Best AI Prompts for Legacy Code Migration with Google Antigravity

Quick Answer

We treat legacy code migration as a strategic campaign using Google Antigravity agents. This requires precise prompt architectures to guide AI from analysis to deployment. Mastering these instructions is the key to reducing risk and achieving production-ready modernization.

Benchmarks

Refactoring Legacy Code as a Strategic Campaign

What if you could migrate a critical monolith not as a single, terrifying rewrite, but as a series of targeted, manageable operations with clear objectives and real-time progress tracking? This isn’t a futuristic dream; it’s the necessary evolution for tackling the immense technical debt locked within legacy systems. For years, teams have faced the “legacy code dilemma”: aging monoliths in COBOL or Java become unmanageable black boxes, holding business hostage with crippling maintenance costs and security risks. Traditional “big bang” migrations are notoriously slow, astronomically expensive, and have a staggering failure rate because they treat a complex system as a single problem to be solved all at once.

This is where the concept of “Migration as a Campaign” fundamentally changes the game. Instead of a chaotic, all-or-nothing rewrite, we reframe the challenge as a strategic operation. We deploy specialized forces to conquer specific territories (modules) and use a central command center to monitor the campaign’s progress. This approach introduces order, reduces risk, and makes an overwhelming task manageable.

To execute this campaign, we introduce a hypothetical but powerful framework: Google Antigravity. This isn’t a single script; it’s a distributed system for modernization. Its core philosophy is to treat migration as a campaign where you deploy specialized AI agents to specific modules. Each agent is an expert in a particular domain—a data access layer, a business logic component, or a user interface module. These agents work in parallel, and their progress, challenges, and successes are all reported back to a central “Mission Control” dashboard. This provides unprecedented visibility and control over the entire migration lifecycle.

The success of this entire framework, however, hinges on one critical element: the quality of your instructions. The AI agents are powerful, but they are only as effective as the prompts that command them. A vague command yields a vague, potentially flawed result. A precise, architecturally-aware prompt, however, directs the agent to produce clean, idiomatic, and production-ready code. This article will provide the specific prompt architectures you need to command these agents effectively, turning you from a project manager into the master strategist of a highly efficient, AI-powered migration campaign.

Mission Control: Defining the Strategy and Scope

Before you deploy a single AI agent, you need a battle plan. Treating migration as a campaign means you don’t just charge into the codebase; you first establish a central command. This “Mission Control” isn’t a physical room, but a strategic framework you define through precise prompts. It’s where you analyze the terrain, assess the risks, and set the measurable objectives that will guide your entire operation. Getting this phase right is the difference between a chaotic, bug-ridden rewrite and a disciplined, successful modernization.

The Architectural Blueprint: From Monolith to Microservices

The first task in your Mission Control is to draw the map. You must define the target architecture before writing a single line of new code. Your AI agent needs to understand not just what the legacy code does, but where it should live in the new world. This is where you move from simple translation to strategic modernization.

A common mistake is to ask the AI to “rewrite this module in Go.” This is too vague. A better approach is to first ask it to analyze the existing structure and propose a new one based on modern principles like domain-driven design. This forces the AI to think like an architect, not just a compiler.

Prompt Example: Architectural Analysis

Analyze the attached legacy monolith. Identify bounded contexts and suggest a microservices architecture. Output a JSON object mapping legacy modules to proposed services.

This prompt does three critical things: it asks for an analysis of the current state, it requires a proposal for the future state based on established patterns (bounded contexts), and it demands a structured, machine-readable output (JSON). This JSON can then be used to automatically scaffold new service directories or inform your project management tools. You’re not just getting code; you’re getting a strategic plan.

Golden Nugget: When you ask for a JSON mapping, also instruct the AI to include a “migration_complexity_score” (e.g., 1-10) for each module in the JSON. This score, based on factors like external dependencies and cyclomatic complexity, allows you to prioritize which services to tackle first. Starting with a low-complexity service builds momentum and validates your entire migration strategy.

Risk Assessment and Dependency Mapping

Every legacy system is a minefield of hidden dependencies and risky code patterns. The most valuable thing an AI can do in the early stages is to walk ahead of you and flag these mines. Before you even think about refactoring, you need to know which parts of the system are brittle, which are tightly coupled, and where performance bottlenecks are hiding.

This is where you task your AI with a forensic analysis. You’re asking it to be a risk analyst. By identifying synchronous calls that could block threads in a new concurrent environment, or spotting undocumented dependencies on third-party services, you prevent catastrophic failures down the line.

Prompt Example: Dependency and Risk Scan

Scan the `src/legacy/payment` directory. Identify all external dependencies and flag any synchronous calls that could block the main thread. Create a dependency graph.

The output here is twofold: a list of immediate red flags (synchronous I/O) and a visual representation of the system’s coupling. The dependency graph is invaluable. It might reveal a circular dependency between the payment and user modules that you must break before you can separate them into distinct microservices. Addressing this now, during the planning phase, saves you weeks of debugging later.

Setting KPIs for Mission Control

A campaign without measurable objectives is just a wish. Your Mission Control dashboard needs to display real-time progress, but what metrics should you track? Simply measuring “lines of code rewritten” is a vanity metric. It tells you nothing about the quality, performance, or stability of the new system.

You need to prompt the AI to help you define meaningful Key Performance Indicators (KPIs) that align with the goals of modernization: stability, speed, and maintainability. These KPIs will become the success criteria for your entire migration campaign, visible on your dashboard for every stakeholder to see.

Prompt Example: Defining Success Metrics

Based on the proposed Go microservices architecture for our legacy Java monolith, suggest 5 key performance indicators (KPIs) to track on our Mission Control dashboard. For each KPI, provide a baseline value from the legacy system (if known) and a target value for the new microservice. Include metrics for code quality, performance, and deployment velocity.

This prompt forces the AI to think contextually. It will suggest metrics like:

• API Latency (p95): A direct measure of user-facing performance.
• Test Coverage: A proxy for code quality and maintainability.
• Deployment Frequency: How often you can safely ship changes to that service.
• Error Rate: The stability of the new service compared to the old module.

By defining these KPIs upfront, you create a data-driven feedback loop. The dashboard isn’t just for show; it’s the objective truth that tells you whether your campaign is succeeding or if you need to adjust your strategy.

The Vanguard Agent: Boilerplate and Scaffold Generation

Every successful migration campaign begins with securing a beachhead. Before you can refactor a single line of business logic, you need a modern, stable environment to land the new code. This is the job of the Vanguard Agent: its purpose is to generate the foundational infrastructure—the Docker containers, the CI/CD pipelines, the cloud resources—that will house your modernized application. Getting this right is non-negotiable; a shaky foundation will cause every subsequent step to crumble.

Bootstrapping the New Environment

The most common mistake I see teams make is underestimating the complexity of modern scaffolding. They try to hand-write a Dockerfile or cobble together a CI/CD pipeline from a dozen outdated blog posts. This is where the Vanguard Agent, directed by a precise prompt, saves you days of tedious work and prevents subtle, hard-to-debug configuration errors.

Your goal is to generate infrastructure-as-code that is not just functional, but production-ready from day one. This means it must be secure, efficient, and idiomatic for your target stack. When you’re prompting the Vanguard, you’re not just asking for a file; you’re asking for a blueprint for a secure, scalable system.

Example Prompt:

Generate a production-ready `Dockerfile` and `docker-compose.yml` for a Python FastAPI service. Include multi-stage builds and security best practices.

This prompt is effective because it includes specific, expert-level directives. “Multi-stage builds” instruct the agent to create a lean final image by separating the build environment from the runtime environment, which reduces attack surface and deployment times. “Security best practices” tells it to avoid running as root, use non-privileged users, and leverage trusted base images. The agent will return a complete, ready-to-use configuration that you can drop into your repository, instantly creating your new environment’s foundation.

Insider Tip: Always ask for the rationale behind the generated configuration. Append a line to your prompt like: After generating the files, add a comment block at the top explaining the key security and performance choices you made. This forces the AI to act as a consultant, not just a script. It will explain why it chose a specific base image, how the multi-stage build works, and what security flags are being set. This explanation is invaluable for training your team and for auditing the generated code.

Entity and Data Model Translation

With the environment secured, the Vanguard’s next task is tackling the data layer—the heart of any legacy system. Manually translating a complex, denormalized legacy database schema into a modern ORM is a soul-crushing exercise in attention to detail. It’s also incredibly prone to human error. One missed foreign key or mistyped index can lead to catastrophic data integrity issues in production.

The Vanguard Agent excels at this pattern-matching task. You provide the source of truth (the old schema) and the target format (the new ORM), and the agent performs a near-perfect translation. This is where you leverage its ability to hold two complex structures in memory and map one to the other with precision.

Example Prompt:

Given this legacy SQL schema (pasted below), generate the equivalent Prisma schema file. Ensure all foreign key constraints and indexes are preserved.

By explicitly stating the requirement to “preserve all foreign key constraints and indexes,” you prevent the AI from taking shortcuts. A naive translation might create the models but forget the crucial @relation attributes or the @@index directives that ensure query performance and data consistency. This prompt tells the agent that fidelity to the original structure’s integrity is paramount. The result is a modern, type-safe ORM schema that is a direct, reliable reflection of your legacy data model.

API Contract Generation

Finally, before any code is written, the Vanguard Agent can help you define the new system’s public interface. In modern development, we strive for an “API-first” approach. This means defining the contract before implementation, ensuring that front-end and back-end teams can work in parallel and that the final system is predictable and well-documented.

Legacy systems, however, are the opposite of API-first. Their contracts are often implicit, buried in controller logic, or documented in dusty Word files. The Vanguard can reverse-engineer this chaos into a clean, modern OpenAPI (Swagger) specification.

Example Prompt:

Analyze the following legacy API controller code and generate a complete OpenAPI 3.0 specification YAML file. Infer appropriate data types and required fields for all request bodies and responses.

This prompt instructs the agent to perform a deep analysis of the code, identifying endpoints, HTTP methods, expected payloads, and response shapes. It’s a powerful way to create a single source of truth for your API. Once you have this OpenAPI spec, you can use it to generate client SDKs, server stubs, and interactive documentation, effectively locking in the contract and de-risking the entire development process.

The Infiltration Agent: Business Logic Extraction and Refactoring

What happens when your most critical business rules are buried under decades of UI code, database calls, and obsolete framework patterns? This is the core challenge of legacy migration. The “Infiltration Agent” is the specialist you deploy for surgical extraction. Its mission is to sneak past the noise, isolate the pure business logic, and refactor it into a clean, modern, and testable form. This isn’t about a brute-force rewrite; it’s a precise operation to save the patient and discard the diseased tissue.

The most difficult part of any migration is untangling the business logic from the infrastructure it’s entangled with. Modern architecture principles demand a clear separation of concerns, but legacy systems often have business rules, UI rendering, and database access all woven into a single, tangled function. This is where Chain of Thought prompting becomes your most powerful tool. You aren’t just asking the AI to write code; you’re asking it to think like a senior engineer and architect the solution.

Decomposing the Monolith: Surgical Extraction of Business Rules

Your first task for the Infiltration Agent is to perform a clean extraction. You need to give it a clear target and a strict set of rules for what to keep and what to discard. The goal is to produce a pure function that contains only the business rule, completely decoupled from its original environment.

Example Prompt:

Analyze the code in `auth_service.java`. Your task is to isolate the core business logic for 'User Authentication'. Follow these steps:
1. Identify the sequence of checks: password validation, account status verification, and role assignment.
2. Extract only this logic into a new, standalone function named `authenticateUser`.
3. The new function must be decoupled. Remove all direct database calls (e.g., `db.connect()`, `sql.query()`) and UI-related logging. Instead, define clear interfaces for dependencies like a `userDatabase` and `logger`.
4. Return the new, clean function with its signature and clear parameter definitions.

This prompt forces the AI to reason about the code’s structure before writing anything. It moves beyond simple pattern matching and starts to emulate architectural thinking. A key expert insight here is to always ask the AI to define interfaces for the dependencies it removes. This not only cleans the logic but also designs the contract for the next phase of your migration campaign, where you’ll implement those interfaces with modern services.

Modernizing Syntax and Patterns: From Spaghetti to Structure

Once the logic is isolated, it’s often still written in an outdated style. Think callback hell, manual state management, or verbose conditional blocks. The Infiltration Agent’s next job is to refactor this raw logic into modern, idiomatic code that is easier to read, maintain, and test.

Example Prompt:

Refactor the following callback-based JavaScript function into modern, async/await syntax. Ensure you handle errors gracefully with try/catch blocks. The function should maintain its original return values but be significantly more readable.

**Original Code:**
```javascript
function getUserData(userId, callback) {
  fetch('/api/users/' + userId)
    .then(response => {
      if (!response.ok) {
        throw new Error('Network response was not ok');
      }
      return response.json();
    })
    .then(data => callback(null, data))
    .catch(error => callback(error, null));
}

By providing the "before" and "after" context, you guide the AI toward a specific modernization goal. This is a form of few-shot prompting that teaches the AI your team's preferred style. A common pitfall here is forgetting error handling. Always include instructions for graceful error management, as legacy code is notoriously bad at it. This single instruction can prevent a cascade of runtime errors in your new system.

### The Guardian Agent: Ensuring Fidelity with Unit Tests

Before you delete a single line of the old, battle-tested code, you must prove the new code works exactly as intended. This is the job of the Guardian Agent, and it starts with comprehensive unit tests. The Infiltration Agent extracts and refactors, but the Guardian validates. This creates a crucial safety net.

**Example Prompt:**

Write a comprehensive suite of Jest unit tests for the calculateTax function provided. Your tests must cover:

• Standard cases (e.g., income of $50,000).
• Edge cases, including zero income, negative income, and very large numbers.
• Mock any external dependencies the function might have.
• Ensure the test descriptions are clear and human-readable.

> **Golden Nugget:** A powerful technique is to prompt the AI to write the tests *before* you've even fully finalized the refactored function. This is a practice known as "test-driven development with AI." By asking for the tests first, you force the AI to think about all the possible inputs and edge cases. This often reveals flaws or ambiguities in your own requirements, allowing you to correct the logic *before* you've invested time in a flawed implementation. It's a massive time-saver and a hallmark of a mature migration strategy.

This iterative cycle of **Extract -> Refactor -> Test** is the engine of your migration campaign. It ensures that every piece of business logic you modernize is verifiably correct, building a foundation of trust in both the new codebase and the AI agents executing your commands.

## The Guardian Agent: Security and Compliance Auditing

When you're migrating a critical application, how do you ensure your new code isn't just functional, but fundamentally secure and compliant? Legacy systems are often riddled with security debt—outdated libraries, forgotten backdoors, and patterns that violate modern standards. The Guardian Agent is your AI-powered security auditor, designed to sniff out these vulnerabilities before they ever make it into production. It operates on the principle that security isn't a final checklist item; it's a foundational requirement woven into every line of code from the start.

### Vulnerability Scanning via Prompting

Static analysis tools are excellent at finding known syntax errors and simple patterns, but they often miss the nuanced, context-dependent vulnerabilities that can cripple a system. Think of a subtle logic flaw that allows a user to bypass a payment step or an access control issue that lets a junior employee view executive salaries. These are business logic errors, not syntax errors, and they require a deep understanding of code intent. This is where prompting a Large Language Model (LLM) becomes a game-changer. You can instruct the Guardian Agent to reason about the code's purpose and identify potential exploits that a rule-based scanner would ignore.

**Prompt Example:**

Review the following code snippet for security vulnerabilities, specifically focusing on OWASP Top 10 issues like SQL Injection and Broken Access Control. Suggest fixes.

> **Expert Insight:** The real power here is combining the LLM's broad knowledge with your specific context. For instance, if you're migrating an e-commerce platform, you can add a clause like, "...and pay special attention to any logic that could allow for price manipulation or cart tampering." This directs the AI's focus to the highest-risk areas for your specific application, yielding far more relevant and actionable results than a generic scan.

### Compliance Mapping

Navigating the labyrinth of data privacy regulations like GDPR and HIPAA is a non-negotiable part of modern software development. A single misstep in how you handle personal data can lead to crippling fines and a loss of customer trust. When migrating from a legacy system, the challenge is even greater. Your old code may have been written before these regulations even existed, meaning it's almost certainly non-compliant by default. The Guardian Agent can be tasked to analyze data handling logic and rewrite it to meet modern legal standards, effectively future-proofing your application.

**Prompt Example:**

Analyze this data processing function. Does it comply with GDPR ‘Right to be Forgotten’ principles? If not, rewrite the function to include pseudonymization.

This prompt forces the AI to do more than just translate syntax; it must reason about the legal implications of the data flow. It will identify where personal data is stored, how it's used, and whether the system provides a mechanism for its complete removal upon request. By rewriting the function to include pseudonymization (replacing private identifiers with fake ones), you create a system that respects user privacy by design, a core tenet of GDPR.

### Proactive Secret Management

One of the most common and dangerous sins in legacy codebases is the hardcoded secret. API keys, database passwords, and encryption tokens are often committed directly into source files, creating a massive security liability. Manually hunting these down across thousands of lines of code is tedious and prone to error. The Guardian Agent excels at this pattern-matching task, but it goes a step further: it can generate the necessary Infrastructure-as-Code (IaC) to manage these secrets properly.

Your prompt should not only ask the agent to find and remove hardcoded secrets but also to provide the modern solution.

**Example Prompt for Secret Management:**

Scan the following code for hardcoded API keys or credentials. Remove them and replace them with environment variable references. Then, generate a HashiCorp Vault configuration snippet to securely store and inject these secrets.

The result is a two-for-one benefit. The AI cleans up the immediate vulnerability in the application code and simultaneously provides the blueprint for a robust, scalable secret management system using a tool like Vault. This elevates the migration from a simple code rewrite to a genuine architectural improvement, leaving you with a system that is not only modern but also demonstrably more secure and compliant than its predecessor.

## The Scout Agent: Documentation and Knowledge Transfer

You've just inherited a critical service with zero documentation. The original developers are gone, and the only comments in the code are `// TODO: fix this` or `// it works, don't touch`. This is the "archaeology" phase of migration, where you spend more time deciphering intent than writing new code. The Scout Agent is your expert archaeologist. Its mission isn't to rewrite code, but to excavate knowledge and build a living map of the legacy system. By focusing on documentation first, you transform a black box into a transparent, understandable component, which is the most critical first step for any successful migration campaign.

### Generating Living Documentation

Legacy systems often suffer from "documentation rot"—the official docs are years out of date, if they exist at all. The Scout Agent solves this by generating documentation directly from the source of truth: the code itself. This ensures your documentation is always accurate and version-controlled right alongside your code. Instead of a separate, dusty Word document, you create a `README.md` that becomes an integral part of the project.

A powerful prompt for the Scout Agent looks like this:

> **Prompt:**
> ```
> Generate a detailed README.md for this service. Include sections for Setup, API Endpoints (list them with parameters and return types), Database Schema (using SQL DDL), and a high-level architecture diagram (using Mermaid syntax).
> ```

This single prompt can save you days of manual investigation. The Scout Agent will parse the codebase, identify all public functions or controller routes, infer their parameters from usage, and even reconstruct the database schema from model definitions or migration files. The Mermaid diagram is a game-changer; it gives you an instant visual overview of how components interact, which is invaluable for identifying tight coupling and planning your refactoring strategy.

### Explaining the "Why"

Finding `what` a piece of code does is easy. Figuring out `why` it was written that way is the real challenge. This is where most automated tools fail, but the Scout Agent excels by reverse-engineering business intent. This is especially crucial for complex or seemingly bizarre code blocks. A regular expression is a perfect example—it might look like gibberish, but it was written to solve a very specific, and often painful, problem.

Consider this prompt:

> **Prompt:**
> ```
> Analyze this complex regex string: ^(?=(?:[^a-z]*[a-z]){3})(?=(?:[^0-9]*[0-9]){2})(?!.*(.).*\1).{8,16}$ Write a plain English explanation of what it matches and why it was likely written this way.
> ```

The Scout Agent won't just tell you it validates a password; it will tell you it enforces a policy of "at least 3 letters, 2 numbers, no repeating characters, and a length between 8 and 16." This "why" is pure gold. It's the business requirement captured from the code. By documenting this, you preserve the original intent, preventing future developers from "simplifying" the code and accidentally breaking a critical business rule.

### Training the Team

The final piece of the knowledge transfer puzzle is ensuring your human developers can keep up with the AI agents. As the Vanguard and Infiltration agents begin their work, the codebase will change rapidly. Your team needs a guide to understand the *patterns* of change, not just the final output. The Scout Agent can generate these guides, acting as a technical writer that explains the AI's own work.

This is where you shift from asking for code to asking for explanations. After an agent completes a task, you can prompt the Scout:

> **Prompt:**
> ```
> Create a "Migration Guide" for this module. Compare the old jQuery implementation to the new React version. Explain the key architectural changes, such as the shift from imperative DOM manipulation to declarative state management, and list the new dependencies that were introduced.
> ```

This guide becomes an essential onboarding document for your team. It explains the *new* world order, teaching your developers the modern patterns you've established. It bridges the gap between the old and new, ensuring that the knowledge gained during the migration is transferred to the entire team, making them more effective and confident in the modern stack.

## Case Study: The "Big Bank" Migration Campaign

Imagine a 20-year-old COBOL mainframe system. It’s the digital bedrock for a major financial institution, processing millions of transactions daily, but it's a black box of undocumented business rules and brittle dependencies. The cost of maintenance is astronomical, and finding developers who can even read the code is a strategic risk. This was the scenario for "Big Bank," a fictionalized but highly realistic institution that decided to migrate its core interest calculation and account management modules to a cloud-native Java microservices architecture using the Antigravity framework. They didn't treat this as a one-off project; they launched a full-scale **migration campaign**.

The campaign's success hinged on a simple but powerful concept: deploying specialized AI agents to specific tasks and orchestrating them through a central command hub. Instead of a single, monolithic code translation, the bank broke the problem down into hundreds of smaller, manageable "missions."

### Deploying the Agent Swarm

The campaign began with the **Vanguard Agent**. Its first task wasn't to touch the COBOL itself, but to build the foundation for the new world. Using a high-level prompt, the Vanguard was instructed to scaffold a production-ready Kubernetes cluster in their AWS environment, complete with Terraform configurations for infrastructure-as-code, Helm charts for deployment, and a basic CI/CD pipeline. This ensured that when the new microservices were ready, they had a modern, scalable home to land in, eliminating weeks of manual DevOps work.

Next, the **Infiltration Agent** was dispatched to tackle the most critical and complex piece: the "Interest Calculation" module. This module was a tangled web of procedural logic, hidden in a 5,000-line COBOL paragraph. The prompt given to the Infiltration Agent was surgical: "Extract the core interest calculation logic from `ACCT-INTEREST-PARA`. Ignore the I/O handling and data definitions. Refactor the extracted logic into a pure, stateless Java function, preserving all rounding rules and business exceptions." The agent successfully isolated the 200 lines of pure business logic, leaving behind the legacy I/O clutter.

### Mission Control: Orchestrating the Campaign

The true power of the Antigravity framework was visible in the **Mission Control Dashboard**. This wasn't just a progress bar; it was a real-time operational view of the entire campaign. As 50+ agents worked in parallel, the dashboard provided a clear, at-a-glance summary:

*   **Agent Status:** A grid showing the state of each agent (e.g., Vanguard: `Idle`, Infiltration: `In Progress (78%)`, Guardian: `Pending`, Scout: `Completed`).
*   **Code Line Velocity:** A live graph tracking the number of lines of modern Java code generated versus the number of lines of COBOL retired, showing tangible progress every hour.
*   **Quality Assurance Gate:** A prominent "Translation Accuracy" score, which ran continuous unit tests against both the legacy system's output and the new microservice's output for the same inputs, flagging any discrepancies.
*   **Risk & Anomaly Alerts:** A feed highlighting potential issues, such as "⚠️ Infiltration Agent detected a non-deterministic loop in module X" or "✅ Guardian Agent has verified GDPR compliance for data handling."

This dashboard transformed the migration from a black-box process into a transparent, manageable operation. The project lead could see exactly where resources were needed and which parts of the campaign were on track.

### The Results: Quantifiable Success

The campaign concluded with outcomes that exceeded all initial projections. By leveraging a parallel, agent-driven approach, Big Bank achieved:

*   **40% Reduction in Migration Time:** The campaign was completed in 8 months, compared to an estimated 14-month timeline for a traditional manual rewrite. The parallel execution of tasks by specialized agents was the key driver.
*   **99.9% Accuracy in Logic Translation:** The continuous testing loop within Mission Control ensured that the new Java microservices produced identical results to the original COBOL module for over 10 million test cases, building immense confidence in the new system.
*   **Zero Downtime During Cutover:** The Vanguard's pre-built infrastructure allowed for a blue-green deployment strategy. The new services were brought online alongside the mainframe, and traffic was seamlessly switched over in seconds, with no impact on customers.

> **Expert Insight:** The most critical "golden nugget" from this campaign was the role of the **Guardian Agent** in the final phase. Before the cutover, we tasked it with a full security and compliance audit of the newly generated code. It identified three potential PII handling issues that were present in the original COBOL but would have violated modern data privacy standards. Fixing these pre-emptively saved the bank from a potential compliance nightmare, proving that this approach isn't just about speed—it's about building a better, safer system.

## Conclusion: Commanding the Fleet

We've treated this migration not as a single, monolithic task, but as a strategic campaign orchestrated from a central **Mission Control**. You've seen how the hierarchy of prompts works: starting with high-level architectural plans, then dispatching specialized **Agents** to handle specific modules—Vanguard for scaffolding, Infiltration for logic, and Guardian for security. This is the core of the Antigravity framework: a systematic approach that replaces chaotic manual refactoring with targeted, intelligent automation.

### Your Role as Mission Commander

The most critical takeaway is that **Google Antigravity doesn't replace the developer; it elevates them**. Your role shifts from a line-by-line coder to a Mission Commander. You provide the strategic vision, define the rules of engagement, and review the output with architectural oversight. The AI handles the repetitive, soul-crushing work of translation and refactoring, but your expertise is what guides the fleet. You are the architect, and the AI is your tireless, hyper-competent junior developer.

> **Golden Nugget:** The real power isn't in a single massive prompt. The campaigns that succeed are built on a chain of small, verifiable steps. After each agent completes a micro-task, the next prompt should be: "Review the output against the original requirements and list any discrepancies." This forces a verification loop that catches errors early and builds immense trust in the process.

### The Future is Prompt Engineering

The world's most critical software—financial systems, government infrastructure, healthcare databases—is built on aging codebases. Modernizing it is a multi-trillion dollar challenge. The key skill for tackling this in 2025 and beyond isn't just knowing a new programming language; it's **prompt engineering for code migration**. It's the ability to deconstruct a complex legacy problem into a sequence of clear, unambiguous commands that an AI can execute flawlessly. This is the new craft, and the developers who master it will be the ones who modernize the world.


### Critical Warning

<div class="nugget-box blue-box">
    <h4>Prompt Architecture: The JSON Map</h4>
    <p>Never ask an AI to 'rewrite' blindly. Instead, prompt it to analyze the legacy monolith, identify bounded contexts, and output a JSON object mapping legacy modules to proposed microservices. This structured output serves as your architectural blueprint for scaffolding and project management.</p>
</div>


## Frequently Asked Questions
**Q: What is the 'Migration as a Campaign' concept**

It is a strategy that reframes a chaotic rewrite into a series of targeted, manageable operations executed by specialized AI agents with centralized oversight

**Q: How does Google Antigravity fit into this**

It is a hypothetical framework representing a distributed system where AI agents tackle specific modules in parallel, reporting progress to a 'Mission Control' dashboard

**Q: Why is JSON output important in these prompts**

JSON provides a machine-readable architectural plan, allowing for automated scaffolding of new services and integration with project management tools


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