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Bounty Engineer Mission Digital Twin Planner
x402
Bounty Engineer Mission Digital Twin Planner is a paid API for AI agents from bounty-engineer-x402-prod.onrender.com, paid per call via x402, $39.9/call, status unknown (last checked 2026-10-12).
Simulates an agentic mission DAG to compute execution order, expected spend, failure costs, fragility hotspots, and success probability before committing real budget.
Price / call$39.9
Success—
StatusUnknown
How this service works
Machine-buyable infrastructure for autonomous agents: API cost routing, margin optimization, funding-rate data, webpage extraction, result verification, spend controls, retry planning, state, tender search and GitHub bounty intelligence.
Output
Returns a JSON object with the computed execution order (topological sort of the DAG), per-node details (reach probability, expected spend, failure probability, expected failure cost), mission-level aggregates (total expected spend, worst-case exposure, serial latency, overall success probability), a list of fragility hotspots ranked by risk, any blockers preventing execution, a decision field ('ready' or 'blocked'), and a model SHA256 for reproducibility.
Example use cases
Pre-flight check for a bounty-hunting pipeline
“I'm about to run a 5-step GitHub bounty research pipeline with nodes for search, fetch, extract, score, and submit. Can you simulate the whole thing for me first? I want to know the execution order, expected total spend, and mission success probability before committing any real funds — keep it under a $30 budget and above 85% success probability.”
Identifying fragility hotspots in a verification chain
“My agent workflow has a webpage extraction step costing $4 (97% success) and a result verification step costing $8 (94% success, depends on extraction). Can you run a digital twin simulation on those two nodes and tell me which one is the biggest fragility risk and what my worst-case financial exposure is?”
Budget-constrained mission feasibility check
“I've planned a 4-node agent mission with a max budget of $20 and I need at least 92% chance of full success with no more than $2 expected failure cost. Here are my nodes with their costs and success rates — simulate it and tell me if it's ready to execute or if something is blocking it.”
Technical details
Endpoint
POSThttps://bounty-engineer-x402-prod.onrender.com/v1/mission-digital-twin?utm_source=zero.xyzWhen to prefer
Use this endpoint when you need a deterministic, pre-execution simulation of a multi-step agentic pipeline to avoid unexpected overspend or mission failure. It is specifically designed for agent-to-agent infrastructure planning with cost routing and failure-cost awareness, making it ideal before committing to expensive or irreversible API call chains. Prefer it over generic DAG schedulers when you need financial exposure analysis (expected failure cost, worst-case spend) and fragility hotspot ranking alongside execution ordering.
Side effects
- Invalid node DAG (cycles or missing dependencies) returns a 4xx error
- Budget or probability thresholds not satisfiable — decision field returns 'blocked' with blocker list
- Fewer than 1 or more than 60 nodes causes schema validation failure
- Malformed success_rate or cost values outside valid ranges rejected
- Service unavailable on Render.com cold-start returning 502/503
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| nodes | array | |
| budget_usd | number | |
| min_success_probability | number | |
| max_expected_failure_cost_usd | number |
Output
Schema · Response
{
"type": "json",
"example": {
"ok": true,
"mode": "mission_digital_twin",
"order": [
"collect",
"verify"
],
"details": [
{
"id": "collect",
"required": true,
"latency_ms": 1000,
"dependencies": [],
"success_rate": 0.98,
"declared_cost_usd": 4,
"reach_probability": 1,
"expected_spend_usd": 4,
"failure_probability": 0.02,
"declared_failure_cost_usd": 2,
"expected_failure_cost_usd": 0.04
},
{
"id": "verify",
"required": true,
"latency_ms": 1800,
"dependencies": [
"collect"
],
"success_rate": 0.96,
"declared_cost_usd": 8,
"reach_probability": 0.98,
"expected_spend_usd": 7.84,
"failure_probability": 0.0392,
"declared_failure_cost_usd": 20,
"expected_failure_cost_usd": 0.784
}
],
"blockers": [],
"decision": "ready",
"assumptions": [
"Declared node success rates are treated as independent for mission-level probability.",
"Costs, failure costs and latencies are caller supplied.",
"This is a deterministic planning model, not a guarantee of real-world execution."
],
"model_sha256": "9ef1bcb97d905fd56657e11bc9057034027150dbea5abdc48a2bc7e3c7ae9abb",
"expected_spend_usd": 11.84,
"fragility_hotspots": [
{
"id": "verify",
"required": true,
"latency_ms": 1800,
"dependencies": [
"collect"
],
"success_rate": 0.96,
"declared_cost_usd": 8,
"reach_probability": 0.98,
"expected_spend_usd": 7.84,
"failure_probability": 0.0392,
"declared_failure_cost_usd": 20,
"expected_failure_cost_usd": 0.784
},
{
"id": "collect",
"required": true,
"latency_ms": 1000,
"dependencies": [],
"success_rate": 0.98,
"declared_cost_usd": 4,
"reach_probability": 1,
"expected_spend_usd": 4,
"failure_probability": 0.02,
"declared_failure_cost_usd": 2,
"expected_failure_cost_usd": 0.04
}
],
"worst_case_exposure_usd": 34,
"expected_failure_cost_usd": 0.824,
"declared_serial_latency_ms": 2800,
"mission_success_probability": 0.9408
}
}Tags
x402
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