Agent in a Box

Autonomous Legacy-to-Modern Code Migration Agent (Goose Framework)

engineering
1

Autonomous Legacy-to-Modern Code Migration Agent (Goose AI Framework)

Problem Statement

Startups and scaling engineering teams often find themselves trapped by "Technical Debt Gravity." As products evolve, core logic remains trapped in legacy patterns (e.g., migrating from CommonJS to ESM, moving from Express to Fastify, or shifting from monolithic functions to microservices). Manual refactoring is a high-risk, low-reward activity for senior developers who would rather build new features.

The specific challenge is that generic AI chat interfaces lack context of the entire file system and local environment. A developer cannot easily say "Update every file in this directory to use the new TypeBox schema instead of Zod" without manually copying and pasting dozens of files. This leads to configuration drift, where parts of the codebase are modernized while others lag behind, creating a fragmented DX (Developer Experience).

Furthermore, current automated tools often fail because they don't run the code they generate. A standard LLM might suggest a refactor that breaks the build, and the developer only finds out minutes later after manual intervention. There is a desperate need for an autonomous AI coding assistant setup that can not only write code but also execute shell commands, run tests, and self-correct using the Goose AI framework’s native ability to interact with the local environment via MCP (Model Context Protocol). This is a core component of modern Autonomous Engineering Post-Mortem & RCA Agent workflows.

What the Agent Does vs. Doesn't Do

What it Does

  • Contextual Analysis: Scans entire local directories to understand dependencies and architecture.
  • In-place Refactoring: Uses Goose to directly modify files on the local disk.
  • Validation Loops: Automatically runs npm test or go test after modifications and fixes errors based on compiler output.
  • MCP Integration: Leverages the Model Context Protocol to access documentation or external APIs during the coding process.

What it Doesn't Do

  • Architecture Decision Making: It won't decide to migrate to AWS; it only executes the migration steps defined by a human.
  • PR Approval: It does not self-merge; it creates a branch and pushes for human review.
  • Security Auditing: While it follows patterns, it is not a replacement for specialized security scanning tools like Snyk. For broader security workflows, see our Autonomous Vendor Risk Assessment & Security Questionnaire Agent.

Workflow

  1. Input & Scope: Developer provides a migration goal (e.g., "Refactor all API routes to use the new Auth middleware") and a target directory.
  2. Inventory Scan: Goose uses the developer extension to list files and read current implementation patterns.
  3. Plan Generation: The agent outputs a multi-step execution plan (e.g., 1. Update Utils, 2. Update Controllers, 3. Update Tests).
  4. Iterative Execution: Goose modifies one file at a time. After each modification, it runs the local build command (e.g., tsc or make build).
  5. Self-Correction: If the build fails, Goose reads the error log, identifies the line of code it just wrote, and applies a fix.
  6. Verification: Once the build passes, Goose runs the test suite.
  7. Output: Goose commits the changes to a new git branch and provides a summary of changes. This mirrors the efficiency of an Automated API Documentation & SDK Generator Agent.

Tool Stack

  • Goose AI Framework - Open-source agentic framework by Block for local environment interaction.
  • Claude 3.5 Sonnet (via Anthropic API) - Best-in-class model for coding tasks and complex reasoning.
  • Model Context Protocol (MCP) - Open standard for connecting AI models to local/remote data and tools. [Unverified]
  • GitHub MCP Server - Enables the agent to interact with repositories, create branches, and manage PRs. [Unverified]
  • Express.js - Fast, unopinionated, minimalist web framework for Node.js (Common migration source).
  • Fastify - Low overhead framework used as a modern migration target. [Unverified]
  • Docker - Containerization platform for isolated migration environments. [Unverified]
  • npm - Package manager for Node.js dependency management. [Unverified]
  • Snyk - Developer security platform for vulnerability scanning. [Unverified]
  • TypeBox / Zod - TypeScript-first schema validation libraries. [Unverified]

Quick Integration

Goose AI Framework (OpenAI-Compatible Migration)

import openai

# GooseAI is OpenAI-compatible. 
# Verified: 2024-05-22

openai.api_key = "YOUR_GOOSE_AI_API_KEY"
openai.api_base = "https://api.goose.ai/v1"

def migrate_code_snippet(legacy_code):
    response = openai.Completion.create(
      engine="gpt-neo-20b", # Or other available GooseAI models like 'fairseq-13b'
      prompt=f"Convert the following CommonJS code to ES Modules:\n\n{legacy_code}\n\nModern ESM code:",
      max_tokens=100,
      temperature=0
    )
    return response.choices[0].text.strip()

# Example usage for legacy-to-modern migration
legacy_js = "const fs = require('fs');\nmodule.exports = () => { console.log('hello'); };"
print(migrate_code_snippet(legacy_js))

Source: Docs

Claude 3.5 Sonnet (Migration Logic)

import anthropic

client = anthropic.Anthropic(
    api_key="your_api_key_here",
)

message = client.messages.create(
    model="claude-3-5-sonnet-20240620",
    max_tokens=1024,
    system="You are an expert software engineer. Your task is to refactor legacy code to modern standards while maintaining functional parity.",
    messages=[
        {
            "role": "user",
            "content": "Refactor this Express route to use Fastify and TypeBox: app.post('/user', (req, res) => { const user = req.body; res.send(user); });"
        }
    ]
)

print(message.content[0].text)

Source: Docs

Real-World Examples

AWS outcome: Reduces modernization timelines from months to weeks or days using agentic AI workflows. Read case study

Prompt Skeletons

System Persona Prompt

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