Uncertainty Planning
Master uncertainty planning to build robust agents that thrive in unpredictable environments
Your Progress
0 / 5 completedThe Reality of Uncertainty
Perfect information is a luxury most AI agents don't have. In the real world, agents face incomplete data, unpredictable outcomes, and changing conditions. A travel agent doesn't know if flights will be delayed. A trading bot can't predict market crashes. A research assistant can't guarantee API availability.
The difference between a fragile agent and a robust one? How it handles uncertainty. Great agents don't just plan for the happy path—they anticipate unknowns and adapt dynamically.
Why Traditional Planning Fails
Brittle Planning
Assumes perfect information. Single fixed plan. Breaks when reality differs from expectations. No fallback options.
Robust Planning
Acknowledges uncertainty. Multiple contingency plans. Adapts to new information. Degrades gracefully under failure.
Interactive: Planning Under Uncertainty
See how agents respond when faced with uncertain conditions
Available Information
- ✓ All flight prices are fixed and known
- ✓ Weather forecasts are 100% accurate
- ✓ No delays or cancellations possible
- ✓ Hotel availability guaranteed
Agent's Plan: Simple linear plan works perfectly. Book cheapest flight → Reserve hotel → Done. No contingencies needed.
Interactive: Uncertainty Impact
Adjust the uncertainty level to see how planning confidence changes
Moderate Confidence
Agent should prepare backup plans. Monitor for changes during execution.
Core Principle
Uncertainty isn't a bug—it's a feature of real-world environments. Robust agents don't try to eliminate uncertainty; they plan for it. They create flexible plans with multiple paths, monitor execution continuously, and adapt when reality diverges from expectations.