AI‑Native Development for Transaction Core: A Five‑Gate Framework to Stabilize Legacy Systems

The order system is the transaction backbone, handling all user ordering and payment actions; its stability directly impacts business profit and loss. Over more than a year, the team performed a three‑stage refactor: (1) establishing a 99.99% SLA baseline, (2) isolating the core payment chain, and (3) modularizing the execution flow with full‑link instrumentation.

Challenges of AI‑Native Core Systems

After the three‑stage overhaul, the system gained stability, scalability, and modularity, but the rise of AI‑generated code introduced new risks:

Error‑pattern migration: AI tends to replicate historical mistakes at scale.

Knowledge‑structuring pressure: Scattered constraints make AI “blind” to team knowledge.

Code‑volume vs review capacity: Production volume multiplies while code‑review resources stay static, increasing defect escape.

Fault traceback difficulty: Human‑AI co‑created code obscures root‑cause identification.

Entropy increase across the pipeline: Faster coding without proportional speed gains in upstream/downstream steps raises overall entropy.

The core problem is not AI’s inability to write code, but the legacy pipeline’s lack of machine‑readable inputs and verifiable outputs. The article presents a five‑gate governance model to retrofit the pipeline for AI coding.

Five‑Gate Governance Model

1. Demand Clarification – Ensure Correct Direction

At this stage no code is produced; the goal is to capture business intent in a structured, precise form. The running example is an overseas order supporting gift cards, where the amount equals face value + overseas service fee. A mismatch between order creation and confirmation would cause financial loss. The gate pins down “what to calculate” and “when a mismatch should block the flow”.

Requirements are expressed as BDD Gherkin scenarios, e.g.:

Given an overseas order selects gift‑card payment with face value 100 and service fee 10,
When the user confirms the order,
Then the gift‑card amount must be 110.

Given a calculation that yields an abnormal amount,
When the order is placed,
Then block and reject the order.

Scenario priority: P0 – must pass before release.

These scenarios become a hard contract linking demand, test, and implementation.

2. Technical Design – Lock Down the How

Using the clarified spec, the design phase answers “how to change” and “how to fallback”. All design decisions are locked before any code is written, preventing AI from improvising during implementation.

The design follows a five‑section template (business goal, overall architecture, detailed scenario design, data‑structure design, stability design). Technical specifications hide all technical terminology, forcing a business‑language‑only description.

For the gift‑card case, the design extracts a five‑part module description (objective, change location, field/config changes, core logic, fallback behavior) and aligns it with knowledge‑base constraints such as “both confirmation and creation must compute the same amount”.

3. TDD Implementation – Test‑First Coding

With the design locked, developers write failing tests (RED), implement code to pass them (GREEN), then refactor (REFACTOR). The RED step creates a test like “gift‑card amount = 110”. GREEN implements GiftCardCalculator.calcAmount. REFACTOR extracts the calculation into a shared method used by both confirmation and creation. TDD guarantees a one‑to‑one trace from requirement → design → test → code.

4. Gate Control – Structured Review

After TDD passes, a gate‑control stage validates the output. Four principles guide the gate:

Machine‑first decision: each review agent emits structured JSON; the gate reads PASS/FAIL from JSON.

Human as auxiliary: only critical decisions (e.g., overriding a constraint) need human confirmation.

All conclusions are persisted locally for traceability.

Failure routes back to the specific stage and issue rather than a generic rework.

Gate checks are distributed across the pipeline (invariant‑reviewer, delta‑guard, bdd‑acceptance), ensuring that every constraint introduced in the spec is enforced in code.

5. End‑to‑End Telemetry – Dashboard and Metrics

The final gate feeds telemetry that turns the development process into data: effort cost per demand, stage latency, knowledge‑base call success rate, gate‑pass rate, and root‑cause statistics. The dashboard drills down from demand to code, exposing five observation dimensions and three typical improvement scenarios (knowledge‑base enrichment, process bottleneck reduction, and agent convergence).

Supporting Infrastructure

Five‑layer bottom‑up architecture: Infrastructure (unified telemetry), Agent system (intent and context orchestration), Development flow (five core stages), Measurement (full‑link observability), Governance (spec/code/arch/BDD parallel review).

Spec‑Driven Claude Code plugin: Automates demand clarification, knowledge‑base alignment, and spec generation, eliminating “human‑only” interpretation.

Incremental code health check: Scans changes across nine dimensions (new external calls, thread pools, error‑code duplication, etc.) and produces a Feishu report for local and CI consumption.

Overall, the article argues that AI‑native development is no longer about model superiority; it is about converting AI output into a verifiable, measurable, and accountable production process. The constant—process design—ensures that AI‑generated code meets the rigorous stability requirements of transaction core systems.