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Bounty Engineer Autonomous Mission Control
x402
Bounty Engineer Autonomous Mission Control is a paid API for AI agents from bounty-engineer-x402-prod.onrender.com, paid per call via x402, $79.9/call, status unknown (last checked 2026-10-12).
Plans and validates multi-agent task execution packages by routing tasks to agents, computing fragility hotspots, spend exposure, and success probability within budget and risk constraints.
Price / call$79.9
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 structured execution package including: a decision ('ready_to_execute' or blocked), ordered task assignments with selected agent details, fragility hotspot analysis with failure probabilities and expected costs, projected total spend, gross blast radius, a SHA256 package hash for integrity verification, a machine contract with execute/stop conditions, and overall mission success probability.
Example use cases
Multi-agent research and publish pipeline
“I want to run a research-then-publish workflow using two agents: agent-A handles web research for $5 at 98% reliability, and agent-B handles publishing for $20 at 95% reliability. Plan the execution order, flag any fragility hotspots, and confirm we can proceed with a $30 budget and 90% minimum success probability.”
Budget-constrained agent task assignment
“I have 5 agents and 10 tasks with a $100 budget and a max exposure of $2000 — can you assign each task to the best agent by price and success rate, compute the mission success probability, and tell me if there are any blockers before I kick off the run?”
Failure cost risk check for critical node pipeline
“Before I run this autonomous agent mission, can you check the expected failure cost and flag any critical nodes in my 4-task pipeline? I want to make sure the mission success probability is at least 85% and expected failure cost stays under $50.”
Technical details
Endpoint
POSThttps://bounty-engineer-x402-prod.onrender.com/v1/autonomous-mission-control?utm_source=zero.xyzWhen to prefer
Use this endpoint when you need to plan and validate a complex multi-agent task execution before committing any funds or contacting agents. It is ideal for pre-flight risk assessment — computing mission success probability, identifying fragility hotspots, enforcing budget caps and failure cost limits, and producing a cryptographically-signed execution package that downstream systems can verify. Choose this over simpler routing tools when your workflow has task dependencies, multiple agents with varying reliability and pricing, and hard risk constraints that must be checked before execution begins.
Side effects
- No eligible agents for a required task capability — returns blockers array with unmet requirements
- Budget cap exceeded by projected spend — returns decision other than ready_to_execute
- min_success_probability threshold not met — mission flagged as not ready
- max_expected_failure_cost_usd exceeded — stop condition triggered
- Missing or invalid required fields (tasks or agents array empty) — request rejected
- max_critical_nodes constraint violated — plan blocked
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| tasks | array | |
| agents | array | |
| budget_usd | number | |
| target_ids | array | |
| max_exposure_usd | number | |
| max_critical_nodes | integer | |
| min_success_probability | number | |
| max_expected_failure_cost_usd | number |
Output
Schema · Response
{
"type": "json",
"example": {
"ok": true,
"mode": "autonomous_mission_control",
"blockers": [],
"decision": "ready_to_execute",
"assignments": [
{
"task_id": "research",
"fallback": null,
"requires": [
"web"
],
"selected": {
"agent_id": "a",
"price_usd": 5,
"success_rate": 0.98,
"p95_latency_ms": 2000
},
"dependencies": [],
"eligible_count": 1
},
{
"task_id": "publish",
"fallback": null,
"requires": [
"publish"
],
"selected": {
"agent_id": "b",
"price_usd": 20,
"success_rate": 0.95,
"p95_latency_ms": 3000
},
"dependencies": [
"research"
],
"eligible_count": 1
}
],
"limitations": [
"This combines declared agent capabilities, prices, success rates and a caller-supplied dependency graph into a deterministic execution package.",
"It does not spend funds, contact agents, guarantee outcomes or verify candidate claims."
],
"affected_nodes": [
{
"id": "publish",
"criticality": 0.9,
"dependencies": [
"research"
],
"exposure_usd": 700
}
],
"package_sha256": "0548ec4edd3900b5b51c8b53be2387100852bbf470c3e38028dd71dfb121819a",
"execution_order": [
"research",
"publish"
],
"machine_contract": {
"stop_if": [
"selected_agent_terms_drift",
"task_graph_changes",
"budget_cap_exceeded",
"failure_cost_limit_exceeded"
],
"execute_only_if": [
"package_sha256_matches",
"budget_unchanged",
"selected_agent_terms_unchanged"
]
},
"fragility_hotspots": [
{
"id": "publish",
"required": true,
"latency_ms": 3000,
"dependencies": [
"research"
],
"success_rate": 0.95,
"declared_cost_usd": 20,
"reach_probability": 0.98,
"expected_spend_usd": 19.6,
"failure_probability": 0.049,
"declared_failure_cost_usd": 30,
"expected_failure_cost_usd": 1.47
},
{
"id": "research",
"required": true,
"latency_ms": 2000,
"dependencies": [],
"success_rate": 0.98,
"declared_cost_usd": 5,
"reach_probability": 1,
"expected_spend_usd": 5,
"failure_probability": 0.02,
"declared_failure_cost_usd": 5,
"expected_failure_cost_usd": 0.1
}
],
"projected_spend_usd": 25,
"gross_blast_radius_usd": 700,
"expected_failure_cost_usd": 1.57,
"mission_success_probability": 0.931
}
}Tags
x402
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