AI SDLC Learnings
Anthropic released its AI Native SDLC approach and the internet reacted as if software delivery had just discovered electricity.
Anthropic released its AI Native SDLC approach and the internet reacted as if software delivery had just discovered electricity 🤪.
I did what any perfectly normal framework author would do and placed it next to my slightly older AgentFlow approach in a comparison table. What stood out was not novelty or ranking but convergence. Both make intent, planning, evidence, review and human authority part of engineering itself rather than treating them as administrative work around code.
The difference is where each goes deepest. AgentFlow acts as a portable governance and evidence layer across tools, with particular attention to provenance and defensible claims. Anthropic builds a tighter Claude-native operational loop around deployment, rollback, telemetry and learning from incidents. One strengthens control across environments while the other shows what deeper runtime integration can unlock.
Seen that way, the approaches are complementary and the comparison makes AgentFlow's next opportunities clearer. Deploy and Operate need more depth, incidents should flow into evaluations and outcome measures should sit alongside readiness and evidence.
I am pleased that an older approach shares so much of the same foundation and happier still that the differences point somewhere productive. Explore the carousel and use the comparison to reflect on what your own SDLC already covers and where it still needs to evolve. 👇
#AISDLC #AgenticEngineering #SoftwareDelivery