Electrobun SDLC

Building software features end-to-end with an AI agent pipeline involves orchestrating specialized agents across eight stages: research, architecture, planning, development, QA, testing, alignment, and documentation. Each stage's output feeds directly into the next.

An AI agent pipeline for feature development is a structured process that coordinates specialized agents—such as a researcher, architect, dev squad, and QA engineer—to handle the complete software development lifecycle from concept to documentation.

Automating code generation and test writing requires a multi-stage pipeline where a dev squad agent writes the code and a dedicated test writer agent generates the corresponding tests, ensuring structured development before final alignment.

Yes, an AI development pipeline can generate documentation and architecture plans by passing the initial research outputs to an architect agent for design, and a docs agent for final documentation after the coding and testing stages.

No external dependencies are required to run the full software development lifecycle with AI using this pipeline. It internally manages the researcher, planner, dev squad, and QA engineer roles without external components.

Use a multi-agent pipeline over standard AI code generation when you need structured, full-cycle development that includes research, architecture design, QA, alignment, and documentation, rather than isolated code snippets.