This comprehensive guide presents a four-layer framework for prompt engineering: controlling input distribution, sampling techniques, multi-sample orchestration, and context engineering for production systems, with practical examples and engineering principles for reliable LLM applications.
This article presents a comprehensive engineering framework for production Text-to-SQL systems, covering schema contracts, prompt engineering, retrieval with permission-aware evidence closure, multi-layer SQL validation, execution isolation, and evaluation methodologies centered on business correctness and security boundaries.
The article details how a production‑grade Agent platform built on Kubernetes, Kafka, and Nacos addresses token budget overruns, uncontrolled skill execution, and RAG hallucinations by introducing a four‑layer runtime architecture, token pre‑allocation, explicit state management, dynamic governance policies, and robust skill specifications.
The article outlines the five‑stage evolution of AI agents—from the basic ReAct loop to self‑driving, self‑optimizing systems—and presents six engineering pillars (verification, stop, state, recovery, isolation, observability) that together form a Harness framework for deploying reliable, production‑grade agents.
The article presents Agent Harness, a production‑grade AI agent framework built on a seven‑layer pyramid that addresses stability, tool safety, cost, hallucination, autonomous decision‑making, multi‑agent collaboration, work‑tree isolation and observability, and validates each layer with real‑world case studies and concrete benchmarks.
The article distills Anthropic’s production‑agent guidance into five groups of twelve concrete MCP patterns—covering tool surface design, interaction semantics, authentication, context economy, and packaging—explaining why each pattern matters, its trade‑offs, and how it helps build safe, stable, low‑cost agent integrations.
This guide explains how to move from fragile, prompt‑only AI agents to production‑grade systems by designing a control layer—called Harness Engineering—covering memory management, workflow orchestration, permission boundaries, automation patterns, and the Intelligent Harness Runtime that makes agents self‑governing and resilient.
This guide explains how to construct production‑ready AI agent systems by balancing cost, latency, and accuracy, offering a decision framework, concrete techniques such as planner‑executor architecture, chain‑of‑thought prompting, verification agents, parallel agents, and file‑system state management, plus real‑world examples and impact metrics.
This article translates Mahesh Balakrishnan’s 42‑point guide on building production databases, covering customer focus, project management, design principles, code review practices, strategy, observability, and research, offering concrete advice for engineers and teams creating robust backend systems.
