Designing failure in advance is as important as designing for success.

Failing early preserves resources; failing late extracts maximum value from them.

In human-AI teams, explicit agreement on failure direction is essential, not optional.

When you build and operate in high-stakes, chaotic environments, you have to plan for failure. The Stoics had a name for thinking ahead about this: premeditatio malorum—the deliberate contemplation of what will go wrong. Modern systems science uses the term failure modes: the specific ways a system will break. The more clearly you can map your failure modes in advance, the more you can design your system to move through, over, or around them. Of course, not all failures are equal, and not all failure directions are the same. In this post, we will consider three axes for distinguishing between failures and how each one can inform different design choices for complex systems operating in high-stakes environments.

Failing Toward vs Failing Away

Systems that fail toward something have proactively identified a secondary point of stability outside their normal model of operation. These secondary points are not ideal, but they are enough to keep operating at a reduced level, and failing toward them increases the chance of accomplishing the objective even when the first approach falls short. Human-AI teams use this “failing toward” mode of failure. When operating at full capacity, these teams balance complex inputs, perform complicated operations, and share decision authority between human and artificial elements. Human-only decision-making is a........

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