Demo: Multi-Agent SDLC
Demo 5 is Multi Agent SDLC, and this is where wave 3 becomes real.
The burnout driver we are addressing here is simple: one engineer cannot safely parallelize everything that modern software demands. Security checks, API contracts, regression hardening, tests, documentation, release notes. If we try to do it all sequentially, we go slow. If we try to do it all at once in our own head, we burn out.
So the idea is not ‘let the AI do everything.’ The idea is ‘use multiple specialized agents to do focused work in parallel, while keeping the human approval gates intact.’
In this demo I’ll launch three agents on isolated branches or worktrees. Each agent has a clear job. One might focus on security hardening, one on contracts and validations, one on regression tests and edge cases.
The important detail is that each agent first detects the current state. It does not assume. It reads the repo, it finds what is already there, and then it implements the missing work or adds guardrails.
Then they run targeted tests, handle failures, and self correct before they deliver output. What you get back is not a pile of suggestions, it is reviewable changes that fit into a normal PR workflow.
And throughout all of this, you remain in control. The output stays branch scoped. The changes are inspectable. And the final merge is still a human decision.
Alright, let’s switch to VS Code and I’ll show what multi agent work looks like when it is safe, boring, and repeatable.