Machine-to-Machine Debt Scanner
Inter-Agent Credit Risk 2026
Your agents may be lending to each other behind the scenes. Scan debt chains, circular loops, and swarm liabilities before cascading failure collapses the network.
Agentic Pillar capstone. ACS + ARI + MMDS triple-layer protection. Tool #10 -- completing the 10-tool Moonshot Cluster. Part of 125+ free tools.
Machine-to-Machine Debt Scanner
Enter your agent swarm debt metrics. Detect circular credit loops, inter-agent liabilities, and cascading failure risk before systemic collapse.
Number of Agents
15Total autonomous agents in your swarm with inter-agent credit lines
Average Agent Balance
$5,200Mean balance per agent wallet available for lending and settlement
Agent-to-Agent Transactions (30d)
3,400Total inter-agent transactions in the last 30 days
Outstanding Inter-Agent Loans
$28,000Total value of active loans between agents in your swarm
Circular Debt Loops
3Detected A->B->C->A circular lending chains (0 = none found)
Failed Settlements
12Inter-agent payments that failed to clear in the last 30 days
Compute-to-Task Mismatch
3/5Compute resources allocated vs actual task requirements (1 = aligned, 5 = severe mismatch)
Swarm Coordination
3/5Quality of inter-agent task coordination (1 = chaotic, 5 = orchestrated)
Agent Volatility
3/5Agent behavior variability and unpredictability (1 = stable, 5 = erratic)
Collateralization
2/5Percentage of inter-agent loans backed by compute collateral (1 = none, 5 = fully backed)
Max Exposure Threshold
60%Maximum acceptable loan-to-balance ratio before circuit breaker triggers
Multi-Agent Dependency
3/5How dependent agents are on each other for task completion (1 = independent, 5 = fully coupled)
MMDS
49/100
Exposure
35.9%
Cascade
55%
Debt Tier
MODERATE
Machine-to-Machine Debt -- Frequently Asked Questions
What is machine-to-machine debt?
Agents lending compute credits, UBC, or task priority to each other, creating hidden liability chains across swarms. These inter-agent IOUs accumulate invisibly and can cascade into systemic swarm failure.
How do circular debt loops form?
Agent A loans to B, B loans to C, C loans back to A. No external capital = systemic fragility. One default triggers cascading failure through the entire loop, amplifying debt geometrically.
What causes settlement failures?
Compute volatility, collateral shortfalls, mismatched task priorities between agents. Each failed settlement creates an invisible IOU that compounds across the swarm.
What is the MMDS score?
Machine-to-Machine Debt Score (0-100). Measures swarm financial health across 6 risk vectors: Loan Load, Circular Debt, Settlement Risk, Swarm Dependency, Compute Mismatch, and Collateral Gap.
What is cascading failure probability?
The chance one agent default triggers chain reaction through circular debt loops within 30 days. Factors: loop count, failed settlements, agent volatility, multi-agent dependency. Above 40% = imminent systemic risk.
How does MMDS relate to ACS and ARI?
ACS measures agent trust. ARI measures wallet security. MMDS measures inter-agent debt topology. Together they form the complete Agentic Pillar -- the first triple-layer machine economy risk system.
What is the collateral gap?
Percentage of inter-agent loans with no backing -- pure trust-based lending between machines. Back loans with GPU credits or UBC stake to eliminate this vulnerability.
What is the 90-day stabilization plan?
Four phases: Days 1-14 (debt mapping), Days 15-45 (loop unwinding), Days 46-75 (collateralization with GPU/UBC), Days 76-90 (circuit breakers + real-time monitoring). Target: MMDS below 40.