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Planning Simulator

Master AI agent planning through interactive simulations and real-world scenarios

Managing Plan Resources

Every plan consumes resources: API calls cost money, compute takes time, and memory has limits. Effective resource management means achieving goals within budgets—not just technically possible plans, but economically feasible ones.

Critical Resources to Track

💰

API/LLM Costs

Each tool call and LLM query costs tokens. Track cumulative spend.

GPT-4 call:
$0.03 per 1K tokens
⏱️

Time Budget

User tolerance for waiting. Real-time vs batch processing trade-offs.

User expects:
< 5s for simple tasks
🔧

Tool Quotas

APIs have rate limits. Respect them or face throttling/blocking.

Typical limit:
100 calls/min

Interactive: Resource Budget Allocation

Distribute 100 points across three resource categories. See the trade-offs!

40%
30%
30%
Total Budget100%
✓ Budget perfectly allocated
40%
30%
30%
Plan Complexity
Moderate
Processing Speed
Slow (sequential)
User Experience
Laggy

Resource Allocation Strategies

balanced Strategy

Optimal trade-off between speed, cost, and reliability.

Performance
70%
Efficiency
70%
Reliability
75%
Best For:
• Production systems • General-purpose agents • Most real-world use cases
Avoid When:
• Extreme requirements • Need maximum speed • Zero tolerance for latency

Resource Optimization Best Practices

💾

Caching

Store and reuse results of expensive operations. Don't re-fetch unchanged data or re-compute identical queries.

Batching

Group similar operations together. Make one API call with 10 items instead of 10 separate calls.

🔄

Lazy Loading

Only fetch or compute data when actually needed. Don't prepare resources that might never be used.

📊

Monitoring

Track resource consumption in real-time. Set alarms for budget overruns before they become critical.