.com’s Open‑Source Genie AI Agent

JD.com’s Genie open‑source AI agent introduces eight powerful capabilities—including plug‑in multi‑agent support, iterative planning, cross‑task context sharing, digital employee enhancements, deep model‑plus‑search, CodeTool for code lifecycle, refined system prompts, and a configurable MCP server—designed to boost intelligent workflow efficiency.

AI AgentArtificial Intelligencecode management
0 likes · 1 min read
8 Key Features of JD.com’s Open‑Source Genie AI Agent
Architect
Architect
Jul 6, 2025 · Artificial Intelligence

How Graphs Empower AI Agents: Taxonomy, Advances, and Future Opportunities

An extensive review introduces a taxonomy for integrating graph techniques with AI agents, detailing how graphs enhance core functions such as planning, execution, memory, and multi‑agent coordination, and discusses representative applications, challenges, and future research directions.

AI agentsgraph neural networksknowledge graphs
0 likes · 9 min read
How Graphs Empower AI Agents: Taxonomy, Advances, and Future Opportunities
Old Zhao – Management Systems Only
Old Zhao – Management Systems Only
Jun 3, 2025 · Operations

How to Turn Procurement into a Profit‑Driving Powerhouse

This article reveals why procurement is often underestimated, outlines the three essential capabilities—planning, collaboration, and cost control—and provides a step‑by‑step framework for creating effective purchase plans, aligning with production, sales, and R&D, and mastering total‑cost management to boost company profitability.

Cost Managementplanningprocurement
0 likes · 8 min read
How to Turn Procurement into a Profit‑Driving Powerhouse
Alibaba Cloud Developer
Alibaba Cloud Developer
May 26, 2025 · Artificial Intelligence

How Multi‑Agent Planning Boosts Copilot 3.0 with DeepSeek R1 GRPO Training

This article examines Copilot 3.0’s planning module, explains how DeepSeek R1’s GRPO reinforcement‑learning pipeline enables flexible multi‑agent orchestration, addresses the limitations of Copilot 2.0, and presents experimental results that show a 61% reduction in reasoning length and a 9% relative gain in accuracy.

AIReward Designmodel training
0 likes · 14 min read
How Multi‑Agent Planning Boosts Copilot 3.0 with DeepSeek R1 GRPO Training
DevOps
DevOps
May 13, 2025 · Artificial Intelligence

The Rise of AI Agents: Current Trends, Core Capabilities, and Future Outlook

This article surveys the rapid emergence of AI agents, outlining their projected 2025 breakthrough, market momentum, key frameworks such as Manus and MCP, the four core abilities of perception, planning, tool use, and memory, and the evolving landscape of multimodal and autonomous AI systems.

AI agentsArtificial IntelligenceMemory
0 likes · 11 min read
The Rise of AI Agents: Current Trends, Core Capabilities, and Future Outlook
Alibaba Cloud Developer
Alibaba Cloud Developer
Apr 25, 2025 · Artificial Intelligence

Unlocking AI Agents: Theory, Design Patterns, and Hands‑On Experiments

This article combines theoretical analysis and practical case studies to systematically explore the core components, design patterns, and future directions of AI agents, detailing the implementation of OpenManus, custom memory and planning modules, experimental evaluations, and insights for improving agent reliability and scalability.

AI AgentLLMMemory
0 likes · 31 min read
Unlocking AI Agents: Theory, Design Patterns, and Hands‑On Experiments
Data Thinking Notes
Data Thinking Notes
Apr 15, 2025 · Artificial Intelligence

Understanding AI Agents: From Reinforcement Learning to LLM-Powered Planning

Professor Li Hongyi’s lecture provides a comprehensive, step‑by‑step exploration of AI agents, covering their definitions, reinforcement‑learning roots, LLM integration, memory mechanisms, tool usage, planning strategies, benchmarks, and practical examples, offering a valuable resource for anyone studying modern artificial intelligence.

AI agentsLarge Language ModelsMemory
0 likes · 67 min read
Understanding AI Agents: From Reinforcement Learning to LLM-Powered Planning
Smart Era Software Development
Smart Era Software Development
Mar 29, 2025 · Artificial Intelligence

40+ Diagrams Uncover LLM Agents’ Core Components, Multi‑Agent Frameworks, and MCP Stack

This article breaks down the essential building blocks of LLM agents—including environment, sensors, effectors, short‑ and long‑term memory, tools, planning, and reasoning—while illustrating how Model Context Protocol (MCP), Toolformer, ReAct, Reflexion, and popular multi‑agent frameworks such as AutoGen, MetaGPT and CAMEL enable scalable, collaborative AI systems.

LLM AgentsMemoryModel Context Protocol
0 likes · 11 min read
40+ Diagrams Uncover LLM Agents’ Core Components, Multi‑Agent Frameworks, and MCP Stack
Fun with Large Models
Fun with Large Models
Mar 2, 2025 · Artificial Intelligence

Why 2025 Is the Year of AI Agents: Definitions, Types, and Real‑World Examples

The article explains what AI Agents are, how they differ from single large‑model systems, outlines four agent architectures—Reflection, Tool Use, Planning, and Multi‑Agent—and cites concrete examples from Grammarly, VS Code plugins, Image Describer X, ChatDev, as well as initiatives by Tencent and Google, highlighting the 2025 AI Agent boom.

ChatDevmulti-agentplanning
0 likes · 9 min read
Why 2025 Is the Year of AI Agents: Definitions, Types, and Real‑World Examples
ByteDance SYS Tech
ByteDance SYS Tech
Feb 18, 2025 · Operations

How Can Data Center Planning Cut Costs and Boost Efficiency?

This article explains how a mixed‑integer programming tool developed by ByteDance's SYS‑DCD team integrates cost, reliability, delivery speed, and environmental metrics to optimize data‑center planning, reduce power waste, and accelerate deployment across multiple regional scenarios.

Data CenterLinear ProgrammingOperations
0 likes · 15 min read
How Can Data Center Planning Cut Costs and Boost Efficiency?
Architecture Breakthrough
Architecture Breakthrough
Feb 18, 2025 · R&D Management

How to Turn OKRs into Actionable Plans: A Practical Guide

This article explains how to differentiate goals from plans, align objectives with strategy using SMART and OKR principles, apply Work Breakdown Structure (WBS) to break down goals into concrete actions, and use time‑management techniques like the Eisenhower matrix for effective execution.

OKRWBSgoal setting
0 likes · 5 min read
How to Turn OKRs into Actionable Plans: A Practical Guide
Infra Learning Club
Infra Learning Club
Feb 7, 2025 · Artificial Intelligence

Understanding LLM Agents: Architecture, Capabilities, and Key Challenges

This article explains what LLM agents are, their core components—brain, memory, planning, and tool use—illustrates how they handle complex queries through task decomposition, surveys notable frameworks, and discusses key challenges such as limited context, long‑term planning difficulties, output inconsistency, and prompt dependence.

AI architectureFrameworksLLM Agents
0 likes · 15 min read
Understanding LLM Agents: Architecture, Capabilities, and Key Challenges
DataFunTalk
DataFunTalk
Jan 27, 2025 · Artificial Intelligence

Improving AI Agent Planning and Reasoning: Challenges and Practical Solutions

The article examines current limitations of AI agents in planning and complex reasoning, critiques existing methods like COT/TOT and ReAct, and proposes practical strategies—including combined COT‑Reflection approaches, structured memory algorithms, and white‑box interaction designs—to enhance agent performance within the DataFun knowledge map framework.

AI AgentCoTData Modeling
0 likes · 3 min read
Improving AI Agent Planning and Reasoning: Challenges and Practical Solutions
DataFunSummit
DataFunSummit
Jan 1, 2025 · Artificial Intelligence

Challenges and Evaluation Strategies for LLM Agents in 2024

The article outlines the rapid progress of LLM agents in 2024 while highlighting key difficulties in planning capabilities, evaluation methods, dataset generation, and metric design, and suggests practical combinations and product‑level enhancements to improve efficiency, accuracy, and usability.

AIAgentLLM
0 likes · 3 min read
Challenges and Evaluation Strategies for LLM Agents in 2024
JD Retail Technology
JD Retail Technology
Nov 13, 2024 · R&D Management

Guidelines for New Project Managers: Initiation, Planning, Execution, and Monitoring

This article shares practical advice for novice project managers, covering the four process groups—initiation, planning, execution, and monitoring—through real‑world examples, stakeholder identification, risk handling, change control, and communication techniques to help them deliver value and grow their teams.

MonitoringProject Managementexecution
0 likes · 25 min read
Guidelines for New Project Managers: Initiation, Planning, Execution, and Monitoring
Architect's Alchemy Furnace
Architect's Alchemy Furnace
Jul 25, 2024 · Artificial Intelligence

Designing Autonomous LLM Agents: Architecture, Memory, Planning, and Learning Strategies

This article surveys the design of autonomous large‑language‑model agents, detailing their modular architecture—including profiling, memory, planning, and execution—while also reviewing common profiling methods, memory structures, planning techniques, action strategies, and various learning approaches such as exemplar, human‑in‑the‑loop, and environment‑feedback training.

AIAgent ArchitectureAutonomous Agents
0 likes · 36 min read
Designing Autonomous LLM Agents: Architecture, Memory, Planning, and Learning Strategies
DataFunSummit
DataFunSummit
Jul 24, 2024 · Artificial Intelligence

Overview of Large Language Model‑Based AI Agents: Architecture, Challenges, and Future Directions

This article reviews the emerging field of large language model‑based AI agents, outlining their overall architecture, key challenges such as role‑playing, memory, planning, and multi‑agent collaboration, and discusses future research directions and practical examples in user behavior simulation and software development.

AI agentsLLMMemory Mechanisms
0 likes · 11 min read
Overview of Large Language Model‑Based AI Agents: Architecture, Challenges, and Future Directions
DataFunSummit
DataFunSummit
Jul 17, 2024 · Artificial Intelligence

Overview of LLM‑Based Agents: Architecture, Key Challenges, and Future Directions

This article reviews the emerging field of large‑language‑model (LLM) based AI agents, outlining their overall architecture, core modules such as profiling, memory, planning and action, discussing current challenges, presenting concrete use‑cases, and highlighting promising research directions.

AI AgentAgent ArchitectureLLM
0 likes · 11 min read
Overview of LLM‑Based Agents: Architecture, Key Challenges, and Future Directions
Tencent Cloud Developer
Tencent Cloud Developer
May 28, 2024 · Artificial Intelligence

AI Agents: Concepts, Key Components, and Development Frameworks

AI agents extend large language models with planning, short‑term and long‑term memory, and tool‑use capabilities, enabling autonomous task decomposition, external API interaction, and persistent knowledge retrieval; frameworks such as MetaGPT, LangChain, and CrewAI simplify building agents like a researcher that gather information, browse web content, and generate reports, heralding broader AI‑enhanced productivity.

AI agentsFrameworksFunction Calling
0 likes · 20 min read
AI Agents: Concepts, Key Components, and Development Frameworks
NewBeeNLP
NewBeeNLP
Apr 15, 2024 · Artificial Intelligence

Unlocking LLM‑Based Agents: Architecture, Challenges, and Future Directions

This article systematically outlines the architecture of large‑language‑model (LLM) agents, examines their key technical challenges such as role‑playing, memory design, reasoning and multi‑agent collaboration, and explores emerging research directions and practical case studies.

AIFuture DirectionsLLM Agents
0 likes · 11 min read
Unlocking LLM‑Based Agents: Architecture, Challenges, and Future Directions
DataFunTalk
DataFunTalk
Apr 8, 2024 · Artificial Intelligence

LLM‑Based Agents: Architecture, Key Challenges, and Future Directions

This article surveys the emerging field of large‑language‑model (LLM) based agents, detailing their modular architecture—including profiling, memory, planning, and action components—while discussing critical challenges such as role‑playing, memory design, reasoning, multi‑agent collaboration, and outlining promising research directions and practical case studies.

AI AgentAgent ArchitectureLLM
0 likes · 11 min read
LLM‑Based Agents: Architecture, Key Challenges, and Future Directions
Smart Era Software Development
Smart Era Software Development
Apr 3, 2024 · Artificial Intelligence

Unveiling Andrew Ng’s Four AI Agent Design Patterns with Coze

The article explains Andrew Ng’s four AI agent design patterns—Reflection, Tool Use, Planning, and Multi‑agent Collaboration—illustrates each with concrete Coze workflows and real‑world examples such as automotive research briefs, milk‑tea industry reports, and high‑quality travel planning.

AI agentsCozeDesign Patterns
0 likes · 7 min read
Unveiling Andrew Ng’s Four AI Agent Design Patterns with Coze
DataFunSummit
DataFunSummit
Jan 27, 2024 · Artificial Intelligence

Enhancing Interactive Agents with Large Language Models: The SwiftSage Framework

This article reviews recent advances in using large language models for embodied interactive agents, introduces the dual‑modality SwiftSage architecture that combines a fast T5‑based small model with a powerful large model for planning and grounding, and evaluates its performance on benchmarks such as ScienceWorld.

AI2SwiftSagebenchmark
0 likes · 23 min read
Enhancing Interactive Agents with Large Language Models: The SwiftSage Framework
DevOps
DevOps
May 19, 2023 · Cloud Computing

Comprehensive Guide to Cloud Migration: Evaluation, Planning, Execution, and Cost Optimization

This article provides a detailed guide to cloud migration, covering evaluation and analysis, pilot projects, assessment strategies, planning and design with cloud services, verification and implementation steps, continuous measurement, and cost optimization through FinOps to ensure successful and secure migration.

Cloud ComputingFinOpscloud migration
0 likes · 10 min read
Comprehensive Guide to Cloud Migration: Evaluation, Planning, Execution, and Cost Optimization
Rare Earth Juejin Tech Community
Rare Earth Juejin Tech Community
May 5, 2023 · Artificial Intelligence

Limitations of Generative Pre‑trained Transformers: Hallucinations, Memory, Planning, and Architectural Proposals

The article critically examines GPT‑4 and similar transformer models, highlighting persistent hallucinations, outdated knowledge, insufficient domain coverage, lack of planning and memory, and proposes architectural extensions inspired by fast‑slow thinking and differentiable modules to overcome these fundamental constraints.

AI limitationsGPT-4Large Language Models
0 likes · 24 min read
Limitations of Generative Pre‑trained Transformers: Hallucinations, Memory, Planning, and Architectural Proposals
DataFunTalk
DataFunTalk
May 5, 2023 · Operations

Project Management Overview: Ten Key Areas and Practical Tips

This article outlines ten essential components of project management—including overall management, scope, schedule, cost, quality, resources, communication, risk, procurement, and integration—while providing practical tips, stakeholder analysis methods, and visual guides to help teams execute projects efficiently.

Project ManagementStakeholder Analysisplanning
0 likes · 5 min read
Project Management Overview: Ten Key Areas and Practical Tips
DataFunSummit
DataFunSummit
Jul 16, 2022 · Artificial Intelligence

Data‑to‑Text Generation: Controllability, Reliability, and Planning Strategies

This article introduces data‑to‑text generation, discusses its task background, common datasets, structured data representations, explicit and implicit planning methods, model architectures such as RNN, Transformer and VAE, and compares the advantages and challenges of each approach.

AIVariational Autoencoderdata-to-text
0 likes · 16 min read
Data‑to‑Text Generation: Controllability, Reliability, and Planning Strategies
Architects Research Society
Architects Research Society
Jun 12, 2022 · Operations

Planning and Optimizing Business Processes for Application Development

This guide explains how to plan an application by defining the problem it solves, understanding users and goals, then outlines systematic steps to analyze, improve, and automate business workflows, including practical questions, optimization methods, and a detailed expense‑report case study.

Business AnalysisOperationsProcess Optimization
0 likes · 5 min read
Planning and Optimizing Business Processes for Application Development
Efficient Ops
Efficient Ops
Jul 21, 2021 · Operations

Why Most Digital Transformations Fail: Planning Logic and DevOps Insights

This article examines why digital transformation projects often stumble, outlining the planning logic, stages of transformation, and the roles of DevOps, cloud computing, and technology in reshaping business models, operations, and talent development, while highlighting common failure causes and practical guidance for successful implementation.

DevOpsplanning
0 likes · 11 min read
Why Most Digital Transformations Fail: Planning Logic and DevOps Insights
ITFLY8 Architecture Home
ITFLY8 Architecture Home
Jun 24, 2021 · Operations

Mastering IT Architecture Governance: A Practical Planning Guide

This guide presents a comprehensive, step‑by‑step framework for IT architecture governance, covering objectives, governance models, roles, processes, standards, toolchains, metrics, and an implementation roadmap to help organizations align technology decisions with business goals.

IT ArchitectureIT Managementgovernance
0 likes · 2 min read
Mastering IT Architecture Governance: A Practical Planning Guide
Infra Learning Club
Infra Learning Club
May 26, 2021 · R&D Management

New Managers’ Top Challenges and How to Overcome Them

The article outlines the common problems faced by newly promoted managers—time shortage, delegation uncertainty, unmet goals, and unmotivated subordinates—then presents a five‑unit framework covering roles, planning, organization, leadership, and control to build effective management habits.

delegationleadershipmanagement
0 likes · 14 min read
New Managers’ Top Challenges and How to Overcome Them
Alibaba Cloud Developer
Alibaba Cloud Developer
May 17, 2021 · Product Management

Master the Art of Strategic Planning: 4 Methods & 2 Mindsets to Boost Your Team’s Success

This article explains why every team member should master planning, outlines its three core benefits—focus, insight, and future direction—introduces two thinking modes (inductive and deductive) and presents four practical planning methods to make your roadmap clear, actionable, and results‑driven.

methodologyplanningproduct management
0 likes · 15 min read
Master the Art of Strategic Planning: 4 Methods & 2 Mindsets to Boost Your Team’s Success
21CTO
21CTO
May 11, 2021 · Product Management

Mastering Strategic Planning: 4 Proven Methods to Boost Team Success

This article explains why planning is essential for every team, outlines two core thinking modes—inductive and deductive—and shares four practical planning methods (similar‑thinking, dimensional‑thinking, reduction‑thinking, and specialization) to help practitioners create focused, actionable, and measurable roadmaps.

R&Dmethodologyplanning
0 likes · 15 min read
Mastering Strategic Planning: 4 Proven Methods to Boost Team Success
IT Architects Alliance
IT Architects Alliance
Aug 6, 2020 · Operations

Eight Essential Steps for Successful Disaster Recovery Drills

This guide outlines eight practical steps—including defining scope, forming a planning team, setting clear objectives, designing realistic scenarios, creating evaluation checklists, assigning roles, conducting pre‑drill briefings, and performing post‑drill reviews—to help organizations execute effective, repeatable disaster recovery exercises that strengthen business continuity.

Disaster RecoveryOperationsbest practices
0 likes · 9 min read
Eight Essential Steps for Successful Disaster Recovery Drills
Programmer DD
Programmer DD
Jul 20, 2020 · R&D Management

10 Surprising Reasons Projects Fail (And How to Avoid Them)

Drawing from years of project experience, the author outlines ten common factors—ranging from unattainable goals to inexperienced managers—that consistently lead to project failure, and suggests that even a single misstep can doom a project.

R&Dfailure factorsplanning
0 likes · 3 min read
10 Surprising Reasons Projects Fail (And How to Avoid Them)
dbaplus Community
dbaplus Community
Mar 29, 2020 · Databases

Mastering GaussDB T: Planning Single‑Node, HA, and Distributed Architectures

This article outlines a comprehensive plan for deploying GaussDB T, covering the software architecture, component roles, and practical guidelines for single‑node, primary‑standby, and distributed setups, while highlighting common pitfalls and operational considerations for production environments.

Database ArchitectureGaussDBHigh Availability
0 likes · 13 min read
Mastering GaussDB T: Planning Single‑Node, HA, and Distributed Architectures
DataFunTalk
DataFunTalk
Feb 27, 2020 · Artificial Intelligence

Technical Challenges in Planning and Control for Autonomous Heavy Trucks

The article reviews the complex system model of autonomous heavy trucks, outlines traditional and modern planning and control methods—including rule‑based FSM, POMDP, learning‑based and optimization techniques—highlights safety, efficiency, fuel‑economy, and dynamic modeling challenges specific to heavy‑truck and trailer configurations, and shares practical attempts such as lane‑changing, merging, and trailer‑aware trajectory planning.

ControlPOMDPautonomous driving
0 likes · 13 min read
Technical Challenges in Planning and Control for Autonomous Heavy Trucks
DataFunTalk
DataFunTalk
Nov 8, 2019 · Artificial Intelligence

Balancing Safety and Comfort in Autonomous Driving: Planning and Control Optimization

This article explores how autonomous driving systems can simultaneously ensure safety and passenger comfort by optimizing planning and control modules, defining safety and comfort metrics, formulating constraints and cost functions, and employing models such as the bicycle model for lateral and longitudinal control.

ControlSafetyautonomous driving
0 likes · 12 min read
Balancing Safety and Comfort in Autonomous Driving: Planning and Control Optimization
21CTO
21CTO
Jan 26, 2019 · Operations

Why Planning Beats Summarizing: Turning Goals into Actionable Roadmaps

This article argues that proactive planning is more constructive than passive year‑end summaries, explains how setting clear goals, breaking them into tasks, estimating time, and using simple tools like spreadsheets and Gantt charts can improve personal and professional productivity, even amid rapid change.

Project Managementcareer developmentgoal setting
0 likes · 13 min read
Why Planning Beats Summarizing: Turning Goals into Actionable Roadmaps
Efficient Ops
Efficient Ops
Dec 12, 2018 · Operations

Essential Project Management Practices Every Manager Should Master

This article shares practical insights on project management, covering how to understand project goals, assess resources, communicate with stakeholders, handle risks and changes, create effective plans, and ensure successful delivery through disciplined documentation and verification.

Project Managementchange controlcommunication
0 likes · 23 min read
Essential Project Management Practices Every Manager Should Master