Can AI Reverse Engineer Any App?

AI coding agents can recreate impressive interfaces, trace API calls, and build functional prototypes from existing websites. But can they reverse engineer an entire software product, including the backend logic that powers its data?

I wanted to find out by attempting to replicate Google Analytics.

The experience taught me something important about AI-powered reverse engineering. Reproducing what a product looks like is relatively straightforward. Reproducing what it knows, how it calculates results, and why those results are reliable is a much harder problem.

I could get some metrics close to the original, with traffic volume reaching around 90% accuracy in my experiment. However, several backend dependencies remained difficult to reproduce, and the gap between a convincing interface and a reliable analytics product became increasingly obvious.

That experiment changed how I think about AI coding agents and the limits of reverse engineering.

GitHub Link:morluto/rea

Traditional reverse engineering involves studying an existing application to understand its internal behavior, architecture, data flows, and implementation.

AI-powered reverse engineering applies large language models (LLMs), such as Gemini and Claude, alongside coding agents and inspection tools to automate parts of that investigation.

A workflow commonly described as Reverse Engineer Anything (REA) follows a straightforward idea: provide a target URL or feature, inspect its behavior, gather evidence about its dependencies, and use an LLM to implement a similar feature.

Depending on the tools and access available, an agent might:

The important distinction is that the agent reconstructs what it can observe and infers what it cannot.

That second part introduces the biggest source of error.

A typical AI reverse engineering workflow has five stages.

The developer provides a URL, a specific feature, or a workflow to investigate.

For example, imagine you want to understand how an analytics dashboard calculates traffic sources. You could ask an agent to inspect the relevant screens, identify the filters, and document what changes when you select a different date range.

The more specific the target behavior, the easier it is to verify the result.

The agent explores the application and observes how it responds to user interactions.

Browser developer tools, network inspection, source maps when available, and browser automation can help reveal what happens behind the interface.

Suppose you select a date range and the dashboard updates its traffic chart. Network inspection might reveal a request containing the selected dates, dimensions, and metrics.

That provides evidence about the request structure. It does not automatically reveal the complete calculation performed by the server.

The agent investigates requests, responses, and other accessible references to understand how the feature obtains its data.

Tools that connect coding agents to command-line utilities or browser inspection workflows can help collect this evidence. MCP, the Model Context Protocol, can provide a standardized way for an AI application to interact with external tools when an appropriate MCP server is available.

However, MCP itself does not magically expose private APIs or hidden source code. The agent can inspect only what its tools, credentials, permissions, and environment allow it to access.

The LLM uses the collected evidence to build a model of how the feature works.

It might implement the frontend, create a compatible API, reproduce visible calculations, or integrate an accessible data source.

When evidence is missing, the model may infer the missing behavior. Those inferences can be reasonable, but they need to be tested rather than treated as facts.

The final stage involves comparing the replica against the original application.

Test the same inputs, date ranges, filters, and user actions. Record differences in the output and investigate their causes.

Without this stage, a replica can look complete while returning incorrect results.

I wanted to see how far AI-powered reverse engineering could go with a complex, established web platform.

Google Analytics was a useful target because its interface exposes a large amount of information, while the underlying data processing is much harder to reproduce.

The experiment involved recreating parts of the product and comparing the output with the original.

Modern coding agents can generate dashboard layouts, charts, navigation, filters, date selectors, and reusable components quickly.

By inspecting the visible interface, a developer can reproduce much of the user experience without knowing the original implementation.

The same applies to basic interactions. A date picker can update a chart, a navigation item can open another view, and a table can support sorting and filtering.

These features are achievable because their expected behavior can be observed and tested.

A convincing interface, however, says very little about the accuracy of the data displayed inside it.

In my experiment, some traffic volume metrics reached approximately 90% accuracy.

That result was encouraging, but it did not mean the replica had achieved 90% overall parity with Google Analytics. The comparison applied to specific metrics in my test, not every report, calculation, or feature.

Other parts of the system depended on backend behavior and data that I could not fully reproduce.

This is where the project became significantly more difficult. Even when the interface accepted the same filters and displayed similar charts, the underlying results could differ.

The reason is straightforward: a dashboard is the final output of a much larger system.

Its numbers depend on how events are collected, processed, attributed, filtered, aggregated, and reported. Reproducing the presentation layer does not reproduce that entire pipeline.

A browser exposes only a limited view of an application.

You can observe requests and responses, but the server-side implementation may remain inaccessible. Private databases, internal services, processing pipelines, and business rules are outside the agent’s direct view.

An LLM can propose a plausible implementation based on available evidence. It cannot reliably reconstruct code or logic that it has never observed.

Consider an analytics platform that reports 100,000 sessions for a website.

A replica might display the same number using a mock dataset, a public API, or a simplified calculation. The chart could look identical, but the number would not necessarily have the same meaning or provenance.

A useful analytics product needs consistent definitions, reliable collection, and correct aggregation.

For example, sessions and users are different metrics. Attribution rules can affect how traffic is classified, while reporting filters can change which events contribute to a result.

If the replica uses different rules, its output will diverge even when the calculations themselves are implemented correctly.

Suppose you observe a metric for several date ranges and try to infer its formula.

You might find a formula that matches all your test cases. Yet another dataset could expose an exception, a filtering rule, or an edge case you never observed.

This is a familiar problem in software testing: a collection of passing examples does not prove that an implementation matches the original system.

AI agents can make this problem more pronounced when they fill gaps with assumptions and then build further functionality on top of those assumptions.

API tracing can reveal useful information, but access does not guarantee unrestricted usage.

An endpoint might require authentication, enforce rate limits, return only a subset of available data, or change without notice. Some requests may depend on internal services that are unavailable to external developers.

A replica built around an accessible API inherits those limitations.

There is also a legal and security boundary here. Inspecting software you own or are authorized to test is different from bypassing access controls, extracting private data, or copying protected implementation details.

AI-powered reverse engineering is useful when the objective matches the evidence available.

Reverse engineering can be an effective learning exercise. Developers can investigate how a feature behaves, identify its dependencies, and build a simplified version to understand the design decisions involved.

The goal should be to develop an informed model of the system, not to assume that every internal decision has been discovered.

Suppose you want to build a keyword research tool. Studying publicly accessible search interfaces and documented APIs can help you understand the expected workflow.

An AI agent can then build a prototype with search forms, result tables, filters, and a backend that uses an authorized data source.

You can validate the product idea without first recreating the entire infrastructure of an established SEO platform.

Developers can also use AI agents to document requests, map dependencies, compare API responses, and identify which parts of an application rely on external services.

This is useful for debugging and technical learning, provided the investigation stays within authorized access.

A complete replica of a mature commercial product is a different challenge.

Products such as Google Analytics or Semrush can depend on extensive datasets, specialized processing systems, accumulated domain knowledge, and years of engineering work.

An external observer may reproduce the visible workflows without reproducing the systems that generate the results.

The same limitation applies to accuracy. In my experiment, some metrics differed from the original, and the observed traffic volume accuracy of around 90% did not generalize to every feature.

The 10% to 15% discrepancy sometimes encountered in such experiments should not be treated as a universal benchmark. The actual error depends on the metric, data source, test conditions, and implementation.

For business-critical decisions, approximate results need independent validation against a trusted source.

AI coding agents can generate code quickly, but reverse engineering involves repeated investigation.

The agent inspects a feature, proposes an implementation, encounters a mismatch, gathers more evidence, and revises its assumptions. Each iteration can consume additional tokens and developer time.

The hardest problems often require manual debugging because the agent cannot distinguish between a faulty implementation and an incorrect assumption without better evidence.

Before starting a project, define a small scope and a measurable acceptance criterion. For example, reproduce one report using the same dataset and filters, then compare its output against the original.

This gives you a way to measure progress before investing in a broad replica.

If I were approaching a similar project again, I would break it into smaller experiments.

This process helps prevent a common failure mode: building a large amount of code around an assumption that turns out to be wrong.

Can we reverse engineer any app with AI?

We can reverse engineer parts of many applications, provided we have enough observable behavior and authorized access. AI agents can make the work faster, especially when inspecting interfaces, tracing accessible APIs, and implementing prototypes.

My attempt to replicate Google Analytics showed the gap between reproducing a product’s interface and reproducing the system behind its numbers. Getting some traffic metrics close to the original was possible, but the remaining backend dependencies made complete parity much harder.

For developers, AI-powered reverse engineering is worth exploring as a way to learn unfamiliar systems, investigate behavior, and test product ideas. Just be careful about treating a convincing replica as a faithful reproduction.

The quality of a replica depends on the evidence behind it. AI can help you write the implementation, but it cannot manufacture access to data or internal logic you cannot observe.

I hope you found it helpful and informative. If you have any questions or feedback, feel free to leave a comment below. Your support and engagement mean a lot to me.