The AI Taxonomy.
Terms  /  Execution Quality  /  Plan Resilience
04 · Execution Quality

Plan Resilience

A plan's ability to remain accurate and actionable over time despite changing conditions. The opposite of Plan Rot.

Plan Resilience is a design property, not an outcome. A resilient plan anticipates change: it includes checkpoints where progress is verified, conditions under which tasks should be re-evaluated, and clear ownership of each step. Resilient plans don't rot because they're designed to be updated — they have built-in mechanisms for detecting when reality has diverged from the plan. This might mean automated verification of completed tasks, periodic re-assessment triggers, or explicit "staleness" markers on tasks that haven't been touched in a defined period.

Example
A team builds their project plan with three resilience features: (1) each task has acceptance criteria that can be programmatically verified, (2) tasks marked "done" are automatically re-verified every 48 hours, (3) tasks not started within their planned window are flagged for re-assessment. When the API spec changes mid-project, the system automatically flags three "completed" tasks whose acceptance criteria no longer pass. The plan self-heals instead of rotting.

More in Execution Quality

Execution Fidelity

How closely a system follows and completes its intended plan.

Plan Rot

Decay of truth in planning artifacts — task state in markdown files goes stale.

Plan Drift

Deviation from the original plan over time.

Execution Hallucination

Claiming work is done when it is not.

Execution Integrity

Consistent, verifiable completion of planned tasks with evidence.

Task Accountability

Every task has a verifiable record of who did what, when, and whether it actually completed.

Agentic Scope Creep

An agent silently expanding beyond what was delegated, usually from lack of a clear plan or direction. Adds breadth the user didn't ask for.

Depth Fixation

The agent keeps drilling deeper into implementation detail, generating new sub-tasks and refinements rather than recognizing the work is done and returning control to the user. Expands depth rather than breadth.