Uncertainty Planning
Master uncertainty planning to build robust agents that thrive in unpredictable environments
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Review these core principles of uncertainty planning for AI agents. Check off each concept as you understand it. Your progress is tracked below.
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0/15Uncertainty Fundamentals
Uncertainty is inevitable in real-world agent planning—incomplete data, unpredictable outcomes, and changing conditions are the norm, not exceptions.
Traditional rigid planning assumes perfect information and fails catastrophically under uncertainty. Robust planning embraces unknowns as inherent design constraints.
Distinguish between known unknowns (identifiable gaps you can plan around) and unknown unknowns (surprises requiring defensive architecture).
Information Gap Management
When facing known unknowns, gather information proactively, estimate with probabilities, or defer decisions until data becomes available.
For unknown unknowns, build redundancy, monitor continuously, maintain flexibility, and design for graceful degradation when surprises occur.
Use just-in-time planning: make decisions as late as safely possible with the most current information rather than over-planning far in advance.
Contingency Planning
Create backup plans (Plan B, C, D) for different failure modes. More uncertainty and higher criticality require more contingency layers.
Design fallback options that accept partial success over complete failure—70% achievement is better than 0% when full success becomes impossible.
For mission-critical tasks, use redundancy: execute multiple approaches simultaneously and use whichever succeeds first.
Adaptive Execution
Continuously monitor execution against expected outcomes. The observe-detect-decide-adapt loop must run throughout plan execution.
Track key metrics (time, cost, errors, progress) and define specific trigger conditions that automatically initiate contingencies or replanning.
Distinguish minor adjustments (< 30% divergence) from contingency activation (30-70%) and full replanning (> 70% divergence from assumptions).
Strategic Implementation
Structure plans as conditional decision trees: "If X happens, do Y; else do Z." Make your if-then-else logic explicit and executable.
Set clear checkpoints during execution where you explicitly evaluate whether core assumptions still hold and adjust course if needed.
Log divergences, adaptations, and outcomes to build institutional memory. Past surprises should inform future planning strategies.