Artificial Intelligence–Augmented Software Development Life Cycle: From Copilots to Agentic Pipelines

About This Course

Adoption of AI coding assistants is now mainstream: the 2025 Stack Overflow Developer Survey reports that 84% of respondents are using or planning to use AI tools, and 51% of professional developers use them daily; nonetheless, positive sentiment has fallen to about 60%, reflecting concerns over accuracy, quality, and governance.

At the organizational level, widespread use of Artificial Intelligence coding assistants introduces significant challenges across quality assurance, security, governance, and culture. Typical issues include inconsistent adoption and skills gap, quality and maintainability, and security vulnerabilities

To mitigate these risks, organisations require training that addresses both tool usage and team practices. Local enterprises are actively upskilling their engineering workforce. Specifically, teams need:

•Technical usage of Artificial Intelligence tools: hands-on configuration, prompting, and integrating AI code assistants with development environments, as well as guidance for reviewing, editing, and testing AI-generated code.

•Coding standards and documentation: best practices for code quality, internal documentation, and rationale for code choices to ensure maintainability.

•Collaborative workflows: team-based procedures of working with AI tools, including code review and branch/Pull Request hygiene, and patterns for integrating AI-assisted changes into larger projects.

This course equips software teams to integrate Artificial Intelligence tools into the Software Development Life Cycle. It covers core Large Language Model concepts, effective prompt design, and a repeatable end-to-end workflow spanning requirements, design, implementation, testing, deployment, and validation. It also introduces tool-agnostic multi-agent orchestration—defining role specialisation, coordination patterns, and human-in-the-loop oversight—to enable reliable and safe delivery.