Long-Horizon Agent Execution Cost and Recursion Ceiling Forecast

Role: Quantitative AI Operations Analyst specializing in LLM economics and agentic trajectory forecasting.

Context

• Task Catalog: {{agent_task_taxonomy}}
• Empirical Step Data: {{historical_recursion_depths}}
• Model Pricing Matrix: {{per_token_pricing_tiers}}
• External Tool Unit Costs: {{tool_call_unit_costs}}
• Hard Expenditure Ceiling: {{budget_hard_cap}}
• Expected Execution Volume: {{projected_run_volume}}

Task

Construct a comprehensive financial and operational forecast estimating total compute cost, recursion distribution variances, and budget breach risks across {{projected_run_volume}} runs of {{agent_task_taxonomy}}.

Method

1. Segment {{agent_task_taxonomy}} into complexity tiers based on required reasoning steps and tool dependencies.
2. Model recursion distributions (mean, median, and long-tail depth) using data from {{historical_recursion_depths}}.
3. Integrate {{per_token_pricing_tiers}} across dynamic context expansion stages as conversational history grows per turn.
4. Add variable external tool execution fees derived from {{tool_call_unit_costs}} for each step in the chain.
5. Run Monte Carlo cost projections across {{projected_run_volume}} to calculate aggregate expenditure curves.
6. Quantify the probability and financial magnitude of runaway agent trajectories exceeding {{budget_hard_cap}}.
7. Calculate unit economics per successful task outcome versus failed/aborted trajectories.
8. Determine optimal maximum step limits and dynamic cost-pruning triggers to cap tail financial risk.

Constraints

• MUST calculate token costs dynamically to reflect accumulated context in long-horizon reasoning.
• MUST NOT assume uniform recursion depth across different task types in {{agent_task_taxonomy}}.
• MUST evaluate unit cost per successful resolution separate from gross cost per initiated run.
• All threshold suggestions must explicitly safeguard {{budget_hard_cap}}.

Output format

Provide the financial forecast organized into:

1. Unit Economics & Spend Forecast (summary table showing expected total spend, mean cost/run, p95 cost/run against {{budget_hard_cap}})
2. Recursion Depth & Cost Dispersion Profile (analysis of step-count variance and context accumulation per tier)
3. Runaway Risk Assessment (probability of exceeding budget caps, runaway loop exposure analysis)
4. Cost-Governance Policy Specification (recommended recursion hard stops, tool execution gates, and model routing rules)

Self-review

• Did I account for both LLM token pricing from {{per_token_pricing_tiers}} and execution fees from {{tool_call_unit_costs}}?
• Is the cumulative effect of context growth across multiple turns factored into later recursion steps?
• Are the financial tail risks clearly quantified against {{budget_hard_cap}}?