- by x32x01 ||
What if you gave an AI agent a program you do not have the source code for and asked it to figure out how it works?
AI agents are usually associated with writing code, running commands, and automating development tasks. But reverse engineering requires a different set of abilities: inspecting an existing program, analyzing binaries, tracing execution, and using evidence from the running system to understand its behavior.
💡 This is where REA - Reverse Engineer Anything comes in.
REA is an open-source project designed to give AI agents practical reverse-engineering capabilities through MCP and CLI interfaces. Instead of only generating new code, an AI agent can use REA to investigate existing software and build an understanding of how it works.
🔗 Project: https://github.com/morluto/rea
It can work from the evidence available from an existing program and use that information as part of its reasoning process.
"Create a function that processes this type of data."
It can generate the implementation based on the requirements.
A reverse-engineering workflow starts with a different question:
"Here is an existing program. What does this part of it actually do?"
That changes the workflow.
The agent may need to:
Instead of expecting the model to perform every operation itself, the agent can interact with external tools and receive their results as context.
This is particularly useful for technical workflows where the answer cannot be determined from the model's existing knowledge alone.
For example, analyzing a native binary may require information that can only be obtained by inspecting the actual file or observing its behavior.
MCP provides a way for the agent to interact with those capabilities as part of its workflow.
AI coding tools are already very good at helping developers create new software. But many real-world engineering problems start with software that already exists.
You may have:
An AI agent equipped with reverse-engineering tools could potentially help bridge that gap.
Instead of asking an AI model to guess the implementation, a reverse-engineering workflow can provide the agent with evidence from the actual application.
The agent can then reason about that evidence and gradually build a more accurate picture of the feature.
The workflow might look like this:
The key difference is the source of the information: the agent can reason from evidence gathered from the target software rather than relying only on assumptions.
Developers may encounter situations where they need to understand software they did not originally build.
REA-style workflows could be useful for:
Instead of thinking about an AI agent only as something that writes software, we can also think of it as something that can investigate software.
That creates a much broader workflow:
Observe → Analyze → Understand → Implement
Rather than starting with an empty project, the agent can start with an existing system and work toward understanding it.
🤖 The bigger question is where this approach will lead.
Will future AI agents simply generate new systems, or will they also become capable of exploring, analyzing, and understanding complex systems that already exist?
Projects such as REA suggest that the second possibility is worth watching.
By connecting an AI agent with reverse-engineering capabilities through MCP and CLI workflows, the project explores a useful idea: AI can potentially help investigate existing software, not just create new software.
For developers, security researchers, and anyone interested in program analysis, this is an area worth following as AI agents become more capable of interacting with real technical environments.
🔗 REA - Reverse Engineer Anything: https://github.com/morluto/rea
AI agents are usually associated with writing code, running commands, and automating development tasks. But reverse engineering requires a different set of abilities: inspecting an existing program, analyzing binaries, tracing execution, and using evidence from the running system to understand its behavior.
💡 This is where REA - Reverse Engineer Anything comes in.
REA is an open-source project designed to give AI agents practical reverse-engineering capabilities through MCP and CLI interfaces. Instead of only generating new code, an AI agent can use REA to investigate existing software and build an understanding of how it works.
🔗 Project: https://github.com/morluto/rea
What Can REA Help With?
REA is designed to support several tasks that are useful when analyzing existing software:- 🔍 Analyzing applications and native binaries
- 🧩 Inspecting and understanding program components
- 🕵️ Tracing execution and examining runtime evidence
- ⚙️ Investigating how specific functions behave
- 🛠️ Helping recreate similar functionality in another project
- 🤖 Giving an AI agent tools for interacting with reverse-engineering workflows
It can work from the evidence available from an existing program and use that information as part of its reasoning process.
How Is This Different From a Typical AI Coding Agent?
A traditional AI coding agent might receive a task such as:"Create a function that processes this type of data."
It can generate the implementation based on the requirements.
A reverse-engineering workflow starts with a different question:
"Here is an existing program. What does this part of it actually do?"
That changes the workflow.
The agent may need to:
- 🔎 Inspect the available binary or application.
- 🧠 Identify relevant functions or components.
- 🧪 Observe how the program behaves during execution.
- 📊 Analyze the evidence collected from the program.
- 🧩 Build a working hypothesis about its behavior.
- 💻 Use that understanding to reproduce or integrate similar functionality when appropriate.
Why MCP Matters
REA uses MCP (Model Context Protocol) to make reverse-engineering capabilities available to an AI agent through tools.Instead of expecting the model to perform every operation itself, the agent can interact with external tools and receive their results as context.
This is particularly useful for technical workflows where the answer cannot be determined from the model's existing knowledge alone.
For example, analyzing a native binary may require information that can only be obtained by inspecting the actual file or observing its behavior.
MCP provides a way for the agent to interact with those capabilities as part of its workflow.
From Code Generation to System Exploration
This is probably the most interesting idea behind projects like REA.AI coding tools are already very good at helping developers create new software. But many real-world engineering problems start with software that already exists.
You may have:
- An application without its original source code
- An old binary that needs to be understood
- A legacy component that must be replaced
- A native application whose behavior is poorly documented
- A system that needs compatibility with another implementation
An AI agent equipped with reverse-engineering tools could potentially help bridge that gap.
A Practical Example
Imagine you have a native application and need to understand how one particular feature works.Instead of asking an AI model to guess the implementation, a reverse-engineering workflow can provide the agent with evidence from the actual application.
The agent can then reason about that evidence and gradually build a more accurate picture of the feature.
The workflow might look like this:
Code:
Existing Application
↓
Reverse-Engineering Tools
↓
Program Analysis + Runtime Evidence
↓
AI Agent
↓
Behavioral Understanding
↓
Possible ReimplementationWhy This Could Be Useful for Developers
Reverse engineering is not limited to security research.Developers may encounter situations where they need to understand software they did not originally build.
REA-style workflows could be useful for:
- 🔧 Legacy software analysis
- 🔄 Reimplementation and compatibility work
- 🧪 Software research
- 🐛 Investigating unexpected program behavior
- 📚 Learning how real applications work internally
- 🔬 Analyzing native software and binaries
The Bigger Idea
REA represents an interesting direction for AI agents.Instead of thinking about an AI agent only as something that writes software, we can also think of it as something that can investigate software.
That creates a much broader workflow:
Observe → Analyze → Understand → Implement
Rather than starting with an empty project, the agent can start with an existing system and work toward understanding it.
🤖 The bigger question is where this approach will lead.
Will future AI agents simply generate new systems, or will they also become capable of exploring, analyzing, and understanding complex systems that already exist?
Projects such as REA suggest that the second possibility is worth watching.
Final Thoughts
REA - Reverse Engineer Anything is an interesting example of how AI agents can move beyond traditional code generation.By connecting an AI agent with reverse-engineering capabilities through MCP and CLI workflows, the project explores a useful idea: AI can potentially help investigate existing software, not just create new software.
For developers, security researchers, and anyone interested in program analysis, this is an area worth following as AI agents become more capable of interacting with real technical environments.
🔗 REA - Reverse Engineer Anything: https://github.com/morluto/rea