agent-finops

name

agent-finops

description

Design cost-efficient AI agent architectures. Use when optimizing token usage, selecting model tiers, budgeting compute costs, implementing caching strategies, or designing plan-and-execute patterns for cost reduction. Covers model tiering (frontier for planning, cheap for execution), token budgeting, response caching, the plan-and-execute cost reduction pattern (up to 90% savings), and cost monitoring. Based on emerging FinOps-for-AI trends, heterogeneous model architectures, and production cost optimization practices.

Agent FinOps

Design cost-efficient AI agent architectures with model tiering, token budgeting, and caching.

Workflow

Cost Optimization Workflow

1. Audit current token usage per agent component
2. Classify tasks by complexity (planning vs execution)
3. Assign model tiers to each task class
4. Implement caching for repeated queries
5. Set up cost monitoring and alerts

Cost Audit Workflow

1. Measure tokens consumed per agent interaction
2. Identify the most expensive operations
3. Check for cacheable or downgradable operations
4. Calculate potential savings from model tiering
5. Generate cost reduction recommendations

Model Tiering

Three model tiers for different task complexities. Read references for templates.

Tier	Model Class	Use For	Reference
Frontier	GPT-4o, Claude Opus, Gemini Ultra	Complex reasoning, planning, orchestration	references/01-model-tiering.md
Mid-Tier	GPT-4o-mini, Claude Sonnet, Gemini Pro	Standard tasks, code generation	references/01-model-tiering.md
Economy	GPT-3.5, Claude Haiku, Gemini Flash	High-frequency, simple execution	references/01-model-tiering.md

Plan-and-Execute Pattern

Read the reference for the cost reduction architecture.

Component	Description	Reference
Planner	Frontier model creates strategy (high cost, low frequency)	references/02-plan-and-execute.md
Executor	Economy model follows plan (low cost, high frequency)	references/02-plan-and-execute.md
Verifier	Mid-tier model checks results (medium cost, as needed)	references/02-plan-and-execute.md

Token Optimization

Read the reference for token reduction strategies.

Strategy	Savings	Reference
Response Caching	40-80% for repeated queries	references/03-token-optimization.md
Structured Outputs	20-40% vs free-form text	references/03-token-optimization.md
Context Compression	30-50% on conversation history	references/03-token-optimization.md
Batch Processing	10-30% on similar requests	references/03-token-optimization.md

Cost Monitoring

Read the reference for monitoring and alerting setup.

Metric	Description	Reference
Cost per Interaction	Average spend per user session	references/04-cost-monitoring.md
Token Efficiency	Useful output tokens / total tokens	references/04-cost-monitoring.md
Cache Hit Rate	Percentage of requests served from cache	references/04-cost-monitoring.md
Model Tier Distribution	Percentage of requests per tier	references/04-cost-monitoring.md

Anti-Patterns

• Frontier Everything — using the most expensive model for all tasks
• No Caching — regenerating identical responses repeatedly
• Token Bloat — verbose system prompts consuming budget on every call
• Invisible Costs — no monitoring, no budget alerts, surprise bills
• Premature Optimization — optimizing cost before validating quality

Validation Scripts

Estimate agent operational costs with automated scoring (0-10):

python3 scripts/estimate_cost.py <prompt_file> [--strict]

Detects model references across 12 LLMs, calculates per-call and monthly costs (1K/10K calls), checks for tiering/caching/budget strategies, and flags cost anti-patterns (premium models for all requests, full history inclusion, disabled caching).