Building an AI Coding Agent That Can Explain Its Code Changes

AI coding agents can now do more than generate code from natural-language instructions. They can inspect repositories, modify files, run tests, analyze errors, and prepare changes for review.

However, generating code is only one part of software development.

When an AI agent changes an existing application, developers need to understand what was modified, why the change was necessary, and whether the modification introduces unexpected behavior.

Consider a developer asking an AI agent to fix a payment validation issue. The agent might update a validation method, modify a service class, add a test, and change an API response.

The code may compile successfully, but the developer still needs answers to several questions:

What caused the original issue?
Which files were modified?
Why were these particular changes necessary?
Does the new implementation preserve existing behavior?
Which tests verify the fix?
Are there any risks that require additional review?

An AI coding agent should be able to explain these decisions using evidence from the repository rather than producing a generic summary.

In this article, we will build the architecture of an AI coding agent that can inspect code changes, understand their context, explain implementation decisions, and generate a useful review summary.

What Is an AI Coding Agent?

An AI coding agent is a software system that uses a language model to perform development tasks through controlled interactions with tools.

Unlike a basic code-generation assistant, an agent can follow a multi-step workflow.

For example, when asked to fix a bug, it may:

Inspect the repository.
Identify the relevant source files.
Understand the existing implementation.
Determine the likely cause of the issue.
Modify the required files.
Execute relevant tests.
Inspect the resulting changes.
Explain the implementation.

The agent does not need to perform every step independently. It can use tools such as file readers, terminal commands, test runners, and version-control operations.

The important distinction is that the language model makes decisions, while the tools provide access to actual development resources.

Why Code Change Explanations Matter

A code change without an explanation creates additional review work.

Developers must manually compare the previous and updated implementations, trace dependencies, and understand the reasoning behind each modification.

This becomes more difficult when an agent changes multiple files.

Consider the following example.

Original implementation:

public decimal CalculateDiscount(
    decimal price,
    decimal discountPercentage)
{
    return price * discountPercentage / 100;
}

Updated implementation:

public decimal CalculateDiscount(
    decimal price,
    decimal discountPercentage)
{
    if (price < 0)
    {
        throw new ArgumentOutOfRangeException(
            nameof(price));
    }

    if (discountPercentage < 0 ||
        discountPercentage > 100)
    {
        throw new ArgumentOutOfRangeException(
            nameof(discountPercentage));
    }

    return price * discountPercentage / 100;
}

A simple code comparison shows that validation was added.

However, a useful explanation should provide more context:

The original method accepted negative prices.
Discount percentages outside the expected range were not rejected.
Invalid input could produce incorrect discount calculations.
The updated implementation validates both arguments before performing the calculation.
Existing callers should be checked to confirm that they do not intentionally pass values outside these ranges.

The explanation helps the reviewer understand the behavior change rather than simply describing the new lines of code.

Designing the Agent Architecture

A reliable coding agent needs several components working together.

<text weight="medium">1. Repository inspection</text>

The agent reads source files, project configuration, tests, and relevant documentation.

<text weight="medium">2. Task analysis</text>

The model identifies the problem, determines which parts of the application are relevant, and creates an implementation plan.

<text weight="medium">3. Tool execution</text>

The agent uses approved tools to inspect files, execute commands, and modify code.

<text weight="medium">4. Change analysis</text>

The agent compares the original and modified versions using Git diff or another version-control mechanism.

<text weight="medium">5. Validation</text>

The agent executes relevant tests, checks build results, and collects validation evidence.

<text weight="medium">6. Explanation generation</text>

The model creates a structured explanation based on the actual changes and validation results.

The overall workflow looks like this:

Developer Request
       |
       v
Repository Inspection
       |
       v
Task Analysis
       |
       v
Implementation Plan
       |
       v
Code Modification
       |
       v
Git Diff Analysis
       |
       v
Build and Test Execution
       |
       v
Evidence Collection
       |
       v
Change Explanation
       |
       v
Developer Review

The explanation should be generated after the implementation and validation stages. Otherwise, the agent may describe changes that were planned but never actually completed.

Step 1: Give the Agent Controlled Repository Access

The agent needs access to the source code before it can explain changes accurately.

A repository inspection tool can provide operations such as:

List files.
Read file contents.
Search for symbols.
Inspect project dependencies.
Read test files.
Retrieve Git status.
Retrieve Git diffs.

For example, a Python-based tool can read a source file:

from pathlib import Path

def read_file(file_path):
    path = Path(file_path)

    if not path.is_file():
        raise FileNotFoundError(file_path)

    return path.read_text(encoding="utf-8")

This example provides basic file access.

A production implementation should also restrict access to the intended repository directory.

Do not allow an AI agent to read arbitrary system files simply because it can execute a file-reading tool.

Inspect the Repository Structure

Before modifying code, the agent should identify the relevant project files.

For example:

PaymentApplication/
|
+-- Controllers/
|   +-- PaymentController.cs
|
+-- Services/
|   +-- PaymentService.cs
|
+-- Models/
|   +-- PaymentRequest.cs
|
+-- Tests/
    +-- PaymentServiceTests.cs

If the task concerns payment validation, the agent should inspect the controller, service, request model, and related tests.

Reading only one file can lead to incorrect assumptions about how the application behaves.

Step 2: Create a Structured Implementation Plan

The agent should convert the developer's request into a plan before modifying files.

For example, consider this request:

Fix the payment validation logic so that
negative amounts and invalid currencies
are rejected before processing.

The agent can generate a structured plan:

{
  "objective": "Improve payment validation",
  "files_to_inspect": [
    "Models/PaymentRequest.cs",
    "Services/PaymentService.cs",
    "Tests/PaymentServiceTests.cs"
  ],
  "expected_changes": [
    "Reject negative payment amounts",
    "Validate supported currency values",
    "Add validation tests"
  ],
  "validation": [
    "Run payment service tests",
    "Build the application"
  ]
}

A structured plan makes the agent's intended actions easier to inspect.

It also provides a reference for the final explanation.

The agent should not automatically assume that every planned change was completed. It must compare the plan with the actual Git diff after implementation.

Step 3: Use Git Diff to Identify Actual Changes

Git diff is one of the most important inputs for a code-explanation agent.

It shows what changed between two versions of a repository.

For example:

git diff --unified=5

The --unified=5 option includes five lines of surrounding context around each change.

For a focused summary of changed files, use:

git diff --stat

To inspect changes that have already been staged:

git diff --cached

A coding agent should understand the difference between:

Unstaged changes.
Staged changes.
Newly created files.
Deleted files.
Renamed files.
Changes that existed before the agent started working.

This last point is particularly important.

If a developer already had uncommitted changes, the agent must not claim those changes as its own.

A safer workflow records the repository state before the agent begins and compares it with the state after implementation.

Capture the Diff

A simple Python helper can retrieve the current working-tree diff:

import subprocess

def get_git_diff(repository_path):
    result = subprocess.run(
        ["git", "diff", "--no-ext-diff"],
        cwd=repository_path,
        capture_output=True,
        text=True,
        check=True
    )

    return result.stdout

This example retrieves unstaged tracked-file changes.

A complete implementation should also inspect staged changes and untracked files. Newly created files do not appear in an ordinary git diff until they are staged or inspected separately.

Never rely on a single diff command to describe every repository change.

Step 4: Analyze Changes With the AI Model

Once the diff is collected, the agent can send it to a language model for analysis.

However, the prompt should request a technical explanation rather than a generic summary.

A useful analysis instruction might look like this:

You are reviewing changes made by an AI coding agent.

Analyze the supplied Git diff and explain:

1. What changed in each file.
2. Why the change was necessary.
3. What application behavior is affected.
4. Whether existing behavior may be impacted.
5. Which tests were added or modified.
6. What risks require developer attention.

Use only evidence available in the repository,
implementation plan, and test results.

Do not invent reasons for changes.
If the purpose of a change is unclear, state that
the available evidence is insufficient.

The agent should also receive relevant original source code and implementation context when a diff alone is insufficient.

For example, changing a method signature may affect multiple callers that are not visible in the modified file.

Repository search can help identify those dependencies.

Separate Facts From Inferences

The agent should distinguish between verified facts and possible implications.

For example:

Statement	Evidence type
Added validation for negative payment amounts	Directly visible in the diff
Added a test for negative payment amounts	Test file change
Invalid requests are rejected before payment processing	Requires inspection of execution flow
All payment workflows are now secure	Unsupported without broader validation

This distinction prevents explanations from overstating what the code changes actually prove.

Step 5: Validate the Changes Before Explaining Them

A successful code modification does not automatically mean the implementation is correct.

The agent should execute appropriate validation commands.

For a .NET application:

dotnet build

Run the relevant test project:

dotnet test Tests/PaymentApplication.Tests.csproj

If the repository contains multiple test projects, the agent should identify which ones are relevant.

Collect the actual command results:

Build success or failure.
Number of tests passed.
Number of tests failed.
Skipped tests.
Compiler warnings.
Runtime errors.

The agent must not claim that tests passed unless it actually executed them and received successful results.

If a test could not run because a dependency was unavailable, the explanation should clearly state that limitation.

For additional confidence, consider static analysis, formatting checks, security scanning, and integration tests.

The validation process should match the risk and scope of the changes.

Step 6: Generate a Structured Change Explanation

After collecting the diff and validation results, the agent can create a structured explanation.

For example:

{
  "summary": "Improved payment request validation",
  "files_changed": [
    {
      "file": "Services/PaymentService.cs",
      "change": "Added amount and currency validation",
      "reason": "Prevent invalid payment requests",
      "behavior_impact": "Invalid requests are rejected before processing"
    },
    {
      "file": "Tests/PaymentServiceTests.cs",
      "change": "Added validation test cases",
      "reason": "Verify invalid input handling"
    }
  ],
  "validation": {
    "build": "passed",
    "tests": "passed"
  },
  "risks": [
    "Existing callers must provide supported currency values"
  ]
}

This structure is easier to display in a developer dashboard, pull request, or review interface.

It also makes it possible to generate different explanations for different audiences.

A developer may want implementation details, while a project manager may need a short description of the behavior change.

Step 7: Explain the Impact on Existing Code

A useful explanation should go beyond the files directly modified by the agent.

For example, suppose the agent changes a method from:

public void ProcessPayment(decimal amount)

to:

public void ProcessPayment(
    decimal amount,
    string currency)

The direct modification is easy to explain.

The more important question is whether existing callers still compile and behave correctly.

The agent should search for references to the changed method.

A repository search might use:

grep -R "ProcessPayment(" .

For larger repositories, use an appropriate code-search tool or language-aware symbol analysis.

The agent should identify:

Updated method declarations.
Updated call sites.
Unchanged callers.
Related interfaces.
Dependency injection registrations.
Test references.

This helps identify incomplete changes that might not be obvious from the modified file alone.

Designing the Agent's Tool Permissions

An AI coding agent should not have unrestricted access to the development environment.

Use separate permissions for different operations.

Tool	Suggested permission
Read source files	Allowed within the repository
Search repository	Allowed within the repository
Inspect Git status and diff	Allowed
Run tests	Allowed through approved commands
Run build commands	Allowed through approved commands
Modify source files	Controlled by task permissions
Delete files	Restricted
Install dependencies	Approval or policy controlled
Push Git changes	Explicit approval
Merge pull requests	Explicit approval

A practical design separates analysis tools from modification tools.

For example, the agent may inspect files and run tests automatically but require developer approval before deleting files or pushing changes to a shared branch.

Use an isolated working directory or development container when possible.

This reduces the impact of accidental commands and unexpected agent behavior.

Common Mistakes When Building a Code Explanation Agent

Mistake	Why it causes problems	Better approach
Explaining code from the final file only	Original behavior is missing	Compare the original and updated versions
Ignoring uncommitted developer changes	The agent may claim unrelated changes	Record the initial Git state
Sending an entire repository to the model	Increases context usage and irrelevant information	Retrieve relevant files and diffs
Trusting the agent's implementation plan	Planned changes may not match actual changes	Compare the plan with the final diff
Claiming tests passed without execution	Creates false confidence	Use actual test command results
Ignoring downstream dependencies	Existing callers may break	Search for affected symbols and references
Producing generic explanations	Reviewers cannot understand the technical impact	Explain each meaningful change with evidence
Allowing unrestricted terminal access	Commands can damage the repository or environment	Use controlled tools and permissions

Advantages and Disadvantages

Advantages

Makes AI-generated changes easier to review.
Reduces the effort required to understand large diffs.
Helps identify behavior changes and possible regression risks.
Connects implementation decisions with test evidence.
Produces consistent pull request explanations.
Helps developers investigate unfamiliar repositories.
Creates a useful audit trail of agent activity.

Disadvantages

Explanation quality depends on repository context.
Large diffs may exceed model context limits.
Static analysis and tests cannot prove the absence of every defect.
The model may misunderstand complex business logic.
Tool execution requires permission management and isolation.
Generated explanations still require human verification.

Summary

An AI coding agent should not only generate code but also explain what it changed and why.

Building this capability requires controlled repository access, task planning, Git diff analysis, automated validation, and evidence-based explanation generation.

The most important design principle is to generate explanations from actual repository changes and execution results rather than relying only on the model's recollection of its intended actions.

Combine structured change reports with test results, dependency analysis, and developer review.

This makes AI-generated modifications easier to understand, validate, and maintain while keeping developers responsible for the final engineering decision.