Agentic RAG for Augmented Software Engineering

Marjan Moghaddam

In the relentless pursuit of faster development cycles and higher code quality, software engineering teams are constantly seeking innovative solutions. Microservices are powerful, but they bring complexity. Microservices, while offering benefits, introduce challenges:

• Increased Complexity: Managing a distributed system with multiple services requires a deep understanding of interdependencies and communication patterns.
• Code Duplication: Maintaining consistency across diverse codebases can lead to duplication and increased maintenance overhead.
• Rapid Iteration: The need for frequent deployments and updates demands efficient code generation and testing processes.
• Knowledge Silos: Information about code, design, and architecture can be scattered across various repositories and documentation, making it difficult to access and utilize effectively.
• Quality Assurance: Ensuring code quality across a large number of microservices requires robust automated testing and review processes.

Imagine a world where code generation, review, and testing are not just faster, but also smarter. This is the promise of Agentic RAG. By seamlessly integrating LLMs with your existing codebase and knowledge repositories, Agentic RAG empowers developers to automate tedious tasks, gain deeper insights into their code, and ultimately, build better software, faster. Agentic RAG addresses these challenges by empowering developers with intelligent tools that:

• Generate Code: Automate the creation of boilerplate code, unit tests, and even entire microservices based on natural language instructions.
• Review Code: Provide automated code reviews, identify potential bugs and vulnerabilities, and suggest improvements based on best practices and organizational standards.
• Test Code: Generate comprehensive unit tests and integration tests, ensuring code quality and reducing the risk of regressions.
• Retrieve and Utilize Knowledge: Seamlessly access and utilize information from code repositories, documentation, and issue trackers, providing developers with the context they need to make informed decisions.

Overview of LangChain, LangGraph, Cursor MCP, and their application:

• LangChain: A powerful framework for developing applications powered by language models. In our context, we will use it to build agents that can interact with java and python code.
• LangGraph: Extends LangChain with the ability to create stateful, multi-actor applications. This is crucial for building complex workflows that involve multiple steps and interactions, such as code generation and review processes.
• Cursor MCP: A tool that integrates language models into your IDE, allowing for seamless interaction with code. We’ll explore how to develop custom MCP plugins that enable agentic code generation, review, and testing directly within your development environment.
• SonarCloud API Integration: We will show how to use direct API calls or SDKs to retrieve quality gate information. This is critical for ensuring that generated code, and code being reviewed, meets the minimum quality gates.
• Tree-sitter and ASTs: We will show how to use tree-sitter and ASTs to parse and analyze code, allowing the agents to understand the structure and semantics of your code.
• JUnit, Mockito, pytest, and unittest: We will show how to use these testing frameworks in conjunction with the agents to generate and execute unit tests.

By combining these technologies, we can create a powerful Agentic RAG system that transforms the way we develop software, enabling us to build better, faster, and more efficiently. Let’s give it a shot.

Building the Agentic Core

This section is about setting up the foundation for your agents. We’ll explore how to leverage LangChain and LangGraph to create agents that not only understand code but also generate and review it effectively.

To effectively tailor LangChain prompts for code analysis, it’s essential to consider the specific structures of Java and Python. For instance, you might prompt the model to “Analyze the following Java class and identify potential performance bottlenecks: [Java code]. Consider the context of the Spring Boot application,” or similarly, “Review the following Python function for potential security vulnerabilities: [Python code]. Consider the context of the Django web application.” Furthermore, integrating Tree-sitter to parse code into Abstract Syntax Trees (ASTs) allows for deeper semantic understanding. By embedding AST information into prompts, you can ask questions like, “Based on the Abstract Syntax Tree, explain the control flow of this python function: [AST data],” enabling more precise and context-aware code analysis.

import os
from tree_sitter import Language, Parser

try:
    # Build the language library (portable path)
    build_path = os.path.join('build', 'my-languages.so')
    Language.build_library(
        build_path,
        ['tree-sitter-python']
    )

    # Load the Python language
    PYTHON_LANGUAGE = Language(build_path, 'python')

    # Create a parser and set the language
    parser = Parser()
    parser.set_language(PYTHON_LANGUAGE)

    # Example Python code with control flow
    source_code = """
    def example_function(x):
        if x > 10:
            return "large"
        else:
            return "small"
    """

    # Parse the code
    tree = parser.parse(bytes(source_code, "utf8"))

    # Print the AST
    print(tree.root_node.sexp())

except Exception as e:
    print(f"An error occurred: {e}")

LangGraph allows us to create stateful, multi-actor applications, which is essential for complex code-related tasks.

Code Generation Workflow:

• Define nodes for code generation, code validation, and error handling.
• Use LangChain agents to generate code snippets based on natural language instructions.
• Integrate unit testing frameworks (JUnit, pytest) into the workflow to ensure code quality.

Code Review Workflow:

• Create nodes for code analysis, review comments generation, and feedback integration.
• Leverage LangChain agents to identify potential issues, suggest improvements, and generate review summaries.
• Incorporate SonarCloud API integration to check quality gates.

Example workflow description.

• Node 1: Recieve code, and send to node 2.
• Node 2: Send code to LLM with a prompt asking for code review. Send the result to node 3.
• Node 3: Send code and LLM review to SonarCloud API and check quality gates. Send the result to node 4.
• Node 4: Return result to user.

from langgraph.graph import StateGraph, END
#import needed langchain libraries.
#import needed SonarCloud libraries.

class CodeGenerationState:
    def __init__(self, code=None, review=None, tests=None, quality=None):
        self.code = code
        self.review = review
        self.tests = tests
        self.quality = quality

def generate_code(state: CodeGenerationState):
    # Use LangChain to generate code
    generated_code = "def example(): return 1" #Replace with actual langchain code.
    state.code = generated_code
    return {"generated_code": generated_code}

def review_code(state: CodeGenerationState):
    # Use LangChain to review code
    review_comments = "Looks good!" #Replace with actual langchain code.
    state.review = review_comments
    return {"review_comments": review_comments}

def run_tests(state: CodeGenerationState):
    # Run unit tests
    test_results = "Tests passed" #Replace with actual testing code.
    state.tests = test_results
    return {"test_results": test_results}

def check_quality_gates(state: CodeGenerationState):
    #use SonarCloud API
    quality_gate_results = "Quality gates passed" #Replace with actual SonarCloud API code.
    state.quality = quality_gate_results
    return {"quality_gate_results": quality_gate_results}

graph = StateGraph(CodeGenerationState) #entry point.
graph.add_node("generate", generate_code)
graph.add_node("review", review_code)
graph.add_node("test", run_tests)
graph.add_node("quality", check_quality_gates)

graph.add_edge("generate", "review")
graph.add_edge("review", "test")
graph.add_edge("test", "quality")
graph.add_edge("quality", END)

chain = graph.compile()

initial_state = CodeGenerationState()
result = chain.invoke(initial_state)

print(result)

RAG for Microservices

This section focuses on how to effectively ingest, index, and retrieve information from your code repositories, documentation, and design documents, ensuring your Agentic RAG system has the context it needs to perform code-related tasks.

Ingesting and Indexing Code Repositories, Documentation, and Design Documents

The first step is to gather and prepare your data for efficient retrieval.

Code Repositories:

• Utilize tools like Git to clone repositories and extract code files.
• Employ Tree-sitter or similar parsers to extract code structures and metadata.
• Consider using code embedding models to capture semantic information.

Documentation and Design Documents:

• Extract text from various document formats (e.g., Markdown, PDFs, Word documents).
• Use LLMs techniques to extract key concepts and relationships.
• Consider using tools such as Unstructured.io to ingest and extract data from various formats.

Indexing:

• Choose a suitable vector database (Pinecone, Weaviate, Chroma) to store code embeddings and document representations.
• Implement a robust indexing strategy to ensure efficient retrieval of relevant information.
• Example python code using ChromaDB.

import chromadb
from chromadb.utils import embedding_functions

try:
    # Initialize ChromaDB client with persistent storage
    client = chromadb.PersistentClient(path="./chroma_db") #Create a directory called chroma_db

    # Define embedding function
    embedding_function = embedding_functions.SentenceTransformerEmbeddingFunction()

    # Create a collection
    collection = client.create_collection(name="code_knowledge", embedding_function=embedding_function)

    # Add documents with metadata
    documents = [
        "def example_function(x): # This function adds 1 to x",
        "Design document for API endpoint: /users",
        "Documentation for Spring Boot microservice: User service",
    ]
    metadata = [
        {"type": "function", "language": "python"},
        {"type": "api", "endpoint": "/users"},
        {"type": "microservice", "framework": "spring boot"},
    ]
    ids = ["doc1", "doc2", "doc3"]
    collection.add(documents=documents, metadatas=metadata, ids=ids)

    # Query the collection
    results = collection.query(query_texts=["performance optimization of user service"], n_results=2, where={"type": "microservice"})
    print(results)

except Exception as e:
    print(f"An error occurred: {e}")

2. Semantic Search with Vector Databases (Pinecone, Weaviate) for Code Understanding

Vector databases enable efficient semantic search, allowing your Agentic RAG system to retrieve relevant information based on meaning rather than keywords.

Generate vector embeddings for code snippets, documentation, and design documents using models like Sentence Transformers or OpenAI embeddings. Store these embeddings in your chosen vector database.

When a user queries the system, generate a vector embedding for the query. Perform a similarity search in the vector database to retrieve the most relevant documents.

import pinecone
from sentence_transformers import SentenceTransformer

try:
    # Initialize Pinecone
    pinecone.init(api_key="YOUR_API_KEY", environment="YOUR_ENVIRONMENT")

    index_name = "code-index"
    if index_name not in pinecone.list_indexes():
        pinecone.create_index(index_name, dimension=768, metric="cosine") #dimension number is based on all-mpnet-base-v2

    index = pinecone.Index(index_name)

    # Initialize embedding model
    model = SentenceTransformer('sentence-transformers/all-mpnet-base-v2')

    query = "how to handle api errors"
    query_vector = model.encode(query).tolist()

    results = index.query([query_vector], top_k=5)
    print(results)

except Exception as e:
    print(f"An error occurred: {e}")

3. Filtering Code Context Based on SonarCloud Quality Gates

To ensure that your Agentic RAG system provides high-quality code and recommendations, you need to filter the code context based on SonarCloud quality gates.

import requests
import json

try:
    sonarcloud_url = "YOUR_SONARCLOUD_URL" #example : "https://sonarcloud.io"
    project_key = "YOUR_PROJECT_KEY"
    api_token = "YOUR_API_TOKEN"

    headers = {"Authorization": f"Bearer {api_token}"}
    url = f"{sonarcloud_url}/api/qualitygates/project_status?projectKey={project_key}"

    response = requests.get(url, headers=headers)
    response.raise_for_status() # Raise HTTPError for bad responses (4xx or 5xx)

    data = response.json()
    quality_gate_status = data["projectStatus"]["status"]
    print(f"Quality Gate Status: {quality_gate_status}")

except requests.exceptions.RequestException as e:
    print(f"Network error: {e}")
except json.JSONDecodeError as e:
    print(f"Invalid JSON response: {e}")
except KeyError as e:
    print(f"Missing key in JSON response: {e}")
except Exception as e:
    print(f"An unexpected error occurred: {e}")

Cursor MCP

This section explores how to leverage Cursor MCP to bridge the gap between your Agentic RAG system and your development environment, enabling seamless code manipulation, generation, review, and testing directly within the IDE.

1. Leveraging Cursor MCP for IDE Integration and Code Manipulation

Cursor MCP serves as a vital conduit, seamlessly integrating the power of your Agentic RAG system into the daily development workflow. By granting access to the IDE’s contextual information, such as the current file, cursor placement, and project structure, Cursor MCP empowers the Agentic RAG system to grasp the developer’s immediate needs, delivering highly relevant code suggestions and manipulations. Furthermore, it enables agents to directly insert, modify, and delete code within the IDE, automating tasks like code generation and refactoring. This integration facilitates real-time feedback, providing developers with instant insights and suggestions.

For example, a developer could select a piece of code, request the agent to generate unit tests, and through Cursor MCP, the agent would send the code to the LLM, receive the generated unit tests, and then insert them directly into the appropriate file within the IDE.

from langchain.llms import OpenAI
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain

# Initialize LangChain
llm = OpenAI(temperature=0.7)
prompt = PromptTemplate(
    input_variables=["code_context", "instruction"],
    template="Given the following code: {code_context}, generate code for: {instruction}",
)
chain = LLMChain(llm=llm, prompt=prompt)

def generate_code_plugin(selection, instruction, editor):
    """
    Plugin to generate code based on user selection and instruction.
    """
    try:
        if not selection or not instruction:
            raise ValueError("Selection and instruction are required.")

        code_context = selection.text
        generated_code = chain.run(code_context=code_context, instruction=instruction)

        # Use editor api to insert generated code at the cursor position
        editor.insert_text(generated_code, selection.start)

    except Exception as e:
        print(f"An error occurred: {e}")
        #handle error in the editor.

# Example usage within an editor:
# editor.register_command("generate_unit_tests", lambda editor: generate_code_plugin(editor.get_selection(), "generate unit tests", editor))

2. Developing Custom MCP Plugins for Code Generation and Unit Testing

Custom MCP plugins allow you to tailor the functionality of your Agentic RAG system to your specific needs.

Code Generation Plugins:

• Develop plugins that enable agents to generate boilerplate code, unit tests, and even entire microservices based on natural language instructions.
• Integrate with your chosen code generation tools and frameworks (e.g., JUnit, pytest).

Unit Testing Plugins:

• Create plugins that allow agents to generate and execute unit tests directly within the IDE.
• Provide developers with real-time feedback on test results and code coverage.

Example Conceptual plugin structure.

• Plugin recieves code and prompt from user.
• Plugin sends code and prompt to Langchain agent.
• Plugin recieves generated code from agent.
• Plugin uses Cursor MCP to insert generated code into the IDE.
• Plugin then can use Cursor MCP to run unit tests.

# Cursor MCP Plugin 

# Assuming a vscode like api.
from langchain.llms import OpenAI
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
import requests
import json

# Initialize LangChain
llm = OpenAI(temperature=0.7)
prompt = PromptTemplate(
    input_variables=["code_context"],
    template="Review the following code and provide feedback: {code_context}",
)
chain = LLMChain(llm=llm, prompt=prompt)

def review_code_plugin(selection, editor):
    """
    Plugin to review selected code and provide feedback.
    """
    try:
        if not selection:
            raise ValueError("Selection is required.")

        code_context = selection.text
        review_comments = chain.run(code_context=code_context)

        # Conceptual: Check SonarCloud quality gates
        sonarcloud_url = "YOUR_SONARCLOUD_URL"
        project_key = "YOUR_PROJECT_KEY"
        api_token = "YOUR_API_TOKEN"

        headers = {"Authorization": f"Bearer {api_token}"}
        url = f"{sonarcloud_url}/api/qualitygates/project_status?projectKey={project_key}"

        try:
            response = requests.get(url, headers=headers)
            response.raise_for_status()
            data = response.json()
            quality_gate_status = data["projectStatus"]["status"]
        except requests.exceptions.RequestException as e:
            quality_gate_status = f"Error fetching quality gate: {e}"
        except json.JSONDecodeError as e:
            quality_gate_status = f"Error parsing JSON: {e}"
        except KeyError as e:
            quality_gate_status = f"Error with Sonarcloud JSON data: {e}"

        # Display review comments and quality gate status in the IDE
        editor.display_panel(f"Review Comments: {review_comments}\nQuality Gate: {quality_gate_status}")

    except Exception as e:
        editor.display_panel(f"An error occurred: {e}")

# Example usage:
# editor.register_command("review_code", lambda editor: review_code_plugin(editor.get_selection(), editor))

3. Automating Code Review Workflows Within the IDE

Cursor MCP can also be used to automate code review workflows, improving code quality and reducing the time spent on manual reviews.

Real-Time Code Analysis:

• Develop plugins that enable agents to analyze code in real-time, identifying potential issues and suggesting improvements.
• Integrate with code analysis tools like SonarCloud to check for quality gate violations.

Automated Review Comments:

• Create plugins that allow agents to generate automated review comments and suggestions directly within the IDE.
• Provide developers with actionable insights and feedback.

Integration with Version Control:

• Integrate with version control systems (e.g., Git) to track code changes and provide context for code reviews.

Example conceptual workflow.

• User commits code.
• A cursor MCP plugin is triggered.
• The plugin sends the changed code to the Langchain Agent.
• The Langchain agent reviews the code, and checks with SonarCloud API.
• The Plugin displays the agents review comments inside of the IDE.

import subprocess
import os

def run_tests_plugin(filepath, editor):
    """
    Plugin to run unit tests within the IDE.
    """
    try:
        if not os.path.exists(filepath):
            raise FileNotFoundError(f"File not found: {filepath}")

        # Check if pytest is available
        subprocess.run(['pytest', '--version'], capture_output=True, check=True)

        result = subprocess.run(['pytest', filepath], capture_output=True, text=True, check=True)
        editor.display_panel(result.stdout)

    except FileNotFoundError as e:
        editor.display_panel(str(e))
    except subprocess.CalledProcessError as e:
        editor.display_panel(e.stderr)
    except FileNotFoundError:
        editor.display_panel("pytest is not installed or not in PATH.")
    except Exception as e:
        editor.display_panel(f"An unexpected error occurred: {e}")

# Example usage:
# editor.register_context_menu_command("Run tests", lambda editor: run_tests_plugin(editor.get_file_path(), editor))

These conceptual snippets provide a starting point for understanding how Cursor MCP and LangChain can be combined to enhance code-aware integration and automation.

Agentic RAG in Action

Here, we’ll showcase how to put the concepts we’ve discussed into practice, with concrete code samples and explanations.

Let’s start by showing how our Agentic RAG system can generate code for both Java Spring Boot and Python microservices.

Imagine a scenario where a developer needs to create a new REST API endpoint. They can use a natural language prompt to instruct the agent.

Prompt: “Generate a Spring Boot REST API endpoint that returns a list of users.”

Conceptual Agent Response:

import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.http.HttpStatus;
import org.springframework.http.ResponseEntity;
import org.springframework.web.bind.annotation.GetMapping;
import org.springframework.web.bind.annotation.RequestMapping;
import org.springframework.web.bind.annotation.RestController;

import java.util.List;

/**
 * REST controller for managing users.
 */
@RestController
@RequestMapping("/users")
public class UserController {

    private final UserService userService;

    /**
     * Constructs a new UserController with the given UserService.
     *
     * @param userService The UserService to use.
     */
    @Autowired
    public UserController(UserService userService) {
        this.userService = userService;
    }

    /**
     * Retrieves a list of all users.
     *
     * @return A ResponseEntity containing the list of users and an HTTP status code.
     */
    @GetMapping
    public ResponseEntity<List<User>> getUsers() {
        try {
            List<User> users = userService.getAllUsers();
            if (users.isEmpty()) {
                return new ResponseEntity<>(HttpStatus.NOT_FOUND);
            }
            return new ResponseEntity<>(users, HttpStatus.OK);
        } catch (Exception e) {
            // Log the exception
            return new ResponseEntity<>(HttpStatus.INTERNAL_SERVER_ERROR);
        }
    }
}

// Example UserService and User class
class UserService {
    public List<User> getAllUsers(){
        //implementation
        return null;
    }
}

class User {
    //user class implementation.
}

Now, let’s see how our system can automate unit test generation.

Prompt: “Generate JUnit tests for the UserService class.”

Conceptual Agent Response:

import org.junit.jupiter.api.Test;
import org.mockito.Mockito;
import static org.junit.jupiter.api.Assertions.*;

class UserServiceTest {

    @Test
    void getAllUsersTest() {
        UserService userService = Mockito.mock(UserService.class);
        Mockito.when(userService.getAllUsers()).thenReturn(List.of(new User("test")));
        List<User> users = userService.getAllUsers();
        assertEquals(1, users.size());
    }
}

The agent generates a JUnit test case using Mockito to mock the dependencies.

Alright, let’s step up the complexity and showcase the Agentic RAG system’s ability to handle more intricate code generation scenarios. We’ll focus on generating a more involved microservice component that incorporates data persistence, API interactions, and some business logic.

Example Scenario: Generating a User Management Service (Python — FastAPI)

Let’s imagine we need to generate a User Management service using FastAPI in Python. This service should:

1. Provide API endpoints for creating, retrieving, and updating user data.
2. Persist user data using a simple in-memory database (for demonstration purposes).
3. Include data validation and error handling.
4. Interact with an external service to validate user email addresses.

Prompt to the Agent:

“Generate a FastAPI service for user management. It should include endpoints for creating, retrieving, and updating user data. Use an in-memory database for persistence. Implement data validation and error handling. Also, integrate with an external email validation API (e.g., ‘emailvalidation.abstractapi.com’) to validate user email addresses.”

from fastapi import FastAPI, HTTPException, Depends
from pydantic import BaseModel, EmailStr
import requests
from typing import Dict

app = FastAPI()

# In-memory database (for demonstration)
users: Dict[int, dict] = {}
user_id_counter = 1

# Data models
class UserCreate(BaseModel):
    name: str
    email: EmailStr

class User(UserCreate):
    id: int

# Email validation function
def validate_email(email: str):
    api_key = "YOUR_EMAIL_VALIDATION_API_KEY"
    response = requests.get(f"https://emailvalidation.abstractapi.com/v1/?api_key={api_key}&email={email}")
    if response.status_code == 200 and response.json().get("deliverability") != "DELIVERABLE":
        raise HTTPException(status_code=400, detail="Invalid email address")

# API endpoints
@app.post("/users/", response_model=User, status_code=201)
def create_user(user: UserCreate):
    validate_email(user.email)
    global user_id_counter
    new_user = user.dict()
    new_user["id"] = user_id_counter
    users[user_id_counter] = new_user
    user_id_counter += 1
    return new_user

@app.get("/users/{user_id}", response_model=User)
def get_user(user_id: int):
    user = users.get(user_id)
    if not user:
        raise HTTPException(status_code=404, detail="User not found")
    return user

@app.put("/users/{user_id}", response_model=User)
def update_user(user_id: int, user: UserCreate):
    validate_email(user.email)
    if user_id not in users:
        raise HTTPException(status_code=404, detail="User not found")
    users[user_id].update(user.dict())
    users[user_id]["id"] = user_id
    return users[user_id]

Automated Code Reviews and Quality Assurance

This section explores how to leverage Agentic RAG to automate code reviews, ensuring high code quality and reducing the burden on human reviewers.

1. Integrating SonarCloud Quality Gates into the Agent’s Review Process

SonarCloud provides valuable insights into code quality, and integrating its quality gates into the agent’s review process ensures that generated and reviewed code adheres to established standards.

Utilize the SonarCloud API to retrieve quality gate information for the code under review. Implement logic to check if the code meets the required quality standards (e.g., no major vulnerabilities, acceptable code coverage).

If the code fails to meet the quality gates, provide detailed feedback to the developer, highlighting the specific issues identified by SonarCloud. Use the agent to suggest potential fixes or improvements based on SonarCloud’s recommendations.

import requests
import json
import os

def check_sonarcloud_quality(project_key, sonarcloud_url=None, api_token=None):
    """
    Checks SonarCloud quality gates for a given project.

    Args:
        project_key (str): The SonarCloud project key.
        sonarcloud_url (str, optional): The SonarCloud URL. Defaults to environment variable SONARCLOUD_URL.
        api_token (str, optional): The SonarCloud API token. Defaults to environment variable SONARCLOUD_API_TOKEN.

    Returns:
        str: The quality gate status ("OK", "ERROR", "WARN"), or an error message.
    """

    sonarcloud_url = sonarcloud_url or os.environ.get("SONARCLOUD_URL")
    api_token = api_token or os.environ.get("SONARCLOUD_API_TOKEN")

    if not sonarcloud_url or not api_token:
        return "Error: SonarCloud URL or API token not provided."

    headers = {"Authorization": f"Bearer {api_token}"}
    url = f"{sonarcloud_url}/api/qualitygates/project_status?projectKey={project_key}"

    try:
        response = requests.get(url, headers=headers)
        response.raise_for_status()  # Raise HTTPError for bad responses (4xx or 5xx)
        data = response.json()
        quality_gate_status = data["projectStatus"]["status"]
        return quality_gate_status

    except requests.exceptions.RequestException as e:
        return f"Error: Network error during SonarCloud check: {e}"
    except json.JSONDecodeError as e:
        return f"Error: Invalid JSON response from SonarCloud: {e}"
    except KeyError as e:
        return f"Error: Missing key in SonarCloud JSON: {e}"
    except Exception as e:
        return f"Error: An unexpected error occurred during SonarCloud check: {e}"

def provide_review_feedback(code, project_key, sonarcloud_url=None, api_token=None):
    """
    Provides code review feedback, integrating SonarCloud quality gates.

    Args:
        code (str): The code to review (not used in this simplified version).
        project_key (str): The SonarCloud project key.
        sonarcloud_url (str, optional): The SonarCloud URL. Defaults to environment variable SONARCLOUD_URL.
        api_token (str, optional): The SonarCloud API token. Defaults to environment variable SONARCLOUD_API_TOKEN.

    Returns:
        str: Feedback message, including SonarCloud quality gate status.
    """

    quality_status = check_sonarcloud_quality(project_key, sonarcloud_url, api_token)

    if quality_status == "OK":
        return "SonarCloud quality gates passed."
    elif quality_status.startswith("Error"): # handle errors.
        return f"SonarCloud quality gate check failed: {quality_status}"
    else:
        return f"SonarCloud quality gates failed. Status: {quality_status}"

2. Implementing Agent-Driven Code Analysis and Feedback

To uplift code analysis beyond superficial checks, we must embrace deep semantic understanding. This involves utilizing Tree-sitter to generate ASTs, enabling LangChain agents to traverse and analyze these structures, identifying intricate patterns and relationships. Moreover, incorporating external context, such as design documents, issue tracker entries, and API specifications, via LangChain, allows for contextual code understanding.

import ast
import langchain

def analyze_ast(code):
    """
    Analyzes Python code AST for potential issues.
    """
    try:
        tree = ast.parse(code)
    except SyntaxError:
        return "Syntax error in code."

    issues = []

    class Visitor(ast.NodeVisitor):
        def visit_BinOp(self, node):
            if isinstance(node.op, ast.Div) and isinstance(node.right, ast.Num) and node.right.n == 0:
                issues.append(f"Potential division by zero at line {node.lineno}.")
            self.generic_visit(node)

        def visit_Name(self, node):
            if node.id == 'eval':
                issues.append(f"Potential security risk: use of 'eval' at line {node.lineno}.")
            self.generic_visit(node)

    visitor = Visitor()
    visitor.visit(tree)

    if issues:
        return "\n".join(issues)
    else:
        return "No issues found."

# Example usage with LangChain
def agent_ast_analysis(code):
  """
  Example of using Langchain to provide additional context.
  """
  result = analyze_ast(code)
  prompt = f"""
  Given the following code analysis: {result}, provide a more detailed explanation of the issues.
  """
  llm = langchain.llms.OpenAI()
  return llm(prompt)

code_snippet = """
def divide(a, b):
    return a / b

x = eval("1+1")
"""

print(agent_ast_analysis(code_snippet))

A prompt like, “Review this code with the following API spec. Does the code follow the specification? If not, what are the differences?” exemplifies this capability.

import langchain

def review_with_api_spec(code, api_spec):
    """
    Reviews code against an API specification.
    """
    prompt = f"""
    Review the following code:\n{code}\n
    Against the following API specification:\n{api_spec}\n
    Identify any discrepancies and provide feedback.
    """
    llm = langchain.llms.OpenAI()
    return llm(prompt)

code_snippet = """
from flask import Flask, jsonify

app = Flask(__name__)

@app.route('/users', methods=['GET'])
def get_users():
    return jsonify({'users': ['user1', 'user2']})
"""

api_spec = """
GET /users: Returns a list of users in JSON format.
"""

print(review_with_api_spec(code_snippet, api_spec))

Furthermore, training agents to recognize common code patterns and anomalies, like anti-patterns, performance bottlenecks, and security vulnerabilities, is crucial. This could involve detecting redundant computations or potential SQL injection vulnerabilities. Finally, enabling interactive feedback and suggestions empowers developers to seek clarifications and alternative solutions. For example, an agent could provide code snippets demonstrating how to refactor a complex function or enhance its performance, fostering a collaborative and iterative development process.

3. Generating Code Review Summaries and Actionable Insights

To elevate code review summaries beyond mere lists of issues, we must transform them into actionable guides. Structured review summaries are essential, categorizing issues based on severity, type, and impact. For instance, issues can be classified as “Critical” for security vulnerabilities, “Major” for performance bottlenecks, “Minor” for style violations, and “Suggestions” for best practice recommendations. This categorization provides developers with a clear understanding of the priorities and potential impact of each issue.

Furthermore, actionable code snippets and examples should be integrated into the summaries, demonstrating practical solutions to address the identified issues. For example, if a performance bottleneck is detected, the agent could provide a code snippet illustrating how to optimize the relevant code section. Contextual links and documentation are also vital, offering developers direct access to relevant coding standards, best practice guides, and security advisories. This ensures that developers have the necessary resources to understand and resolve the issues effectively.

import langchain
from langchain.llms import OpenAI  # Explicitly import OpenAI

def interactive_review(code, question):
    """
    Allows interactive review discussions.

    Args:
        code (str): The code snippet to review.
        question (str): The question to ask about the code.

    Returns:
        str: The LLM's response to the question.
    """
    prompt = f"""
    Given the following code:\n{code}\n
    Answer the following question:\n{question}\n
    """
    llm = OpenAI()  # Initialize OpenAI LLM
    try:
        response = llm(prompt)
        return response
    except Exception as e:
        return f"An error occurred during LLM processing: {e}"

code_snippet = """
def calculate_average(numbers):
    return sum(numbers) / len(numbers)
"""

question = "Is there any potential for an error in this function?"

print(interactive_review(code_snippet, question))

Example of a complex summary.

• “Code review summary. File : user_service.py.
• Critical: SQL injection vulnerability detected on line 55. Recommendation : use parameterized queries. See : [link to documentation].
• Major: Performance bottleneck on line 100. The for loop has O(n²) time complexity. Recommendation : use a hashmap. Estimated performance improvement : 50%. See : [link to performance guide].
• Minor: Style violation on line 120. Code does not follow PEP 8 standard. Recommendation : auto format the code.
• Sonarcloud quality gate : Failed. Blockers : 1. Criticals : 3. Majors : 5.
• Please ask me any questions about this review.”

To foster a collaborative review process, interactive review discussions should be enabled, allowing developers to ask clarifying questions and provide feedback on the agent’s suggestions. An integrated chat interface within the IDE could facilitate this interaction. Finally, including metrics and impact analysis in the review summary quantifies the potential impact of the identified issues, such as estimating performance improvements or security risks. This data-driven approach allows developers to prioritize their efforts and make informed decisions about code improvements.

Real-World Applications and Impact

Let’s translate these benefits into compelling narratives that resonate with the practical realities of Java and Python microservices development. Imagine a world where your developers are freed from the drudgery of boilerplate code generation. With Agentic RAG, Spring Boot controllers, FastAPI routes, and data models spring into existence from API specifications and data schemas, allowing engineers to focus on the core logic and innovation.

# Agent-Driven API Documentation Generation

import langchain
from langchain.llms import OpenAI
from fastapi import FastAPI
from pydantic import BaseModel
import inspect

app = FastAPI()

class Item(BaseModel):
    name: str
    description: str

@app.post("/items/")
def create_item(item: Item):
    """
    Create a new item.
    """
    return item

# Conceptual LangChain Agent for API documentation
def generate_api_docs(app_instance):
    """
    Generates API documentation from a FastAPI app instance.

    Args:
        app_instance (FastAPI): The FastAPI application instance.

    Returns:
        str: The generated API documentation.
    """
    try:
        # Extract relevant information from the FastAPI app instance
        api_info = {
            "routes": [],
            "models": [],
        }

        for route in app_instance.routes:
            api_info["routes"].append({
                "path": route.path,
                "methods": route.methods,
                "summary": route.summary,
                "description": inspect.getdoc(route.endpoint),
                "request_model": route.body_field.type.__name__ if route.body_field else None,
                "response_model": route.response_model.__name__ if route.response_model else None,
            })

        for name, model in app_instance.models.items():
            if issubclass(model, BaseModel):
                api_info["models"].append({
                    "name": name,
                    "fields": model.__fields__,
                })

        prompt = f"""
        Generate API documentation for the following FastAPI application:\n{api_info}\n
        """
        llm = OpenAI()
        docs = llm(prompt)
        return docs
    except Exception as e:
         return f"An error occurred during API documentation generation: {e}"

# Example usage (within an IDE plugin or CI/CD pipeline)
api_docs = generate_api_docs(app)
print(api_docs)

Unit test generation, a crucial but often tedious task, becomes automated, ensuring code quality and minimizing regressions for both Java and Python services. API documentation, always a challenge to keep current, is now dynamically generated, reflecting the latest code changes. Refactoring and code migration, traditionally time-consuming endeavors, are streamlined, enabling faster iterations and smoother technology upgrades. This translates to accelerated development cycles, allowing teams to deliver features faster and respond more rapidly to market demands.

Beyond speed, Agentic RAG fortifies code quality and tackles technical debt head-on. Automated code reviews, powered by deep semantic understanding, pinpoint bugs, security vulnerabilities, and performance bottlenecks early in the development lifecycle. This proactive approach ensures that code adheres to established standards, reducing the risk of costly rework and security breaches.

# Agent-Driven Security Vulnerability Detection

import langchain
import ast

def detect_sql_injection(code):
    """
    Detects potential SQL injection vulnerabilities in Python code.
    """
    try:
        tree = ast.parse(code)
    except SyntaxError:
        return "Syntax error in code."

    vulnerabilities = []

    class Visitor(ast.NodeVisitor):
        def visit_Call(self, node):
            if isinstance(node.func, ast.Attribute) and node.func.attr == "execute":
                if any(isinstance(arg, ast.Str) for arg in node.args):
                    vulnerabilities.append(f"Potential SQL injection at line {node.lineno}.")
            self.generic_visit(node)

    visitor = Visitor()
    visitor.visit(tree)

    if vulnerabilities:
        return "\n".join(vulnerabilities)
    else:
        return "No SQL injection vulnerabilities found."

# Example usage with LangChain
def agent_security_analysis(code):
  """
  Example of using Langchain to provide additional context.
  """
  result = detect_sql_injection(code)
  prompt = f"""
  Given the following code analysis: {result}, provide a more detailed explanation of the issues.
  """
  llm = langchain.llms.OpenAI()
  return llm(prompt)

code_snippet = """
import sqlite3

def get_user(user_id):
    conn = sqlite3.connect('users.db')
    cursor = conn.cursor()
    cursor.execute(f"SELECT * FROM users WHERE id = '{user_id}'")
    return cursor.fetchone()
"""

print(agent_security_analysis(code_snippet))

Identifying and addressing technical debt, such as duplicated code and overly complex functions, becomes a systematic process, leading to more maintainable and robust systems. Furthermore, proactive security analysis, conducted before deployment, minimizes the risk of vulnerabilities reaching production. This emphasis on quality translates to more reliable and secure applications.

Finally, Agentic RAG transforms team collaboration and knowledge sharing. Contextual code suggestions, based on project context and existing code patterns, empower developers to write more efficient and consistent code. Instant access to knowledge repositories, documentation, and issue trackers provides developers with the information they need to make informed decisions, fostering a culture of knowledge sharing and collaboration.

# Agent-Driven Contextual Code Suggestions

import langchain

def get_code_suggestions(code_context, query):
    """
    Provides contextual code suggestions based on the current code.
    """
    prompt = f"""
    Given the following code context:\n{code_context}\n
    Provide code suggestions for the following query:\n{query}\n
    """
    llm = langchain.llms.OpenAI()
    suggestions = llm(prompt)
    return suggestions

code_context = """
from flask import Flask, jsonify

app = Flask(__name__)

@app.route('/users', methods=['GET'])
def get_users():
    # ...
"""

query = "How to add a POST endpoint for creating a new user?"

print(get_code_suggestions(code_context, query))

Automated code documentation and explanations demystify complex code sections, simplifying maintenance and onboarding. By implementing Agentic RAG, teams unlock a new level of productivity, quality, and collaboration, driving significant business value and transforming the software development landscape.

The Future that we all want.

As we look toward the horizon of Augmented Software Engineering, advanced agentic debugging and troubleshooting stand out as a potential revolution. Imagine a development environment where agents actively monitor code execution, identifying anomalies and potential runtime errors in real-time. These agents could move beyond simple error detection to perform automated root cause analysis, tracing complex bugs through intricate codebases, particularly beneficial in microservice architectures where interdependencies can be challenging. Furthermore, predictive debugging emerges as a possibility, where agents leverage machine learning to anticipate potential issues before they manifest, based on code complexity and historical data. Interactive debugging sessions could become collaborative, with agents providing real-time insights and suggestions, essentially functioning as an AI-powered pair programmer.

However, the increasing sophistication of AI-generated code introduces critical security and ethical considerations. We must acknowledge the potential for AI-generated code to introduce novel vulnerabilities, which may be difficult to detect with traditional methods. The complex issue of intellectual property ownership in AI-generated code demands careful consideration, as does the potential for AI models to perpetuate biases found in their training data. Ethical discussions around responsibility and accountability in the context of AI-generated code are also essential, especially in scenarios where AI-generated code contributes to security breaches or system failures. Robust defenses against prompt injection attacks must be implemented.

Beyond Agentic RAG, we can anticipate a broader evolution of AI-powered software engineering tools. AI-driven project management could automate tasks such as scheduling and resource allocation, optimizing the SDLC. AI-powered code optimization could enhance performance and efficiency, while AI-enhanced collaboration tools could provide developers with real-time contextual suggestions. The future may even hold the potential for autonomous agents capable of independently developing and deploying software based on high level specifications. The integration of these tools into development workflows, presents both exciting opportunities and complex challenges. We must strive to ensure that these advancements are implemented responsibly and ethically, maximizing their potential to transform the software development landscape.