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production AI

30 articles · Page 1 of 1
Lao Guo's Learning Space
Lao Guo's Learning Space
Nov 5, 2026 · Artificial Intelligence

Vertical Agent Deployment: 4-Layer Architecture, 7-Step Checklist & Local 70B Cost Analysis

This article presents a practical framework for building vertical AI agents, including a four-layer architecture (access, orchestration, model, tools, memory), a runnable local contract-review agent using 27B models, a seven-step deployment checklist, cost comparison between local and cloud inference, and three critical pitfalls to avoid.

70B modelsAI agent architectureagent orchestration
0 likes · 8 min read
Vertical Agent Deployment: 4-Layer Architecture, 7-Step Checklist & Local 70B Cost Analysis
KooFE Frontend Team
KooFE Frontend Team
Oct 28, 2026 · Artificial Intelligence

10 Production Prompt Design Patterns from Real-World AI Systems

This article distills 10 prompt design patterns from analyzing seven production system prompts (Claude, ChatGPT, Perplexity, Cursor, v0), covering vertical vs. general product constraints, tool-driven prompt length, iterative bug-fixing, brand identity, layered architecture, constraint-over-guidance, honesty about limitations, removing AI style, risk-graded decisions, and UX details—culminating in the shift from prompt engineering to context engineering.

AI agentsAI product designLLM prompting
0 likes · 14 min read
10 Production Prompt Design Patterns from Real-World AI Systems
Su San Talks Tech
Su San Talks Tech
Oct 20, 2026 · Artificial Intelligence

5 RAG Optimization Techniques That Actually Work in Production

The article details five practical engineering techniques to boost RAG accuracy in production: semantic chunking with metadata injection, hybrid vector/keyword search, cross-encoder reranking, query rewriting for conversational context, and dynamic evaluation to prevent hallucinations.

Dynamic EvaluationHybrid SearchQuery Rewriting
0 likes · 10 min read
5 RAG Optimization Techniques That Actually Work in Production
KooFE Frontend Team
KooFE Frontend Team
Oct 20, 2026 · Artificial Intelligence

How OpenAI Structures ChatGPT's 17K System Prompt: Layered Design & Prompt Patterns

This article dissects ChatGPT GPT-5.2 Thinking's 17,000-character system prompt, revealing its layered architecture—base behaviors, functional modules, and dynamic developer messages—and analyzes specific rules for trustworthiness, factuality, persona, tool use, web search citations, and writing style that make it a production-grade prompt engineering reference.

ChatGPTGPT-5.2LLM
0 likes · 22 min read
How OpenAI Structures ChatGPT's 17K System Prompt: Layered Design & Prompt Patterns
DataFunTalk
DataFunTalk
Oct 20, 2026 · Artificial Intelligence

Xiaomi Data Agent: Why Engineering Harness Beats Model Alone for Production Analytics

Xiaomi details its production Data Agent architecture — multi-level semantic fallback, multi-path SQL generation with voting, data lineage traceability, automated evaluation loops, and anti-decay monitoring — achieving 90%+ accuracy across finance, HR, and billing scenarios with 10x efficiency gains.

BIRD BenchmarkEvaluation HarnessSemantic Layer
0 likes · 19 min read
Xiaomi Data Agent: Why Engineering Harness Beats Model Alone for Production Analytics
KooFE Frontend Team
KooFE Frontend Team
Oct 19, 2026 · Artificial Intelligence

Deconstructing Claude's System Prompt: Anthropic's Blueprint for Honest, Restrained AI

This article dissects Anthropic's official system prompt for the Claude chat interface, analyzing its seven XML-wrapped modules—identity, refusal handling, tone, user wellbeing, knowledge cutoff, evenhandedness, and long-conversation reminders—to reveal how honesty, restraint, and proactive safety shape production-grade AI behavior.

AI safetyAnthropicClaude
0 likes · 33 min read
Deconstructing Claude's System Prompt: Anthropic's Blueprint for Honest, Restrained AI
21CTO
21CTO
Oct 18, 2026 · Industry Insights

5 Best AI Gateways Compared: Benchmarks, Routing & Enterprise Features

This article compares five leading AI gateways—Bifrost, Cloudflare AI Gateway, Vercel AI Gateway, LiteLLM, and OpenRouter—evaluating their performance benchmarks, model routing capabilities, deployment options, observability, cost control, and suitability for different production environments.

AI GatewayLLM routingObservability
0 likes · 24 min read
5 Best AI Gateways Compared: Benchmarks, Routing & Enterprise Features
Java Tech Enthusiast
Java Tech Enthusiast
Oct 12, 2026 · Artificial Intelligence

Harness Engineering: The Third Layer of AI Engineering for Production Systems

This article explains the progression from Prompt Engineering to Context Engineering to Harness Engineering, using analogies and concrete examples to show how each layer addresses a different challenge: task clarity, information access, and controllable execution in production workflows, with practical frameworks for each stage.

AI EngineeringAgent SystemsLLM applications
0 likes · 24 min read
Harness Engineering: The Third Layer of AI Engineering for Production Systems
DataFunTalk
DataFunTalk
Oct 7, 2026 · Artificial Intelligence

360's Data Agent: Dual-Path Semantic Layer & Confidence-Driven Evaluation

360's enterprise Data Agent bridges the NL2SQL production gap with a dual-path routing system—AST compilation for mature semantic layers and RAG fallback for sparse metadata—guided by confidence scores that gate automated reporting versus human review, while evolving skills into an ontology-driven knowledge layer and validating via a three-level evaluation flywheel.

Dual-Path RoutingNL2SQLOntology Metadata
0 likes · 6 min read
360's Data Agent: Dual-Path Semantic Layer & Confidence-Driven Evaluation
DataFunSummit
DataFunSummit
Oct 6, 2026 · Artificial Intelligence

How 6 Chinese Tech Giants Engineer Context for Production AI Agents

Six leading Chinese companies — JD.com, Hangzhou Qunhe, Guanyuan Data, Datastrato, Li Auto, and AWS — share their production-grade context engineering practices for AI agents, covering layered memory, semantic layers, evidence chains, and action loops at DACon 2026.

AI agentsDACon 2026Semantic Layer
0 likes · 22 min read
How 6 Chinese Tech Giants Engineer Context for Production AI Agents
Huolala Safety Emergency Response Center
Huolala Safety Emergency Response Center
Sep 30, 2026 · Artificial Intelligence

Harness Engineering for Stable AI Agents: Context, Scheduling & Production Lessons

This article details Harness Engineering practices for building stable AI Agents, covering when to apply Harness, constraining action spaces with skills/MCP, managing context via data precomputation and cost control, implementing instruction sets and event-driven scheduling, persisting memory with version control, and demonstrating measurable improvements in tool-call efficiency and token costs through cache optimization.

AI agentsEvent-Driven Schedulingcontext management
0 likes · 17 min read
Harness Engineering for Stable AI Agents: Context, Scheduling & Production Lessons
macrozheng
macrozheng
Sep 21, 2026 · Artificial Intelligence

Why AI Knowledge Bases Fail: The 70% Ceiling and How to Break It

A case study of a failed AI knowledge base project reveals the gap between impressive demos and production systems, detailing the required technical paradigms—RAG with multi-granularity indexing, structured data querying, knowledge graphs, ontologies, rule engines, agentic workflows, and rigorous evaluation loops—to build reliable, traceable enterprise AI.

AI knowledge baseAgentic WorkflowRAG
0 likes · 18 min read
Why AI Knowledge Bases Fail: The 70% Ceiling and How to Break It
DataFunSummit
DataFunSummit
Sep 20, 2026 · Artificial Intelligence

Alibaba's OpenCodeReview: Why Production Agents Are Reclaiming Control from LLMs

Alibaba's OpenCodeReview adopts a hybrid deterministic-engineering-plus-agent architecture for code review, cutting token usage by ~9x versus generic coding agents by constraining agent autonomy with hard-coded filters, token guards, and line-resolution modules, trading lower recall for higher precision and reliability.

AACR-BenchAI code reviewAgent Architecture
0 likes · 16 min read
Alibaba's OpenCodeReview: Why Production Agents Are Reclaiming Control from LLMs
AI Step-by-Step
AI Step-by-Step
Sep 14, 2026 · Artificial Intelligence

Claude Agent SDK: Architecture and Capabilities for Production-Grade AI Agents

This article provides a comprehensive technical analysis of the Claude Agent SDK, detailing its seven core capabilities — loop control, context management, tool systems with MCP, permission governance, hooks, subagents, and skills/plugins — and explains how they layer together to form a production-ready agent architecture with observable execution, safety guardrails, and domain knowledge integration.

AI agentsAgent ArchitectureAnthropic
0 likes · 15 min read
Claude Agent SDK: Architecture and Capabilities for Production-Grade AI Agents
dbaplus Community
dbaplus Community
Sep 9, 2026 · Artificial Intelligence

Production-Grade Enterprise Agents: Unifying Harness, Skills & Virtual File Systems

This article details a production-grade architecture for enterprise AI agents, combining a unified harness for execution control, federated skills for domain expertise, and a virtual file system for long-task context management, drawing on Stripe's Kai platform and Deep Agents framework to address governance, security, and scalability challenges.

AI GovernanceAgent EvaluationAgent Harness
0 likes · 37 min read
Production-Grade Enterprise Agents: Unifying Harness, Skills & Virtual File Systems
PaperAgent
PaperAgent
Sep 6, 2026 · Artificial Intelligence

Anthropic's Killer Multi-Agent Blueprint: One Loop, Skills, Harness & Snapshot Eval

Anthropic's production e-commerce and math-formalization agents share a unified architecture: a single-model loop with modular skills, tool calls to existing systems, code-enforced harness rules, and snapshot-based evaluation, enabling scalable, verifiable multi-agent systems.

Agent ArchitectureAnthropicLLM Engineering
0 likes · 17 min read
Anthropic's Killer Multi-Agent Blueprint: One Loop, Skills, Harness & Snapshot Eval
DataFunSummit
DataFunSummit
Jul 28, 2026 · Artificial Intelligence

Why Enterprises Still Hesitate to Deploy Production‑Ready AI Agents Despite Palantir’s New Capabilities

Palantir’s Agent Stack introduces Orchestrator, observability, and Ontology layers to make AI agents durable, interruptible, and governed, but enterprises remain reluctant because trust, state management, permission control, and continuous evaluation are required before agents can operate on real business processes.

AI agentsObservabilityOrchestrator
0 likes · 14 min read
Why Enterprises Still Hesitate to Deploy Production‑Ready AI Agents Despite Palantir’s New Capabilities
Architect's Ambition
Architect's Ambition
May 8, 2026 · Artificial Intelligence

A 12,000‑Word Guide to Agent Harness: Designing and Implementing Production‑Ready AI Agents

The article presents a comprehensive 7‑layer Agent Harness architecture that transforms experimental LLM‑based agents into stable, cost‑effective, secure, and observable production‑grade autonomous workers, illustrated with real‑world case studies, performance metrics, and concrete implementation details.

AI agentsAgent ArchitectureObservability
0 likes · 33 min read
A 12,000‑Word Guide to Agent Harness: Designing and Implementing Production‑Ready AI Agents
AI Waka
AI Waka
Apr 26, 2026 · Artificial Intelligence

Why Runtime, Not Model, Determines AI Agent Success in Production

The article argues that despite powerful models like Claude, the primary cause of AI Agent failures in production is the surrounding runtime infrastructure—such as session management, compliance, and orchestration—rather than the model itself, and examines the split between teams building custom runtimes versus those leveraging platform services.

AI agentsClaudeModel vs Runtime
0 likes · 6 min read
Why Runtime, Not Model, Determines AI Agent Success in Production
AI Architecture Hub
AI Architecture Hub
Apr 21, 2026 · Artificial Intelligence

Why Harness Architecture Turns LLMs into Production‑Ready Agents

This article explains why the Harness architecture—linking prompts, context, and runtime support—is the decisive factor that turns large language models from demo prototypes into reliable production agents, detailing its core capabilities, structural components, execution loop, design trade‑offs, and industry trends.

AI operationsAgent HarnessLLM Engineering
0 likes · 35 min read
Why Harness Architecture Turns LLMs into Production‑Ready Agents
AI Large-Model Wave and Transformation Guide
AI Large-Model Wave and Transformation Guide
Apr 7, 2026 · Artificial Intelligence

Why Harness Engineering Is the New AI Competitive Edge in 2026

The article argues that as large‑model capabilities converge, the decisive factor in 2026 AI competition shifts from raw model power to the ability to engineer a full‑stack Harness system that multiplies performance tenfold through standardized adapters, dynamic prompt registries, multi‑agent orchestration, context compression, and observability.

AI EngineeringHarnessObservability
0 likes · 14 min read
Why Harness Engineering Is the New AI Competitive Edge in 2026
AI Architecture Hub
AI Architecture Hub
Apr 1, 2026 · Artificial Intelligence

How Harness Turns AI Agents from Demo to Production‑Ready Systems

Enterprise AI teams often see impressive results with single‑turn prompts, but when tasks become long‑running and complex, models lose context, produce faulty code, and require heavy manual intervention; the Harness framework provides a full‑lifecycle control system that stabilizes agents, manages knowledge, and ensures reliable production deployment.

AI AgentAI operationsHarness framework
0 likes · 12 min read
How Harness Turns AI Agents from Demo to Production‑Ready Systems
Aikesheng Open Source Community
Aikesheng Open Source Community
Mar 9, 2026 · Artificial Intelligence

Why Traditional AI Benchmarks Fail and How SCALE Redefines SQL LLM Evaluation

The article examines the shortcomings of conventional AI evaluation methods, introduces the concept of an "unknown" risk in production settings, and presents SCALE—a continuously updated, high‑fidelity benchmark that stresses large‑model SQL capabilities with real‑world incident data and mixed objective‑subjective scoring.

AI evaluationLarge Language ModelsModel Selection
0 likes · 11 min read
Why Traditional AI Benchmarks Fail and How SCALE Redefines SQL LLM Evaluation
Shi's AI Notebook
Shi's AI Notebook
Mar 2, 2026 · Artificial Intelligence

Why OpenClaw’s Hype Marks a Shift to Agent Runtime Paradigms

OpenClaw is not just another AI chatbot; it redefines the focus from answering questions to executing sustainable, governable workflows across multiple channels, prompting a broader industry move from model‑centric to execution‑centric agent systems.

AI agentsAgent RuntimeOpenClaw
0 likes · 13 min read
Why OpenClaw’s Hype Marks a Shift to Agent Runtime Paradigms