Moving from AI-Assisted Engineering to AI-Agentic Software Engineering

Sajith Vijsekara — Tue, 18 Aug 2026 15:23:07 +0000

Moving from AI-Assisted Engineering to AI-Agentic Software Engineering

The rise of AI coding assistants has transformed how developers write software. Tools like GitHub Copilot, ChatGPT, Claude, and Gemini have significantly improved developer productivity by helping generate code, explain concepts, and automate repetitive tasks.

However, the industry is now entering the next evolution: AI-Agentic Software Engineering.

Instead of AI simply assisting developers, AI agents can now take ownership of entire software engineering tasks—from requirement analysis and architecture design to implementation, testing, documentation, and code reviews. The challenge is no longer whether to use AI, but how to integrate AI agents into a structured Software Development Lifecycle (SDLC).

This requires moving away from vibe coding toward specification-driven development, where AI agents operate using well-defined requirements, standards, and engineering principles.

Today, I'd like to discuss two of the most popular frameworks enabling this transition.

1. Spec Kit

Spec Kit is a specification-driven framework designed for Human + AI collaborative software development.

The philosophy is simple: define the specification before generating the code.

Rather than asking an AI to build an application from a vague prompt, Spec Kit encourages teams to create structured specifications, architectural decisions, and engineering principles that guide AI throughout the development lifecycle.

Some key benefits include:

Structured and repeatable software development
Better requirement traceability
Consistent architecture decisions
Reduced AI hallucinations
Lower development costs through predictable AI interactions
Support for selecting the most appropriate LLM based on project requirements
Integration of quality engineering practices from the beginning of the SDLC

Spec Kit is particularly valuable for engineering teams that want to adopt AI without sacrificing software quality or maintainability.

2. BMAD (Breakthrough Method for Agile AI-Driven Development)

BMAD is an AI agent orchestration framework that models an entire software engineering organization using specialized AI agents.

Instead of relying on a single coding assistant, BMAD coordinates multiple expert agents, including:

BMAD Master
Product Manager
Business Analyst
Software Architect
UI/UX Designer
Scrum Master
Software Developer
QA Engineer
Technical Writer

Each agent is responsible for a specific stage of the SDLC, allowing teams to build software using a structured, collaborative AI workflow.

Compared with traditional vibe coding, BMAD offers several advantages:

Specifications are created before implementation
Context is preserved throughout the project lifecycle
More deterministic and repeatable outputs
Clear separation of responsibilities across AI agents
Better scalability for enterprise software projects

BMAD also supports multiple development workflows:

Quick Flow – Rapid prototyping and proof of concepts
Standard Flow – Balanced workflow for most software projects
Enterprise Flow – Designed for large-scale enterprise development with governance and quality controls

Another significant advantage is its support for the Model Context Protocol (MCP), enabling AI agents to integrate with external engineering tools such as:

GitHub
Atlassian (Jira & Confluence)
SonarQube
Figma
Other MCP-compatible services

This allows AI agents to work directly with the same ecosystem that engineering teams already use.

Final Thoughts

AI-assisted development has already changed how we write code.

The next transformation is AI-Agentic Software Engineering, where AI becomes an active engineering teammate rather than just a coding assistant.

The organizations that succeed will not simply adopt more AI tools—they will establish structured frameworks, engineering governance, and specification-driven workflows that enable AI agents to deliver reliable, maintainable, and scalable software.

The future isn't about replacing software engineers.

It's about enabling engineers to lead teams of AI agents that can execute software development with greater speed, consistency, and quality.

What are your thoughts?

Are you still using AI mainly as a coding assistant?
Have you started experimenting with AI agent frameworks like Spec Kit or BMAD?
Which approach do you think will become the standard for enterprise software engineering over the next few years?

I'd love to hear your experiences and perspectives.

CKAD Exam Guide for 2024

Sajith Vijsekara — Sat, 13 Jan 2024 14:25:58 +0000

In the year 2024, I started with good news, I had cleared my CKAD exam and I thought to share my experience to prepare and face the exam.

Background

I have done projects in the last 4 years using the k8 environments. Though I have worked on projects I thought to prepare for certification and prove I know k8. My first choice was to start CKA and decided to start CKAD first and then moved to CKA.

Materials

Mumshad Mannambeth this udemy course I have followed.
KillerCode this Tutorial link
K8 Documentation https://kubernetes.io/docs/home/
For Network policies this repository is very useful
This GitHub repository is ok but some of the content is outdated.

Prepare for Exam

Read full article in here

Event Driven Architecture with AWS SQS

Sajith Vijsekara — Mon, 10 Jan 2022 16:00:47 +0000

What is Message Queue Service?

Before learning the AWS SQS we have to learn what is message queue service first. Message Queue service is “the system that pushes a message into a queue by providing an asynchronous communications protocol”. There are two parties involved in queue service. Those parties are called Sender and Receiver.

DEV Community: Sajith Vijsekara

Moving from AI-Assisted Engineering to AI-Agentic Software Engineering

Moving from AI-Assisted Engineering to AI-Agentic Software Engineering

1. Spec Kit

2. BMAD (Breakthrough Method for Agile AI-Driven Development)

Final Thoughts

CKAD Exam Guide for 2024

Event Driven Architecture with AWS SQS