What's Left for Developers When Agents Write the Code
The short answer is that the job doesn't disappear, it moves. When AI compresses implementation, the bottleneck shifts upstream and downstream: requirements, architecture and verification stay human-paced, while a developer's value moves from typing the solution to defining goals, boundaries and acceptance criteria. The same raw model, inside different harnesses, produces very different results, so the useful question is no longer only which model, but which system runs it. In this talk (inspired by the latest SDLC paper by Addy Osmani & c) I will walk through the shift from implementor to conductor and orchestrator, and from vibe coding to agentic engineering, with the concrete pieces that make the difference: rule files and context engineering to give an agent the onboarding you'd give a new colleague, MCP to connect it to real tools, hooks and tests as a safety net, human review as a gate that changes shape but doesn't disappear. I show a demo using Antigravity (recorded probably or live) harness intercepting an error live, because the point isn't a perfect agent, it's a verifiable process. People leave with one operational question: how much of their project's standard is readable by an agent today, and where they can start without risking production.