After the Last Git Commit (gist.github.com)

This is a time-capsule account of how AI agents quietly rewired software engineering between 2021–2030: economics drove the change—AI teams produced equivalent output for a fraction of human costs (under 6% of a 2021 human-team bill), drawing capital and accelerating adoption. Code completion gave way to autonomous, role-based agents that read repos, manage dependencies, run simulations, test edge cases and maintain long-term memory. By 2025–2029 a few platforms dominated (Cursor for developer-first workflows, OpenAI Code for regulated enterprises, and GitHub Copilot as ubiquitous but slower-to-adapt), with agents handling ~96% of engineering automation in Fortune 500s and 72% of commits in fast-cycle teams. Telemetry cited: 94% of enterprise commits were agent-authored by 2029; average intent-to-deploy latency fell to 23 minutes; engineers spent only 12% of time editing code. Technically and organizationally, the SDLC morphed into Adaptive Continuous Engineering (ACE)—an always-on loop of intent capture → agent planning → auto-coding → test/simulate → contextual QA → live deploy → runtime feedback → memory update (CI/RL/CM rather than CI/CD). New priorities are governance, intent traceability, multi-agent alignment, latency resilience and auditability. GitHub’s Copilot pivot added a Control Plane, Intent-Aware Reviews, Agent Summons and open model releases (8B/32B/270B) to win enterprise trust. The takeaway: developers become cognitive directors coordinating heterogeneous agents; the next competitive edge is safe, auditable coordination—smaller local models, hybrid orchestration, and systems that justify decisions, not just produce code.