Official tech channel of AliExpress International Tech Division, showcasing the latest technology developments and innovations in global e‑commerce.
The article details how the massive 700‑plus error‑code validation system in AliExpress’s marketing pipeline is protected by an AI‑driven, multi‑phase attack‑defence pipeline that leverages Saiga Mock for non‑destructive testing, MQ‑driven orchestration, and plug‑in sub‑engines to achieve rapid, risk‑free verification.
The article explains how traditional static models are limited and introduces a self‑evolution paradigm that lets AI agents continuously learn from interaction feedback, evolving not only model parameters but also prompts, skills, memory and workflows through a closed execution‑feedback‑adjustment loop, while discussing concrete mechanisms, algorithms, and remaining challenges.
The article explains how the ECC Continuous Learning module silently watches every tool invocation of an AI agent, extracts recurring implicit habits, turns them into confidence‑scored instinct files, manages them through a CLI, and feeds them back into the agent’s memory system to create a fully automated learning feedback loop.
The article presents an AI‑driven cross‑domain fund element knowledge graph that assembles runtime call‑chains, SQL templates and field registrations into trusted facts, uses multi‑agent collaboration to trace field transformations across repositories, and delivers structured risk analysis, impact‑scope queries, and automated loss‑prevention recommendations for AliExpress’s financial operations.
The article details a full‑chain intelligent ticketing platform that lets engineers configure tools and skills, run AI agents to diagnose and resolve tickets automatically, and continuously improve through a closed‑loop feedback system, highlighting architecture, runtime flow, configuration steps, and operational metrics.
To automate UI testing for constantly changing new features, the team built a six‑layer framework—case generation, pre‑construction, environment orchestration, self‑planning execution, assertion & attribution, and reporting—showing that AI speeds case writing but 80% of value lies in surrounding engineering layers.
Starting in May 2025, the Tmall/Taobao overseas team standardized AI coding through a five‑stage Specification‑Driven Development (SDD) SOP, evolving from manual Vibe Coding rules to the unified All‑in‑One skill, which raised AI coding usage from under 5% to over 90% across multiple business domains.
This article presents a comprehensive, fine-grained evaluation framework for AI agents that moves beyond traditional text-similarity metrics, defines architecture-aligned quality, cost, and performance indicators for each core module, describes dataset construction, LLM-as-Judge tasks, execution engine, and visual dashboards, and shares practical lessons and future directions.
The article analyzes the challenges of applying AI coding to AliExpress' high‑traffic, complex marketing system, introduces an SDD‑driven AAIC workflow, builds a business‑knowledge base for the dynamic‑Ticket feature, validates improvements with precision/recall metrics, and outlines architecture and testing upgrades to sustain AI‑assisted development.
The article presents a detailed analysis of a digital‑employee AI system that handles both known and unknown fund‑settlement queries by using a five‑step confidence‑based routing chain, a Draft‑Critique‑Refine decision loop, and a persistent AUDNP memory ring to continuously learn and expand its capabilities.
