Generation became cheap; acceptance did not. A working definition of AI-native engineering, four mental models for reasoning about it, and what it changes for teams and for individual engineers.
Code generation has a free, instant, perfect oracle. Prose has none. Here is two months of narration-quality fixes in PaperCast, why each one was locally right and globally insufficient, and what we are changing.
AI coding makes code abundant, but review capacity stays scarce. Here is a practical model for shifting from line-by-line review to risk-based change verification.
MCP standardizes how tools and context are exposed to models, but reliable agent systems still depend on orchestration, policy, durability, observability, and approval-driven runtime design.
A practical reference blueprint for an agent harness, grounded in the live harness-engineering repo and updated with a real blueprint export instead of an imaginary architecture diagram.
Why agent performance engineering lives in the harness, with a real repo update that adds per-step timing and workload estimates to trace summaries and run summaries.
A practical look at what security actually means in an agent harness after adding a checked-in policy config, provider env handling, and verification surfaces to the live demo repo.
A practical look at small multi-agent systems after adding explicit planner→executor→reviewer handoffs to the live harness-engineering demo.
A practical look at harness-level sandboxing and isolation after adding explicit action categories, policy checks, and write-path enforcement to the live harness-engineering demo.
A practical look at tracing, operator-facing observability, and trace-aware evals in the live harness-engineering demo after adding persisted trace summaries and lightweight eval fixtures.
