Trust infrastructure for enterprise AI agents
Give every agent a verifiable identity and govern every model execution, tool call, policy decision, cost, and outcome in a tamper-evident trust history — across any model, provider, cloud, or framework.
Built for every stakeholder
Instrument every agent once; keep identity, cost, and outcome history independent of which model runs. Compare models inside your real workflows on verified outcomes.
Ed25519-signed events in a tamper-evident hash chain, per-agent trust scoring, tool/MCP authorization, and typed security events — with cross-tenant isolation and no raw prompts stored by default.
Every model execution, tool call, policy decision, and outcome recorded with evidence and confidence levels (observed / declared / verified). ProofLedger records evidence; it does not assert regulatory compliance.
Drop-in TypeScript & Python SDKs, framework adapters, OpenTelemetry ingestion. No rewrites, no lock-in — the trust layer stays when the model layer changes.
Cost per verified outcome, value-to-cost ratios, spend by model and provider — measure what your agents produced, not just tokens consumed.
The controls
Persistent agent:// identity, Ed25519 keys, key rotation, configuration versions — independent of the model.
Provider, hosting region, jurisdiction, fallback routing, cost & latency per execution, with evidence levels.
Runtime allow / warn / block / require-approval, model-level and enterprise policy packs, human-in-the-loop approvals.
Webhook, API-response, and database-state verifiers with signed evidence — outcomes are never self-declared.
Append-only per-agent SHA-256 hash chains; any edit, deletion, or reorder is detectable.
Explainable, dimensional trust that survives model changes and feeds policy gates.
ProofLedger identifies, records, signs, verifies, governs, authorizes, traces, scores, and provides evidence. It does not eliminate hallucinations or guarantee regulatory compliance.