#Plan-and-Execute Agent
103 related articles
·4 min
AI Agent Development Deep Dive: Opportunities, Real Entry Barriers, and Learning Paths
A deep dive into AI Agent development: real architecture, entry barriers, and learning paths. From ReAct to multi-agent systems and LangChain — cut through the hype.
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·5 min
Embedded Linux vs. AI Agent Development: A Comprehensive Comparison of Salary, Job Availability, and Career Stability
Embedded Linux or AI Agent development? This in-depth comparison covers salary, job availability, and career stability to help developers pick the right path.
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·6 min
LangChain 1.3 in Practice: A Complete Guide to DeepAgent Architecture and the Harness Philosophy
An in-depth look at LangChain 1.3's core modules and DeepAgent architecture—covering the Harness philosophy, LangGraph internals, HITL, memory management, and guardrails to master production-grade AI Agent development.
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·5 min
The Halo Open-Source Project: Building Tamper-Evident Runtime Evidence for AI Agents
Halo is an open-source tool providing tamper-evident runtime evidence for AI agents, recording every action via hash chains and cryptography to solve auditing, compliance, and accountability challenges.
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·7 min
Context Warp Drive: A Deep Dive into Deterministic Context Folding for AI Agents
An in-depth look at 'Deterministic Context Folding' from Context Warp Drive: solving AI agent context window management with reproducible, cacheable, debuggable context compression for production-grade agents.
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·5 min
A Programmer's Guide to Large Language Models: The Complete Path from Prompts to Agents
A tailored large-model learning path for ordinary programmers: from prompt engineering, API calls, and LangChain, to RAG, Agents, fine-tuning, and enterprise deployment—six steps to build AI application skills fast.
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·2 min
Andrew Ng on AI Agent Planning Pattern: How LLMs Autonomously Create Execution Plans
Andrew Ng explains the AI Agent Planning Design Pattern: how LLMs autonomously create step-by-step execution plans using tools, with real-world examples and current limitations.
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·7 min
Spring AI 2.0 in Practice: A Beginner's Guide for Java Developers Building AI Agents
A complete Spring AI 2.0 guide for Java developers covering unified API abstraction, RAG, tool calling, MCP protocol, and enterprise projects to build AI Agents.
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·4 min
Enterprise AI Agent Development Guide: Multi-Task Recognition and Dynamic Tool Calling in Practice
A deep dive into AI Agent architecture and enterprise deployment. From LangChain and ReAct design to dynamic tool calling and multi-task recognition — build autonomous enterprise AI assistants.
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·7 min
Claude Sonnet 5 Released: The Most Powerful Agent-Capable Mid-Tier AI Model
Anthropic releases Claude Sonnet 5, its most agentic mid-tier model with planning, browser/terminal tool use, and autonomous execution—bringing flagship Agent capabilities at significantly lower cost.
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·7 min
Claude Sonnet 5 Released: The Most Capable Agent Model at Mid-Tier Pricing
Anthropic releases Claude Sonnet 5, its most agentic Sonnet model yet. With planning, browser/terminal tool use, and autonomous execution, it brings flagship Agent capabilities to mid-tier pricing.
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·3 min
Hermes Agent Setup in Practice: Run a Complete AI Agent Workflow in 3 Minutes
Step-by-step guide to installing Hermes Agent: no sudo required, single-command deployment, supports Ollama, Anthropic, OpenRouter and more. Includes verification steps, key commands, and beginner tips.
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·7 min
Agent Loop Explained: The Core Runtime Mechanism and Think-Act Cycle of AI Agents
Deep dive into Agent Loop mechanics: the think-act cycle, how agents differ from LLMs, termination conditions, and design principles for building autonomous AI Agent systems.
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·7 min
AI Agent Practical Guide: The Complete Path from Building to Production Deployment
A deep dive into AI Agent architecture and engineering practices, covering tool design, ReAct execution patterns, Vercel deployment, and production considerations to bridge the prototype-to-production gap.
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·4 min
Deep Dive into Alibaba's AgentScope 2.0: Six Core Upgrades and Agent Design Patterns Explained
Deep analysis of Alibaba's AgentScope 2.0 multi-agent framework: six core upgrades including event systems, security interception, HITL, and workspace systems, plus ReAct vs Plan-and-Execute agent design patterns.
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·3 min
6-Week AI Agent Development Learning Roadmap: A Complete Guide from Zero to Project Deployment
A systematic 6-week AI Agent development roadmap covering core architecture, ReAct paradigm, multi-agent collaboration, RAG integration, and deployment for beginners to build production-ready agents.
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·3 min
Java AI Framework Comparison: A Selection Guide for Spring AI, LangChain4J, and More
In-depth comparison of four Java AI frameworks — Spring AI, LangChain4J, DJL, and JBot AI — covering features, use cases, and ecosystem compatibility to guide your selection.
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·3 min
Getting Started with AI Agent Development: Core Concepts, Architecture, and Practical Essentials
A comprehensive guide to AI Agent development covering core concepts, the Perception-Brain-Action architecture, key differences from chatbots, four essential components, and mainstream framework selection.
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·4 min
AI Large Language Model Career Transition Roadmap: A Three-Phase Learning Guide from Zero to Enterprise-Ready
A systematic three-phase AI LLM career transition roadmap: from Transformer fundamentals to RAG, Agent & LangChain development, to LoRA fine-tuning. Build enterprise-ready skills in two months.
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·3 min
Four Core Modules of AI Agent Development: An Architecture Guide from Failure to Stable Deployment
Deep dive into four core AI Agent modules: system prompts, tool calling, RAG memory, and ReAct workflow orchestration. Solve hallucinations, loops, and build reliable agents.
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