Planning Fundamentals
Master how AI agents break down complex goals into executable steps
Your Progress
0 / 5 completedPlanning Strategies
Different problems require different planning approaches. Just like a GPS can route by fastest time or shortest distance, AI agents use different strategies depending on the problem structure.
Interactive: Strategy Explorer
Select a strategy below and watch how the planning process unfolds
Forward Planning
Start from current state, plan toward goal
1
Current State
Where we are now
2
Step 1
First action
3
Step 2
Next action
4
Step 3
Final action
5
Goal Achieved
Desired outcome
Example
Travel from NYC to LA: Book flight → Pack bags → Drive to airport → Board plane → Arrive LA
✓ Advantages
- • Natural to understand
- • Easy to validate each step
- • Works well with known starting conditions
⚠ Limitations
- • May explore unnecessary paths
- • Can get stuck in local optima
Choosing the Right Strategy
Use Forward Planning When:
- • Current state is well-defined and known
- • Actions have predictable outcomes
- • You want to explore multiple paths
- • Example: Route planning, game AI, process automation
Use Backward Planning When:
- • Goal is clear but path is uncertain
- • You want the most efficient solution
- • Prerequisites are well-defined
- • Example: Dependency resolution, proof planning, workflow design
Use Hierarchical Planning When:
- • Problem is large and complex
- • Need to adapt plan during execution
- • Want to reason at different abstraction levels
- • Example: Project management, military strategy, multi-agent coordination
Hybrid & Advanced Strategies
Real-world agents often combine multiple strategies:
Bidirectional Planning
Plan forward from start AND backward from goal simultaneously, meet in the middle
Reactive Replanning
Start with a plan, but monitor execution and replan if conditions change
Constraint-Based Planning
Define constraints upfront, let solver find any valid plan that satisfies them