AI-Assisted Software Development: Efficiency and Consistent Quality Throughout the Entire Lifecycle

In software development today, the question is no longer whether to use AI, but how.

Because AI can be useful on its own (for example, for code generation) but it provides the greatest value when it supports not just a single task, but the entire development lifecycle.

Boris Cherny, creator of Claude Code, writes about this here (in his post titled 13/ A final tip): if AI-supported development works cyclically in tandem with testing within an integrated system, a 2-3-fold quality improvement can be achieved.

This is the cornerstone of our AI-assisted SDLC solution.

What’s SDLC, and Why It’s Critical

SDLC (Software Development Life Cycle) is the regulated process of software development, which typically covers the following stages:

• needs assessment
• design
• development
• testing
• release/deployment
• operation/maintenance

This process provides the basis for ensuring that development is predictable and traceable, produces the right quality, and responds to business needs in a timely manner.

When Does AI-Assisted Software Development Support the Entire Process?

When AI does more than just write code. In practice, AI can support development in many other ways, for example:

• interpreting and creating specifications
• creating implementation plans
• writing and reviewing test cases
• generating release notes
• writing automated tests
• analyzing test results
• administration (e.g., Scrum board)
• understanding source code and refactoring suggestions

And here lies the solution: with the help of AI agents, most of these steps can not only be assisted by AI but can even be partially automated.

Agent = AI + Processes + Tools

An AI agent is much more than a “smart chat window.” A well-designed agent has the following characteristics:

• it has a clear role (e.g., Spec Agent, Coding Agent)
• it works in a goal-oriented manner
• can be integrated with tools (e.g., language models, Claude Code, ChatGPT Codex, GitHub/GitLab/Azure DevOps, CI/CD)
• can be integrated into workflows
• can operate in an event-driven manner
• and operates with human-in-the-loop logic: humans perform checks at critical points

From Business Need to Release: This is What the Process Looks Like

We view AI-assisted SDLC as a series of processes in which each step is supported by a dedicated AI agent. It is structured as follows:

1) The Entry Point: Spec Agent

Business requirements can come from JIRA tickets, Confluence pages, or documents (in PDF or DOCX format), for example, and Spec Agent works based on this information. Its tasks include

• processing descriptions in natural language
• identifying incomplete and/or contradictory requirements
• structuring functional and non-functional requirements
• creating uniform specifications that can be interpreted by both humans and machines

It is important to note that Spec Agent does not make business decisions: validation and final approval remain a human responsibility.

2) Planning and task breakdown: WBS Agent

Based on the specifications and existing source code, WBS (Work Breakdown Structure) Agent’s role is to break down tasks into detailed steps and create a step-by-step, detailed plan that can be used as a basis for development and testing.

3) Implementation: Coding Agent

Coding Agent performs development and prepares the related documentation based on the specification, existing source code, and any other instructions. If necessary, it can also be integrated with various tools (e.g., Codex CLI, OpenCode, MCP). However, it is not responsible for IT security issues, license management, or legal compliance.

It is important to note that the code generated by the agent is entered into the system with human approval, and the developer remains responsible for the quality of the code.

4) The entire testing process: AI-assisted Testing

The AI-assisted testing process is supported by the following agents:

• test case design: Test Designer Agent
• test case writing: Test Analyst Agent
• test case validation: Test Reviewer Agent
• running test cases: Test Runner Agent
• evaluation of test results: Evaluator Agent

5) CI/CD + DeployFix Agent

CI/CD processes typically stick to the usual tools — but AI can help with the following:

• generating pipeline configurations and scripts
• creating infrastructure descriptions
• analyzing build/deployment errors and developing repair recommendations, creating an analysis process

 

Finally, at the end of the process, the finished software goes live after human validation.

 

How Does This Benefit Business and IT? (The Top Six Advantages)

1) Faster development, faster business response

Automated creation of specifications, coding, testing, and bug fixing result in significantly shorter lead times, enabling faster service rollouts, faster response to business and market needs, and efficient change management.

2) Better quality, fewer bugs

The AI-based system ensures consistent quality at every stage of development. Thanks to multiple tests and more efficient error detection, fewer incidents are expected during live operation.

3) Cost savings and capacity gains

Thanks to the automation of repetitive tasks, developer capacity is freed up, allowing developers to focus on creating real value. 

4) Standardized, transparent operations

Every step of the process is documented, auditable, and traceable, reducing the risk of miscommunication between business and IT.

5) Reduced risk, more robust compliance

The AI-driven process helps maintain regulatory compliance ( ISO, SOX, GDPR, etc.). Fewer human errors mean lower operational and IT risks – and since every decision and change is automatically logged by the system, the process is ready for auditing.

6) Faster and safer innovation

Since multiple parallel projects can run with the same development and testing capacity, innovation is accelerated and new (digital) services can be brought to market faster.

 

Final Thought

AI-assisted SDLC does not replace developers and testers. Instead, it relieves them of some of the monotonous, repetitive tasks, speeds up the entire development cycle, further improves the quality of the software created, and enhances business-IT collaboration.

It may not be an exaggeration to say that it could usher in a new era in software development.

Would your company also benefit from accelerating the entire development lifecycle and ensuring consistent code quality? Why not discuss this over a cup of coffee?