#LangChain MCP agent
294 related articles
·3 min
How to Choose an AI Agent Platform for Your Team: 5 Evaluation Dimensions and a Decision Framework
Choose the right AI Agent platform by evaluating model flexibility, observability, tool integration, security compliance, and total cost. A complete decision framework to help technical leaders avoid vendor lock-in.
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·3 min
How to Choose an AI Agent Platform for Your Team: 5 Evaluation Dimensions & Decision Framework
Choose an AI Agent platform by evaluating model flexibility, observability, tool integration, security compliance, and total cost. A complete decision framework to avoid vendor lock-in.
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·4 min
WebMCP in Practice: How MakeMyTrip Is Reshaping the Travel Booking Experience
India's largest OTA platform MakeMyTrip uses WebMCP to standardize AI Agent interactions with web apps, replacing fragile DOM scraping with natural language-driven test automation and simplified complex booking scenarios.
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·4 min
WebMCP in Practice: How MakeMyTrip Is Reshaping the Travel Booking Experience
India's largest OTA platform MakeMyTrip uses WebMCP to standardize AI Agent interaction with web apps, solving DOM scraping fragility, enabling natural language test automation, and simplifying complex international flight bookings.
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·3 min
From Frontend to Agent Development: A Complete 6-Week Systematic Learning Path
A 6-week systematic learning path for frontend engineers transitioning to AI Agent development, covering core architecture, ReAct, multi-agent collaboration, RAG integration, and deployment.
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·4 min
AI Agent Development in Practice: A Complete Path from Zero to Production
A systematic guide to AI Agent development covering core modules, framework selection, tool calling, data preparation, and production deployment to help developers build production-ready Agent applications.
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·5 min
The Complete Guide to AI Agent Development: A Four-Stage Progressive Roadmap
A systematic guide to AI Agent development across four stages: LLM fundamentals, ReAct paradigm, memory & tools, and multi-agent collaboration for developers.
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·4 min
GenAI Job Interview Gone Wrong: Interviewer Googles Gemini for Questions on the Spot — What's Broken in AI Hiring?
A fresh grad interviewing for a GenAI Trainer role faced prime number coding and activation function questions while the interviewer used Gemini to generate questions live — exposing AI hiring chaos.
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·2 min
DeepAgents in Practice: A Deep Dive into Sandbox Backends and Agent Skills
A deep dive into DeepAgents' sandbox backend and Agent Skills: why Agents need sandbox isolation to run code, how Skills enable modular capability reuse, and how the two combine to build reliable AI Agent systems.
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·2 min
DeepAgents in Practice: A Deep Dive into Sandbox Backends and Agent Skills
A deep dive into DeepAgents' sandbox backend and Agent Skills: why Agents need sandboxes to isolate code execution, how Skills enable modular capability reuse, and how the two work together to build reliable AI Agent systems.
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·4 min
Spring AI 2.0: Five Key Updates Explained — Marching Toward the Age of AI Agents
Spring AI 2.0 brings five core updates: mandatory upgrade to Spring Boot 4, Tools parsing moving up, a built-in Agentic mechanism, MCP switching to Streamable HTTP, and an on-demand tool Advisor.
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·4 min
Spring AI 2.0: Five Key Updates Explained — Stepping into the Agent Era
Spring AI 2.0 brings five core updates: mandatory upgrade to Spring Boot 4, lifted Tools parsing, built-in Agentic mechanism, MCP switch to Streamable HTTP, and an on-demand tool-loading Advisor.
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·4 min
Spring AI 2.0 Explained: Five Key Updates Ushering in the Age of AI Agents
Spring AI 2.0 brings five core updates: mandatory Spring Boot 4 upgrade, Tools parsing moved up, built-in Agentic mechanism, MCP switch to Streamable HTTP, and on-demand tool-loading Advisor.
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·3 min
Pinterest Medic: Engineering an AI Agent for Automated Spark Failure Diagnosis
How Pinterest engineers built Medic for Apache Spark — a multi-agent auto-diagnosis tool — covering the evolution from a single ReAct agent, observability, log denoising, and end-to-end testing.
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·8 min
A Complete Guide to Agent Testing: Five Core Dimensions and Automation in Practice
In-depth analysis of the five core dimensions of AI Agent testing: command safety, tool-calling accuracy, task planning, output consistency, and error self-repair. Master automated testing and the transition path for test engineers.
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·8 min
The Complete Guide to Agent Testing: Five Core Dimensions and Automation in Practice
An in-depth analysis of the five core dimensions of AI Agent testing: command safety, tool-calling accuracy, task planning, output consistency, and error self-repair. Master automated testing methods and the transition path for test engineers.
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·4 min
Spring AI 1.0 Practical Guide: A Java Developer's Fast Track to Building AI Applications
Spring AI 1.0 is here — Java developers can now build AI apps without switching to Python. This guide covers LLM integration, RAG, intelligent customer service, and Agent patterns for enterprise deployment.
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·8 min
LangChain Crash Course: A Complete Roadmap from Zero to Agent Development
A systematic roadmap from LangChain and LangGraph to multi-agent development, covering RAG, Tool Calling, MCP, and more, helping developers break into AI app development.
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·2 min
Getting Started with LangGraph: Graph Orchestration and State Machines for AI Agents
A comprehensive guide to LangGraph's core concepts: Graph API vs Functional API, three-layer architecture, and workflow visualization methods for building AI Agents.
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·6 min
30-Minute Hands-On: Building a Working AI Agent from Scratch
Skip the dry theory and get hands-on! This article demonstrates step by step how to build a working AI Agent from scratch in 30 minutes using AI coding tools—covering the agent skeleton, tool system, memory mechanism, Flask web UI, and DeepSeek API integration.
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