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Latest from Chengwu Tech Stack

44 recent articles
Chengwu Tech Stack
Chengwu Tech Stack
Aug 18, 2026 · R&D Management

Why ForgeX Stopped an AI Delivery Mid-Process — And What It Proves

The article details ForgeX's integration of a real project (Bracelet) which halted at 'action_required' due to incomplete Knowledge/Skill/MCP, demonstrating the platform's safe-stop design; separately, an end-to-end verification in PostgreSQL with a simulated requirement proved the full pipeline works when conditions are met, emphasizing that a releasable candidate is not a production release.

AI-assisted deliveryAgent OrchestrationForgeX
0 likes · 34 min read
Why ForgeX Stopped an AI Delivery Mid-Process — And What It Proves
Chengwu Tech Stack
Chengwu Tech Stack
Aug 16, 2026 · R&D Management

Why AI's 'Done' Isn't Delivery: The Evidence Plane Architecture

This article introduces the Evidence Plane architecture, arguing that AI agents' completion claims must be backed by independently verifiable evidence—including code diffs, test reports, security scans, approvals, and production metrics—organized through Evidence Contracts, automated verification, and traceable evidence bundles to enable trustworthy AI-driven software delivery.

AI agentsEvidence ContractEvidence Plane
0 likes · 31 min read
Why AI's 'Done' Isn't Delivery: The Evidence Plane Architecture
Chengwu Tech Stack
Chengwu Tech Stack
Aug 15, 2026 · Artificial Intelligence

Why AI Agents Fail in Enterprises: The Case for Organizational Context Engineering

This article explains why AI agents that excel in personal projects falter in enterprise environments, arguing that the gap stems not from model limitations but from fragmented organizational knowledge, and proposes a five-layer context engineering framework to make companies AI-readable through versioned context packages, permission controls, and evidence-driven feedback loops.

AI agentsAgent ArchitectureContext Engineering
0 likes · 28 min read
Why AI Agents Fail in Enterprises: The Case for Organizational Context Engineering
Chengwu Tech Stack
Chengwu Tech Stack
Aug 14, 2026 · R&D Management

Why AI Makes Individuals Faster But Organizations Slower: The Bottleneck Paradox

Research shows AI accelerates individual tasks like coding and writing, but organizational throughput doesn't improve because bottlenecks shift to coordination, review, and approval stages; real gains require redesigning workflows, metrics, and decision rights rather than just deploying tools.

AI productivityR&D managementSoftware Development
0 likes · 19 min read
Why AI Makes Individuals Faster But Organizations Slower: The Bottleneck Paradox
Chengwu Tech Stack
Chengwu Tech Stack
Aug 12, 2026 · Artificial Intelligence

Why Your AI Prompts Fail: A Complete Framework for Reliable Results

This article presents a comprehensive framework for effective AI interaction, detailing a six-component prompt structure, task decomposition strategies, JSON output stabilization techniques, and a reusable template to transform vague requests into verifiable, production-ready results through iterative alignment and validation.

AI interactionAI reliabilityJSON output
0 likes · 20 min read
Why Your AI Prompts Fail: A Complete Framework for Reliable Results
Chengwu Tech Stack
Chengwu Tech Stack
Aug 11, 2026 · Artificial Intelligence

Pyramid Theory: The Hidden Logic Behind High-Impact AI Prompts

The article demonstrates how the pyramid principle — conclusion-first, categorized grouping, and layered decomposition — turns vague client requests into structured, verifiable AI prompts, using a contract bulk-import example to show a four-level breakdown from delivery conclusion to executable tasks.

AI PromptingAI collaborationAcceptance Criteria
0 likes · 21 min read
Pyramid Theory: The Hidden Logic Behind High-Impact AI Prompts
Chengwu Tech Stack
Chengwu Tech Stack
Aug 10, 2026 · R&D Management

AI-Native R&D OS Architecture: Codex, Temporal & MCP for End-to-End Delivery

This article details the technical architecture of a company-level AI R&D delivery platform V1.0, positioning it as an AI-native operating system that orchestrates Codex agents via Temporal workflows, connects enterprise systems through an MCP gateway, enforces quality via deterministic CI gates, and ensures full traceability from requirements to production incidents.

AI R&D PlatformAgent OrchestrationCodex
0 likes · 22 min read
AI-Native R&D OS Architecture: Codex, Temporal & MCP for End-to-End Delivery
Chengwu Tech Stack
Chengwu Tech Stack
Aug 9, 2026 · R&D Management

Beyond Codex: Building an Enterprise AI R&D Delivery Platform

The article argues that companies using Codex need an enterprise AI R&D delivery platform integrating project management, stage workflows, Codex as execution engine, MCP for system connectivity, Skills as standardized methods, and quality gates with human approvals to achieve auditable, traceable, and reversible delivery.

AI R&D PlatformCodexMCP
0 likes · 17 min read
Beyond Codex: Building an Enterprise AI R&D Delivery Platform
Chengwu Tech Stack
Chengwu Tech Stack
Aug 7, 2026 · R&D Management

Beyond 'Tests Passed': A Four-Layer Acceptance Framework for AI-Generated Code

This article argues that AI-generated code passing tests is insufficient for delivery, proposing a four-layer acceptance framework—functional, regression, risk, and business verification—backed by a six-part evidence chain and risk-based quality gates to ensure reliable, auditable software releases.

AI-assisted developmentacceptance testingdelivery checklist
0 likes · 19 min read
Beyond 'Tests Passed': A Four-Layer Acceptance Framework for AI-Generated Code