The Slog is Real: Possibilities and Limitations of AI-Assisted AngularJS Migrations

If you have been in the web development ecosystem for the last decade, "AngularJS Migration" is likely a phrase that induces a specific kind of headache. It’s tedious, intricate, and forces you to handle surprises that tend to appear far too late in the process.

With the rise of agentic AI, the obvious question is: Can we just hand this off to an LLM?

We conducted a deep-dive research spike to answer this. After iterating through multiple prototypes—from naive manual prompting to complex agentic swarms—we found that while AI isn’t a magic "fix-it" button, we have high confidence that most of the migration can be automated today if architected correctly.

Here is what we learned about the possibilities, the limitations, and the reality of AI-assisted migrations.

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The Trap of the "Do It All" Agent

Our initial hypothesis was that modern coding agents (like Cursor, GitHub Copilot, or Claude Code) could handle this natively. We tried simply asking a coding agent to "migrate this whole project from AngularJS to Angular".

The Result: It failed.

While tools like Cursor are incredible for focused tasks, asking them to handle a systemic migration caused them to spiral. Without strict guidance, we found that agents:

• Lost Context: They became subject to "bloated context" or context rot, unable to retain the full architectural picture.
• Got Lazy: They often hallucinated completions, wrote sloppy code, or hit a wall.
• Spiral: Without a "human in the loop" or a rigid harness, they would make assumptions that compounded into broken code.

We realized that asking an agent to take on such a feat is a cool thought experiment but impractical.

We kept experimenting—tweaking processes, introducing new tools, and even restructuring parts of the workflow—but every attempt either created new bottlenecks or simply shifted the problem somewhere else. After a string of false starts and dead ends, we finally uncovered an approach that not only addressed the root issues but opened the door to a far more efficient and predictable path forward.

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The Solution: The "Balanced" Pipeline

We found success not by relying solely on AI, but by building a Balanced AI+Code Workflow. Instead of asking the AI to "manage the project," we used Python scripts to handle deterministic tasks (scaffolding, file batching, dependency mapping) and used AI strictly for the semantic reasoning tasks it excels at.

Our successful prototype followed this four-phase structure:

1. Scaffolding & Discovery

We created a monorepo and used AI to "inventory" the legacy app. We didn't just copy files; we had the AI figure out how to run the app and stub external dependencies.

2. Extraction (Metadata)

Before writing a single line of modern code, we used LLMs to analyze the legacy files and extract metadata about entities (components, services) and their relationships.

• Success Rate: We achieved an average 80% success rate in accurately identifying entities and relationships during this phase.

3. Dependency Graphing

Using the extracted metadata, we built a hierarchy. You cannot migrate a leaf node if you don't understand its root dependencies. We used this data to prioritize migrations into "waves," processing independent leaves first.

4. Migration

We batched files and sent them to the LLM with specific "migration prompts" based on our graph.

• Success Rate: Actual code migration varied between 50% and 75% success. However, nearly 100% of files were migrated with some issues, meaning the human developer was reviewing and fixing rather than writing from scratch.

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Key Learnings & Limitations

1. Model Economics Matter

We benchmarked different models on the same workload. The difference in cost and speed was staggering for a projected scale of 100,000 entities:

• Claude Sonnet 4.5: High capability, but slow. Projected cost: ~$16,340.
• DeepSeek 3.1: Surprisingly capable for this specific task. Projected cost: ~$1,760.

2. Prompt Engineering > Fine Tuning

We initially thought we might need to fine-tune a model on Angular syntax. We were wrong. Model training is expensive, time-consuming, and niche. Furthermore, frontier models improve every few months, rendering custom models obsolete.

Instead, we found that automated prompt optimization (using training data to let the AI improve its own prompts) yielded the best results.

3. Vector Search is Not Enough

Common RAG (Retrieval Augmented Generation) approaches using vector/similarity search proved ill-suited for code analysis. Old-school tools like Bash scripts, Grep, and LSPs (Language Server Protocols) provided much better context for the AI than vector embeddings.

4. The "Human in the Loop" is Mandatory

AI is non-deterministic. You cannot fully detach humans from the process because the AI lacks business logic knowledge and opinions on UI/UX. However, by batching decisions, we can defer human intervention so engineers are reviewing "waves" of work rather than unblocking the AI continuously.

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The Verdict

Is a fully automated, one-click migration possible today? No.

Is a highly automated pipeline that reduces effort significantly possible? Yes.

Our research confirms a high confidence level that an AI-assisted pipeline is viable. By treating AI not as a replacement for the developer, but as a reasoning engine plugged into a traditional code automation pipeline, we can turn a multi-year migration slog into a manageable, accelerated project.

And whether you decide to migrate with AI, put your team to work on it, or stay on AngularJS to maximize the useful life of your application, HeroDevs provides a secure drop-in replacement version of AngularJS that will keep your application secure and compliant. Explore pricing with HeroDevs.