production engineering
30 articles about production engineering in AI news
ENS Paris-Saclay Publishes Full-Stack LLM Course: 7 Sessions Cover torchtitan, TorchFT, vLLM, and Agentic AI
Edouard Oyallon released a comprehensive open-access graduate course on training and deploying large-scale models. It bridges theory and production engineering using Meta's torchtitan and torchft, GitHub-hosted labs, and covers the full stack from distributed training to agentic AI.
Harness Engineering for AI Agents: Building Production-Ready Systems That Don’t Break
A technical guide on 'Harness Engineering'—a systematic approach to building reliable, production-ready AI agents that move beyond impressive demos. This addresses the critical industry gap where most agent pilots fail to reach deployment.
Context Engineering: The Real Challenge for Production AI Systems
The article argues that while prompt engineering gets attention, building reliable AI systems requires focusing on context engineering—designing the information pipeline that determines what data reaches the model. This shift is critical for moving from demos to production.
Production RAG: From Anti-Patterns to Platform Engineering
The article details common RAG anti-patterns like vector-only retrieval and hardcoded prompts, then presents a five-pillar framework for production-grade systems, emphasizing governance, hardened microservices, intelligent retrieval, and continuous evaluation.
VMLOps Launches Free 230+ Lesson AI Engineering Course with Production-Ready Tool Portfolio
VMLOps has launched a free, hands-on AI engineering course spanning 20 phases and 230+ lessons. It uniquely culminates in students building a portfolio of usable tools, agents, and MCP servers, not just theoretical knowledge.
The 100th Tool Call Problem: Why Most CI Agents Fail in Production
The article identifies a common failure mode for CI agents in production: they can get stuck in infinite loops or make excessive tool calls. It proposes implementing stop conditions—step/time/tool budgets and no-progress termination—as a solution. This is a critical engineering insight for deploying reliable AI agents.
The Future of Production ML Is an 'Ugly Hybrid' of Deep Learning, Classic ML, and Rules
A technical article argues that the most effective production machine learning systems are not pure deep learning or classic ML, but pragmatic hybrids combining embeddings, boosted trees, rules, and human review. This reflects a maturing, engineering-first approach to deploying AI.
Garry Tan's gstack: The 13-Skill Setup That Turns Claude Code Into a Virtual Engineering Team
Install Garry Tan's open-source gstack to get 13 specialized Claude Code skills (/plan-ceo-review, /review, /qa) that act as a full engineering team, shipping production code faster.
AI Engineering Hub Reaches 30K GitHub Stars, Democratizing Practical AI Development
The open-source AI Engineering Hub has reached 30,000 GitHub stars one year after launch, featuring 90+ hands-on projects covering RAG, AI agents, fine-tuning, and LLMOps. This milestone highlights growing demand for practical, production-ready AI implementation resources.
How I Built a Production RAG Pipeline for Fintech at 1M+ Daily Transactions
A technical case study from a fintech ML engineer outlines the end-to-end design of a Retrieval-Augmented Generation pipeline built for production at extreme scale, processing over a million daily transactions. It provides a rare, real-world blueprint for building reliable, high-volume AI systems.
Greater Bay Tech Rolls First A-Sample Solid-State Battery Cells Off Production Line
Greater Bay Technology has produced its first A-sample all-solid-state battery cells, achieving 260-500 Wh/kg energy density and passing needle penetration tests without fire. The company aims for GWh-level mass production and in-vehicle use by 2026.
Shopify Engineering Teases 'Autoresearch' Beyond Model Training in 2026 Preview
Shopify Engineering has previewed a 2026 perspective suggesting 'autoresearch'—automated research processes—will have applications extending beyond just training AI models. This signals a broader operational automation strategy for the e-commerce giant.
Laravel ClickHouse Package Open-Sourced After 4 Years in Production
Developer Albert Cht has open-sourced a Laravel package for ClickHouse after 4 years of proven use in production. This provides a reliable, high-performance data layer for applications handling AI-generated or telemetry data.
Production Claude Agents: 6 CCA-Ready Patterns for Enforcing Business Rules
An article from Towards AI details six production-ready patterns for creating Claude AI agents that adhere to business rules. This addresses the core enterprise challenge of making LLMs predictable and compliant, moving beyond prototypes to reliable systems.
Building a Production-Grade Fraud Detection Pipeline Inside Snowflake —
The source is a technical article outlining how to construct a full fraud detection pipeline within the Snowflake Data Cloud. It leverages Snowflake's native tools—Snowflake ML, the Model Registry, and ML Observability—alongside XGBoost to go from raw transaction data to a production-scoring system with monitoring.
Seven Voice AI Architectures That Actually Work in Production
An engineer shares seven voice agent architectures that have survived production, detailing their components, latency improvements, and failure modes. This is a practical guide for building real-time, interruptible, and scalable voice AI.
Why Most RAG Systems Fail in Production: A Critical Look at Common Pitfalls
An expert article diagnoses the primary reasons RAG systems fail in production, focusing on poor retrieval, lack of proper evaluation, and architectural oversights. This is a crucial reality check for teams deploying AI assistants.
Managed Agents Emerge as Fastest Path from Prototype to Production
Developer Alex Albert highlights that managed agent services now offer the fastest path from weekend project to production-scale deployment, eliminating self-hosting complexity while maintaining flexibility.
EgoAlpha's 'Prompt Engineering Playbook' Repo Hits 1.7k Stars
Research lab EgoAlpha compiled advanced prompt engineering methods from Stanford, Google, and MIT papers into a public GitHub repository. The 758-commit repo provides free, research-backed techniques for in-context learning, RAG, and agent frameworks.
4 Observability Layers Every AI Developer Needs for Production AI Agents
A guide published on Towards AI details four critical observability layers for production AI agents, addressing the unique challenges of monitoring systems where traditional tools fail. This is a foundational technical read for teams deploying autonomous AI systems.
Inside Claude Code’s Leaked Source: A 512,000-Line Blueprint for AI Agent Engineering
A misconfigured npm publish exposed ~512,000 lines of Claude Code's TypeScript source, detailing a production-ready AI agent system with background operation, long-horizon planning, and multi-agent orchestration. This leak provides an unprecedented look at how a leading AI company engineers complex agentic systems at scale.
Agentic AI Systems Failing in Production: New Research Reveals Benchmark Gaps
New research reveals that agentic AI systems are failing in production environments in ways not captured by current benchmarks, including alignment drift and context loss during handoffs between agents.
Stop Shipping Demo-Perfect Multimodal Systems: A Call for Production-Ready AI
A technical article argues that flashy, demo-perfect multimodal AI systems fail in production. It advocates for 'failure slicing'—rigorously testing edge cases—to build robust pipelines that survive real-world use.
The Agentic AI Reality Check: 88% Never Reach Production, Here's How to Spot the Fakes
A new analysis reveals widespread 'agent washing' in AI, with most systems labeled as agents being rebranded chatbots or automation scripts. The article provides a 5-point checklist to distinguish real, production-ready agents from marketing hype, crucial for retail leaders evaluating AI investments.
Agent Washing vs. Real Agents: A Production Engineer's Guide to Telling the Difference
A technical guide exposes 'agent washing'—where chatbots and automation scripts are rebranded as AI agents—and provides a 5-point checklist to identify genuinely agentic systems that can survive production. This matters because 88% of AI agents never reach production.
Modern RAG in 2026: A Production-First Breakdown of the Evolving Stack
A technical guide outlines the critical components of a modern Retrieval-Augmented Generation (RAG) system for 2026, focusing on production-ready elements like ingestion, parsing, retrieval, and reranking. This matters as RAG is the dominant method for grounding enterprise LLMs in private data.
A Technical Guide to Prompt and Context Engineering for LLM Applications
A Korean-language Medium article explores the fundamentals of prompt engineering and context engineering, positioning them as critical for defining an LLM's role and output. It serves as a foundational primer for practitioners building reliable AI applications.
Prompt Compression in Production Task Orchestration: A Pre-Registered Randomized Trial
A new arXiv study shows that aggressive prompt compression can increase total AI inference costs by causing longer outputs, while moderate compression (50% retention) reduces costs by 28%. The findings challenge the 'compress more' heuristic for production AI systems.
Fractal Emphasizes LLM Inference Efficiency as Generative AI Moves to Production
AI consultancy Fractal highlights the critical shift from generative AI experimentation to production deployment, where inference efficiency—cost, latency, and scalability—becomes the primary business constraint. This marks a maturation phase where operational metrics trump model novelty.
The Agent Coordination Trap: Why Multi-Agent AI Systems Fail in Production
A technical analysis reveals why multi-agent AI pipelines fail unpredictably in production, with failure probability scaling exponentially with agent count. This exposes critical reliability gaps as luxury brands deploy complex AI workflows.