With general intelligence, you can go a layer deeper: you can accelerate the acceleration. You don't just write the prompt that fixes the code; you build the evaluation pipeline that automatically optimizes the prompts. You stop working on the work, and start working on the optimization of the work. You shift from First-Order execution (doing the thing), to Second-Order automation (improving the system), to Third-Order meta-optimization (automating the improvement of the system). AI eats the lower derivatives, constantly pushing you up the stack to become the architect of the machine that builds the machine.

You can't leave anything on the table. This is Amdahl's Law for AI transformation: as the "core" work approaches zero duration, the "trivial" manual steps you ignored—the 10-minute deploy, the manual data entry on a UI, the waiting for CI—become the entire bottleneck. The speed of your system is no longer determined by how fast you code, but by the one thing you didn't automate5. If an agent can fix a bug in 5 minutes but it takes 3 days for Security to review the text or 2 days for Design to approve the padding, the organization has become the bug. You need to treat organizational latency with the same severity you treat server latency.

The core principle is simple: reduce the need for external context and assumptions. An AI agent is at its best when the next step is obvious and the tools are intuitive. This framework builds from the most immediate agent interaction all the way up to the complete system architecture. This isn’t to say today's agents can’t reason or do complex things. But to unlock the full potential of today’s models—to not just solve problems, but do so consistently—these are your levers.