Planning Simulator
Master AI agent planning through interactive simulations and real-world scenarios
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0 / 5 completedMaster Agent Planning
Review these essential principles of agent planning and execution. Check off each concept as you master it. Your progress is tracked below.
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0/15Planning Fundamentals
Planning is the bridge between abstract goals and concrete actions. Good plans decompose high-level objectives into executable subtasks with clear ordering.
Sequential planning is simple but slow; parallel planning is faster but requires careful dependency management. Choose based on task independence and urgency.
Plan complexity grows exponentially with task count. More tasks mean more dependencies, longer planning time, and higher failure risk.
Plan Construction
Use forward chaining (start from current state), backward chaining (start from goal), or hierarchical decomposition (break into subgoals) based on problem structure.
Dependencies define execution order. Data dependencies (B needs A's output), state dependencies (B needs A's effects), and resource dependencies (B needs A's resources) must be respected.
Valid plans have: satisfied dependencies, no circular constraints, atomic executable tasks, clear start/end states, and documented resource requirements.
Resource Management
Every plan consumes resources: API calls cost money, compute takes time, tools have rate limits. Track and budget all resources to avoid overruns.
Resource strategies: Greedy (max speed, high cost), Balanced (optimal trade-offs), Conservative (min cost, slower). Choose based on constraints and priorities.
Optimize resources through caching (reuse results), batching (group operations), lazy loading (fetch on demand), and continuous monitoring (track consumption).
Execution & Monitoring
The execution cycle: Select task → Execute → Verify success → Update state → Continue. Each step must be monitored and logged for debugging.
Track execution metrics: completion time, resource usage, success rates, queue wait times, and error frequencies. Set alerts for anomalies.
Handle failures with retry strategies (simple, exponential backoff, circuit breaker) and recovery actions (rollback, fallback plans, partial success, human escalation).
Simulation & Testing
Simulate plans before deployment to identify bottlenecks, test failure modes, and measure performance without consuming real resources.
Test edge cases: What happens when tasks fail? When resources run out? When dependencies break? Simulation reveals weaknesses safely.
Comprehensive logging enables debugging and optimization. Log task start/end, inputs/outputs, decisions made, state transitions, and error details.