#Google multi-agent platform
387 related articles
·7 min
The Complete Guide to LLM Application Engineer Interviews: Six High-Frequency Agent Topics
A focused guide to core LLM application engineer interview topics, covering agent architecture, Multi-Agent, Langfuse evaluation, security, and RAG optimization.
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·6 min
The AI Builder's Handbook: A Complete Guide to LLM Principles, RAG Architecture, and Agent Design
From the autocomplete nature of LLMs, tokens, and context windows to RAG vector databases, the MCP protocol, and AI agent loop design — this article uses vivid analogies to unpack the reality of AI engineering.
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·7 min
From Chat to Agent Platform: A Practical Guide to Engineering AI Applications
A deep dive into engineering AI applications: from a simple chat page to a multi-layer Agent platform, covering RAG knowledge bases, Workflow scheduling, multi-model management, and run tracing.
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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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·3 min
LangChain v1.3 Hands-On Tutorial: Deep Dive into Agents, RAG, and Local Model Deployment
Deep dive into LangChain v1.3: compare LangChain, LangGraph, and DeepAgent paradigms, explore RAG pipelines, multi-agent systems, and local LLM deployment for enterprise AI apps.
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·3 min
AI Agent Development Landscape: From Architecture Design to Production Deployment
A systematic overview of the AI Agent tech stack: RAG retrieval, Agent planning, MCP protocol, AI Gateway, and observability — helping developers build production-grade AI systems.
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·6 min
GPT-5.6 Hands-On Deep Dive: Three-Tier Models + Ultra Multi-Agent, Codex Fully Evolved
OpenAI launches GPT-5.6 with three tiered models—Sol, Terra, and Luna—Ultra multi-agent parallel collaboration, Codex integrated into ChatGPT desktop, and an upgraded Computer Use.
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·5 min
The Complete Claude Code Guide: Installation, Core Features, and Advanced Workflows
An in-depth guide to Anthropic's Claude Code agentic coding tool, covering installation, pricing plans, model selection, token management, CLAUDE.md global memory, MCP integration, Subagents, and more.
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·4 min
Zero-Code AI Voice Agents with Claude Code: 5 Real-World Business Scenarios Unpacked
Build a production AI voice agent with Claude Code + Telnyx single-stack — no code needed, live phone number in 5 minutes. Covers 5 business scenarios including appointment booking, lead qualification, and support triage.
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·4 min
Cross-Platform AI Agent Setup Guide: Breaking Free from Vendor Lock-In for Good
Model performance gaps are closing. Real competitive advantage lies in portable AI agent architecture. Learn how to build a workspace that works across Claude Code, Codex, and beyond — no vendor lock-in.
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·3 min
The Complete Guide to AI-Native Architecture: RAG, Agents, and MCP
A comprehensive guide to AI-native application architecture: LLM inference, RAG retrieval (vector DB/knowledge graph/BM25), Agents, MCP tool calling, AI gateways, and observability — end-to-end.
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·2 min
The Complete Guide to Agent Skills: Andrew Ng × Anthropic Joint Course Breakdown
Andrew Ng's DeepLearning.ai teams up with Anthropic to teach Agent Skills: file structure, progressive disclosure, MCP integration, and the full path from Claude.ai to the Agent SDK.
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·4 min
Pi Agent Deep Dive: Four Core Advantages of a Minimalist Open-Source Framework
Pi is a minimalist open-source Agent framework with just 4 default tools and under 1,000 tokens in its system prompt, with 70K GitHub stars. Deep dive into its 4 core advantages vs. Claude Code and Codex.
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·2 min
OpenSpiel 2.0 Released: Reinforcement Learning Framework Fully Embraces Large Language Models
OpenSpiel 2.0 by Google DeepMind adds LLM fine-tuning examples, MCP tool server, JSON trajectories, AlphaZero on JAX, 19 new games, and Windows support.
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·5 min
AI Coding Getting Worse With Every Edit? Skills v1.1 Reveals the Real Cause and Solution
AI code getting messier with edits? The root cause isn't weak model capability but a lack of context and process. A deep dive into Matt Pocock's Skills v1.1: grilling, vertical-slice tickets, TDD, and WebFinder.
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·3 min
OpenCode In-Depth Review: Can This Open-Source Coding Agent Replace Claude Code?
OpenCode is an open-source terminal coding agent with 180K+ GitHub stars, supporting hundreds of models and a dual-layer agent architecture — completely free. See how it compares to Claude Code.
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·3 min
Coze Deep Dive: Multi-Agent Collaboration Platform Features & Usage Guide
A comprehensive guide to Coze by ByteDance: multi-agent collaboration, local tool integration, cross-platform sync, and credit system. Compare with Dify to get started fast.
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·2 min
AI Agent Development Roadmap: A Four-Stage Guide from Zero to Production
A four-stage AI Agent development roadmap: from core theory and ReAct paradigm to multi-agent collaboration and production deployment. Covers DeepSeek, Coze, Dify, and more.
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·2 min
LangGraph Multi-Agent Architecture in Practice: From Core Components to Production Deployment
A deep dive into LangGraph multi-agent architecture — covering hierarchical, network, and pipeline patterns with three hands-on projects using LangGraph 0.3.
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BAML: A Type-Safe Programming Language…
·2 min
BAML: A Type-Safe Programming Language Designed for AI Agents
BAML is a domain-specific language for AI Agent development that uses a type system to solve unreliable LLM structured output and unmaintainable prompts.
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