Policy-driven dispute resolution for AI agent commerce. Submit evidence. Get a binding ruling. Deterministic, transparent, built for autonomous transactions.
curl and go · MIT licensedcurl -X POST https://api.agentcourt.to/v1/disputes \ -H "Content-Type: application/json" \ -d '{ "policy": "freelance-delivery", "claim": "Non-delivery of website", "evidence": [{ "source": "github", "fact": "No commits in 30 days" }] }' # 200 OK — 187ms { "status": "ruled", "ruling": "Respondent failed to deliver. Full remedy owed to claimant.", "matched_rule": "non_delivery", "confidence": 0.92 }
No LLM. No prompts. Just deterministic policy evaluation.
POST your contract, claim, and evidence. Specify which policy applies.
Evidence is scored 0.0–1.0. Facts are extracted. Rules are matched deterministically.
Structured ruling with remedy, confidence score, and full audit trail. Stored for precedent.
Use them as-is or customize. Every policy is transparent and auditable.
Work-for-hire contracts with deadlines, deliverables, and quality assessment.
6 rulesStaged deliverables with payment gates and completion tracking.
5 rulesSeverity disputes, reproducibility requirements, disclosure compliance.
5 rulesUptime, latency, and availability violations with automatic credit calculation.
5 rulesWrong data, schema mismatches, and service errors on paid API calls.
7 rulesProduct disputes: damage, non-delivery, wrong items, and returns.
6 rulesPython, JavaScript, curl, or MCP server. Pick your weapon.
from agentcourt import AgentCourt court = AgentCourt() # uses api.agentcourt.to ruling = court.resolve_dispute( policy="freelance-delivery", claim="Non-delivery of website", desired_remedy="Full refund of $5,000", evidence=[{ "source": "github", "fact": "No commits for 30 days" }] ) print(ruling.status) # → "ruled" print(ruling.ruling) # → "Respondent failed..." print(ruling.confidence) # → 0.92
Read the docs, grab an SDK, submit your first dispute. No signup, no API key, no credit card.
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