DEV Community: CharanTeja Chavithina

From Git Hooks to AI Code Reviews: Building Secure Code with DevSecOps and Agentic AI

CharanTeja Chavithina — Wed, 29 Jul 2026 12:48:46 +0000

Introduction

Software development today is not just about writing code—it's about writing secure, reliable, and production-ready code. As applications become more complex, developers need automated mechanisms to catch security issues before they reach production.

During Week 4 – Assignment 6 of the DevOps Micro Internship (DMI), I explored how Git Pre-Commit Hooks, AI-assisted code reviews, and Agentic AI workflows work together to improve code quality. This assignment demonstrated that combining automation with AI can significantly strengthen the software development lifecycle while keeping humans in control of final decisions.

Why This Assignment Was Important

One of the most common mistakes developers make is accidentally committing:

Hardcoded credentials
API keys
Debug statements
Private keys
Large unnecessary files

Once committed, these artifacts become part of Git history and can pose serious security risks.

The goal of this assignment was to prevent such mistakes before the code is committed, using automation and AI-powered analysis.

Step 1: Starting with a Clean Git Workflow

The journey began by confirming the repository setup and creating a dedicated feature branch.

Instead of working directly on the main branch, a separate branch was created to isolate changes.

This simple practice offers several advantages:

Keeps the main branch stable
Makes collaboration easier
Produces cleaner Pull Requests
Reduces merge conflicts

Feature branching is one of the most widely adopted Git best practices in modern software development.

Step 2: Creating a Risky Script for Testing

To test automated security checks, a shell script named:

scripts/notify.sh

was intentionally created with two common security issues:

A hardcoded AWS-style access key
A debug statement exposing sensitive information

Although the values were placeholders, they closely resembled mistakes that developers accidentally introduce into repositories.

This setup provided a realistic scenario for validating security automation.

Step 3: Protecting the Repository with a Git Pre-Commit Hook

The core of this assignment involved creating a custom Git Pre-Commit Hook.

The hook automatically scanned staged files before every commit.

It performed two important checks:

Secret Detection

It searched for patterns matching:

AWS Access Keys
RSA/OpenSSH Private Keys
File Size Validation

It prevented files larger than 1 MB from being committed.

Whenever either rule was violated, Git immediately rejected the commit.

Instead of allowing risky code into the repository, developers received clear feedback about what needed to be fixed.

This demonstrated how automation can enforce security without requiring manual inspection every time.

Understanding Rule-Based Validation

A Git pre-commit hook follows predefined rules.

For example:

If an AWS key pattern is found → Block the commit.
If a staged file exceeds the allowed size → Reject the commit.
Advantages
Extremely fast
Consistent every time
Easy to automate
Excellent for known security risks
Limitation

It only detects patterns that have been explicitly programmed.

It cannot understand the intent or context behind the code.

Step 4: AI-Powered Review with /pr-ready

After implementing rule-based validation, the assignment introduced an AI-powered review skill called:

/pr-ready

Unlike the Git hook, this AI reviewer analyzed the staged changes much like an experienced code reviewer.

It evaluated:

Debug statements
Documentation gaps
Potential security concerns
Pull Request readiness
Overall code quality

Rather than simply blocking a commit, it explained why certain changes could become problems and even generated a draft Pull Request title and description.

This highlighted how AI can provide contextual insights beyond simple pattern matching.

Rule-Based Automation vs AI Code Review
Git Pre-Commit Hook AI Review (/pr-ready)
Pattern Matching Context Understanding
Fast and Consistent Human-like Reasoning
Detects Known Risks Explains Potential Issues
Blocks Unsafe Commits Suggests Improvements
Fixed Rules Adaptive Analysis

Instead of replacing each other, both approaches complement one another to create a stronger review process.

Step 5: Fixing the Security Issues

Once the warnings were identified, the script was updated by removing:

The hardcoded key
The debug statement

The corrected script became a simple placeholder notification script without exposing sensitive information.

After staging the changes, the commit was attempted again.

This time:

✅ The Git pre-commit hook passed.

✅ The AI review reported no issues.

This demonstrated the importance of responding to automated feedback before moving forward.

Step 6: Creating the Pull Request

The final task involved:

Pushing the feature branch
Creating a Pull Request against the personal fork
Using the AI-generated PR description as a starting point
Reviewing and editing the AI draft before submission

One important lesson became clear:

AI can assist developers, but humans remain responsible for validating and approving every change before it becomes part of the codebase.

Understanding the Agentic AI Workflow

This assignment introduced a simple but powerful Agentic AI Loop:

Gather

Collect repository information using Git commands such as:

git status
git diff

Analyze

Run:

Git Pre-Commit Hook
AI /pr-ready review

Human Action

Developers review findings, fix issues, and improve the code.

Verify

Run the automated checks again to confirm everything passes successfully before committing.

This workflow combines:

Automation
AI reasoning
Human judgment

to build more secure and reliable software.

Key Learnings
Built a custom Git pre-commit hook to detect secrets and prevent unsafe commits.
Learned how rule-based automation differs from AI-powered contextual code reviews.
Gained practical experience with Git feature branching, staging, committing, and Pull Request workflows.
Understood how Agentic AI enhances developer productivity by reviewing code intelligently while keeping humans in control.
Strengthened my understanding of DevSecOps by integrating security checks into the development workflow before code reaches the repository.
Conclusion

This assignment reinforced an important principle of modern software engineering:

Security should begin before the first commit—not after deployment.

Git hooks provide fast, automated protection against common mistakes, while AI-powered reviews offer contextual insights that traditional tools cannot provide alone. Together, they help developers deliver cleaner, safer, and higher-quality code.

As AI continues to become an integral part of software development, combining automation, intelligent analysis, and human decision-making will be key to building reliable and secure applications.

This post is part of the DevOps Micro Internship (DMI) with Agentic AI — Cohort 3 — by Pravin Mishra.
My graded progress is public: https://dmi.pravinmishra.com/s/DMI-C3-CharanTeja.html
Start your DevOps journey: https://dmi.pravinmishra.com/?utm_source=student&utm_medium=ps-blog&utm_campaign=cohort3

Learn DevOps Through a Real Business Story: Building EpicReads from Scratch

CharanTeja Chavithina — Sun, 26 Jul 2026 18:30:31 +0000

To make it truly stand out, I'd create it as a "Learn DevOps Through a Real Business Story" series using EpicReads as the running case study. Each part would be detailed, practical, visually engaging, and written in a style suitable for Medium and LinkedIn.

One limitation I should mention: I can't generate a long Word document with embedded custom illustrations directly in a single chat. While I can generate individual images, assembling a 40+ page illustrated document with dozens of custom graphics isn't something I can produce in one response.

Here's what I can do instead—and it will give you a better result:

Part 1: Building the Foundation

I'll create:

Internet basics
DNS explained with the EpicReads example

Part 1: How the Internet Delivers Every Book Order
Packet Switching
TCP/IP
HTTP vs HTTPS
Firewalls
Load Balancers
CDN

Part 2: Building EpicReads
Two-tier vs Three-tier Architecture
Frontend
Backend APIs
Database
Cache
Scalability

Part 3: Becoming a DevOps Engineer
VS Code
Git
CI/CD
Docker
Kubernetes
Monitoring
Complete DevOps workflow

Week 3 DevOps Journey: AWS, Linux, Nginx & Bash

CharanTeja Chavithina — Thu, 23 Jul 2026 06:28:24 +0000

Week 3 Summary – EpicReads Cloud Onboarding

This week focused on building a strong foundation in cloud infrastructure, Linux administration, web server deployment, and automation through hands-on DevOps assignments.

Assignment 1 – AWS Free Tier Account Setup
Created and configured an AWS Free Tier account.
Learned AWS core services and Free Tier limits.
Understood cloud security best practices and account setup.
Launched and connected to an Ubuntu EC2 instance.
Assignment 2 **– Deploy a React App on Ubuntu VM Using Nginx
Installed and configured Nginx as a web server.
Deployed a React application on an Ubuntu EC2 instance.
Configured port 80 and verified application accessibility.
Learned how web servers host modern frontend applications.
**Assignment 3 – Production Maintenance Drill (Ops Checklist)
Performed production readiness checks.
Validated system health, running services, network connectivity, and server configuration.
Practiced troubleshooting using Linux commands.
Learned the importance of operational checklists in production environments.
Assignment 4 – Deploy EpicReads Portfolio Website Using Nginx
Hosted a static portfolio website on Nginx.
Configured the web root and server settings.
Verified successful deployment and browser accessibility.
Gained practical experience in web application deployment.
Assignment 5 – Bash Script Automation Drill
Automated routine operational checks using Bash scripting.
Created reusable scripts for service validation and system monitoring.
Improved operational efficiency by reducing repetitive manual tasks.
Learned scripting best practices for DevOps engineers.
Key Takeaways
Built a strong foundation in AWS, Linux, and Nginx.
Learned how to deploy and host web applications on cloud infrastructure.
Practiced production maintenance and troubleshooting techniques.
Automated operational tasks using Bash scripting.
Strengthened practical DevOps skills through real-world, hands-on assignments.

Overall Learning: This week's assignments provided end-to-end exposure to setting up cloud infrastructure, deploying applications, maintaining production environments, and automating operational tasks—the core building blocks of a DevOps engineer's daily responsibilities.

About DMI & CloudAdvisory

DevOps Micro Internship (DMI) is a project-based DevOps program run by Pravin Mishra (The CloudAdvisory) focused on real-world execution, systems thinking, and career readiness.

It helps learners build strong DevOps foundations with hands-on experience.

Reflection – Week 2

CharanTeja Chavithina — Wed, 15 Jul 2026 06:20:09 +0000

Reflection – Week 2

Name: Chavithina Charan Teja

Week 2 of the DevOps Micro Internship has been one of the most challenging and rewarding learning experiences in my career. Coming from a traditional DevOps and Site Reliability Engineering background, I initially thought this week would mainly be about learning another AI tool. Instead, I discovered an entirely new way of working with AI through Claude Code, Skills, Subagents, MCP, Hooks, Permissions, and Memory. Every single day introduced concepts that I had never used before, and the learning curve was much steeper than I expected.

The journey started with something as basic as installing and configuring Claude Code. Even this first step was not straightforward. I faced installation issues, authentication challenges, and several configuration problems before I was finally able to get everything working correctly. Although these challenges were frustrating at first, they taught me an important lesson: every new technology requires patience, persistence, and hands-on practice. Solving each issue gave me more confidence to move on to the next topic.

One of the biggest highlights of Week 2 was learning about Claude Code and understanding how AI can become an active engineering partner instead of just answering questions. Watching it generate infrastructure, automate repetitive tasks, and assist in development workflows completely changed my perspective on how software engineering and DevOps will evolve in the coming years.

Another fascinating topic was Skills and Subagents. Initially, it was difficult to understand why we needed different skills and specialized agents. However, after completing the practical assignments, I realized how powerful this architecture is. Breaking complex work into smaller specialized agents makes automation more structured, reusable, and scalable. It felt very similar to designing microservices, but this time for AI-powered workflows.

The topics of Hooks, Permissions, and Memory were equally exciting. Hooks demonstrated how automation can be triggered intelligently during different stages of a workflow. Permissions taught me the importance of maintaining security and control while allowing AI agents to perform actions. Memory was probably the most impressive feature because it showed how an AI agent can remember previous project decisions and continue working without requiring the same context repeatedly. This made me realize how much productivity can improve when AI becomes context-aware.

The practical assignments throughout the week were extremely valuable. Every exercise required real implementation instead of simply watching videos or reading documentation. There were moments when I got stuck for hours while troubleshooting errors, understanding configurations, or figuring out why something wasn't working. Instead of skipping those problems, I kept experimenting until I found the solution. Those struggles became the most memorable learning moments because they strengthened my understanding far more than simply reading theory.

The biggest mindset shift for me this week was realizing that learning AI-powered engineering is not about memorizing commands. It is about understanding workflows, experimenting continuously, asking better questions, and building practical solutions. This course has encouraged me to think differently about automation and has shown me that the future of DevOps is becoming increasingly agentic, where engineers collaborate with intelligent AI systems to deliver faster and more efficiently.

One habit I am committed to implementing is dedicating every day after 9:00 PM exclusively to practicing everything I learn in this internship. Rather than only watching the sessions, I will recreate every practical demonstration, complete every assignment, and continue experimenting until I fully understand each concept. I believe consistency is far more valuable than occasional motivation, and this daily learning routine will help me build strong practical expertise in Agentic AI and modern DevOps.

Finally, I would like to sincerely thank Pravin Mishra, Anajana, and all the mentors for designing such an outstanding learning experience. The course is filled with real-world practical demonstrations, challenging assignments, and industry-relevant content that pushes every learner beyond their comfort zone. The guidance, continuous updates, and hands-on approach make this internship truly different from traditional training programs. I am genuinely excited for the upcoming weeks and look forward to applying these new skills in real-world DevOps and AI engineering projects.
About DMI & CloudAdvisory

DevOps Micro Internship (DMI) is a project-based DevOps program run by Pravin Mishra (The CloudAdvisory) focused on real-world execution, systems thinking, and career readiness.

It helps learners build strong DevOps foundations with hands-on experience.

How the DevOps Micro Internship Strengthened My Engineering Leadership

CharanTeja Chavithina — Fri, 03 Jul 2026 18:02:26 +0000

This is Charan here

I am currently working as an Technical manager and have experience in java microservices and production support. As a manager my focus is to enhance three pillars;system reliability operation excellence and team enablement. I led large technical teams and partnered with the global business stakeholders for delivery of stable

secured

and cost-efficient platforms. I manage cross-functional teams of around 40 members I ensure with owner ship models encourage incident management & ICA culture. I promote proactive communication with our stakeholders
and establish/create a strong release management and deployment governance model involved in monitoring key metrics

KPIs

first contact resolution. And regularly engage with client stakeholders

conduct status review meetings and present reliability metrics. Right now at this stage of my career my focus has not just been on technical execution but also on building high performing teams

maturing our engineering processes and aligning technology to the business outcome. My Continuous Learning Journey: Certifications That Shaped My Career in Site Reliability Engineering In the rapidly evolving technology industry

About DMI & CloudAdvisory

DevOps Micro Internship (DMI) is a project-based DevOps program run by Pravin Mishra (The CloudAdvisory) focused on real-world execution, systems thinking, and career readiness.

It helps learners build strong DevOps foundations with hands-on experience.