AIDE (Agent-Informed Development Engineering) -- A Software Development Methodology for the Agentic Era v1.0

Author: CTO (20+ years of architecture experience, 3 years of hands-on AI agent experience) Based on: GPT/Claude/Gemini triple deep research + Team Alpha (Integrationists) 2 reports + Team Beta (Radicals) 1 report Date: 2026-02-18

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Part 5: CI/CD Pipeline

AIDE CI/CD Diagram

flowchart TB
    A["Change Detected<br/>code / meta / skill"] --> B["Stage 1: Static Analysis<br/>TypeScript strict · ESLint · Prettier<br/>Security linters"]
    B --> C["Stage 2: Unit Tests<br/>Parsers · Policies · Business Logic · State Transitions"]
    C --> D["Stage 3: Property-Based Tests<br/>Business Invariant Property Verification"]
    D --> E["Stage 4: Integration Tests<br/>Cross-Feature Coordination · Data Flow · Integration Environment"]
    E --> F["Stage 5: Eval Suites<br/>Dataset + Scenario-Based Code Quality Evaluation"]
    F --> G["Stage 6: Security Gate<br/>AI-Generated Code Security Scan<br/>XSS · SQL Injection · Logic Errors<br/>SCA / Dependency Scan"]
    G --> H["Stage 7: Meta-File Validation<br/>Tier 1 Size Limit (300 lines)<br/>manifest.yaml Consistency"]
    H --> I{"All Gates<br/>Pass?"}
    I -->|Yes| J["Canary Deploy<br/>Conservative Release · Progressive Rollout"]
    I -->|No| K["Block & Report<br/>Structured Error Feedback"]
    J --> L["Production Monitor<br/>Traces · Metrics · Latency · Alerts"]
    L --> M["Eval Flywheel<br/>Incorporate Production Failures into Eval Datasets"]
    M --> F

Eval Flywheel Concept

The Eval Flywheel is a continuous improvement loop that automatically incorporates failures discovered in production into eval datasets to prevent regressions:

1. Production Monitoring: Detect errors/anomalous behavior from logs/metrics
2. Failure Case Collection: Structure the relevant input/context/expected results
3. Eval Dataset Incorporation: Add new test cases to evals/datasets/
4. Automatic CI Execution: The case is included in gates from the next deployment
5. Progressive Quality Improvement: Eval datasets become richer over time, strengthening regression prevention

# evals/datasets/production-failures.yaml
- id: "PF-2026-0218-001"
  source: "production_log_abc123"
  discovered_at: "2026-02-18T10:30:00Z"
  scenario:
    feature: "cart"
    action: "Discount rate application error in cart total calculation"
    input:
      items:
        - unit_price: 10000
          quantity: 2
          discount_rate: 15
  expected_behavior:
    - "Amount after discount: 17,000 KRW"
    - "Total must not be negative"
  actual_behavior: "Amount error due to processing discount rate as decimal instead of percentage"
  severity: "high"
  fix_applied: "Fixed calculate_item_price function in logic.ts"

Security Gate

The Security Gate runs at Stage 6 of CI/CD and includes the following:

1. AI-generated code security scan: Detect XSS, SQL Injection, and logic error patterns (ESLint security plugins, Semgrep, etc.)
2. Auth/AuthZ verification: Confirm that appropriate authentication middleware is applied to all API endpoints
3. SCA (Software Composition Analysis): Scan npm packages and external dependencies for vulnerabilities
4. Sensitive data exposure check: Verify that sensitive information (passwords, tokens, personal data) is not included in logs or responses

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