The friction of writing syntax for complex data pipelines has significantly decreased due to advanced AI agents living inside IDEs. These agents can now generate initial implementations, write tests, and propose refactors, shifting the engineer's core responsibilities. The question arises whether engineers will become mere reviewers or if their work will abstract towards designing systems. Borrowing from thermodynamics, AI agents are viewed as heat engines that turn direction into action but accumulate operational entropy. Human interruption or precise feedback signals are crucial for agents to avoid drifting from correct outcomes and ensure generated motion translates into useful work. Similar to the infinite monkey theorem, agents repeatedly propose, execute, observe, and correct, but enterprise systems present a constantly changing environment. This dynamic is likened to the three-body problem, where small changes in one system can lead to unpredictable trajectories across interconnected platforms. Engineers' new mandate is to design equilibrium by creating containment fields and clear boundaries, such as semantic data contracts. These structures reduce agent assumptions and make errors visible and recoverable. When these bounded domains exist, agents become powerful, executing tasks without needing to infer complex histories. The visibility of software engineering value increases as code generation becomes cheaper, with engineers designing the crucial contracts and feedback loops that govern AI-generated software.