LV2 · Toolooo Standard
AI Agent Cost & Loop Simulator
Compare agent token, retry, branch and model-cost growth against explicit guardrails using your own prices.
How expensive can an agent loop become? Compare the same seeded scenario with and without operational guardrails. Rates below are fictional editable examples, not live provider pricing.
Agent behavior and context
Guardrails and summarization
Model assumptions and safety caps
Maximum 100 steps, 64 active agents, 10 retries, 8 branches per split and 20 tool calls per call. Context and added input are capped at 1M tokens per agent; output and tool-return tokens at 100K, and each price at $100K per 1M tokens. These simulator safety limits also apply to the unguarded baseline. Normal completion means reaching the chosen horizon, not proving task success.
Input cost = input tokens × input rate / 1M; output cost uses the output rate. No external tool fees, cached-token discounts or provider-specific hidden billing are modeled. A retry replays the same modeled call. Guarded retries use your separate retry limit. Step, retry and branch counts are rounded down; tool calls may be fractional averages. Repeated actions are approximated only for the always-retrying scenario; consecutive error steps mean steps that retry, not observed real failures.
Summarization adds a model call per active agent, bills the old context as input and up to 1,000 summary output tokens, then retains that summary. Retention scales the next context; pruning is not free. Budget checks prevent the next aggregate step and cannot guarantee a real provider bill ceiling.
What this tool does and why
Before you start
What
Compare agent token, retry, branch and model-cost growth against explicit guardrails using your own prices.
Why
Repeated calls can repeatedly bill retained context while retries and branches multiply work. Explicit stopping boundaries make that amplification reviewable.
Your Result and Action update in the tool as you change its inputs.
Model note
This browser-side model explains the inputs you choose. Check an important decision against the real system, brief, or production constraints.
Next
How this tool stays honest
How it works
Simulate seeded agent execution, retained context, tool overhead and retries. Compare baseline usage with step, cost, token, tool, context, branch and approval limits. Inspect bounded timelines and billed summarization assumptions without calling a model or pricing API.
Assumptions
The tool runs in your browser and models the information you provide. It does not know facts outside that model.
Limitations
Seeded educational aggregates, not an agent runtime or provider forecast. User rates determine cost. All runs have safety caps; summarization is billed explicitly. Branches share average context and repeated-action detection is a stated approximation.
Understand AI Agent Cost & Loop Simulator
What this tool helps you understand
Simulate seeded agent execution, retained context, tool overhead and retries. Compare baseline usage with step, cost, token, tool, context, branch and approval limits. Inspect bounded timelines and billed summarization assumptions without calling a model or pricing API.
When to use it
Before setting operational budgets for an agent workflow.
Try a scenario
Compare an always-retrying 48-step horizon with a guarded 12-step limit at your entered rates; inspect prevented work and context costs.
How to read the result
Compare the shape and direction of different scenarios. The useful signal is how the result changes when an input changes, rather than one isolated number.
Assumptions and limits
Seeded educational aggregates, not an agent runtime or provider forecast. User rates determine cost. All runs have safety caps; summarization is billed explicitly. Branches share average context and repeated-action detection is a stated approximation.
Toolooo is part of Khizooology, created byKhizar Imtiaz (khizooo).