The Agent Trust Fabric is an open protocol for cryptographically verifiable AI agent authority governance — PQC-secured, formally specified, independently verifiable offline by any auditor.
Every regulated AI deployment faces the same trio of unresolved challenges. ATF is a protocol designed to address all three with cryptographic guarantees.
When an autonomous agent acts, there is typically no signed, auditable record linking that agent's authority to a human principal. Who authorized it? What scope? When did it expire?
Autonomous sessions degrade mid-execution — temporal windows close, authority budgets are consumed, context drifts. No formal mechanism detects illegitimacy before the next decision.
Decision evidence typically exists only in live databases. After migration, decommissioning, or a security incident, the ability to independently verify past decisions is permanently lost.
Six layers. Authority propagates downward from human to agent. Evidence propagates upward — every artifact PQC-signed, independently verifiable.
Each RFC extends the previous without replacing it — a layered invariant stack that grows formally verifiable.
Defines the Agent Identity Record, Delegation Receipt, Trust Lattice, and Monotonic Authority Reduction invariant. Every delegation produces a PQC-signed artifact verifiable by any party without platform access.
Extends RFC-ATF-1 into the full execution lifecycle. Introduces the Continuity Eligibility Score, Authority Fragmentation Guard, and Escalation Protocol — covering temporal expiry, budget exhaustion, context drift, and integrity degradation mid-execution.
Addresses what happens to governance evidence after execution. GPIL for cross-runtime compatibility, the Evidence Archive Pipeline for immutable retention, and the OEP format for forensic deliverables verifiable by regulators years after decommissioning.
A real-time composite metric [0–100] quantifying the runtime health of an agent's authorization. Sampled at every governance decision point. Protocol invariant — weights are immutable.
Grouped by protocol family. Model-checked properties — not configuration. Any implementation claiming compliance must enforce all invariants in its declared profile.
Progressive conformance tiers. Each profile has a defined set of invariants, a conformance test suite, and a badge implementors can include in their documentation.
Permanently archived specifications and research artifacts with academic citation infrastructure.
First publication of the ATF delegation protocol. Defines AIR, DR, Trust Lattice, and 6 core invariants. Includes TLA+ formal specification.
Extension specifying CES, AFG, Escalation Protocol, and 8 RGC invariants. Full formal model-checking specifications.
GPIL taxonomy, EAP pipeline, OEP format, and 26 new invariants. Defines the forensic verification standard.
5 formal properties model-checked against the ATF specification. Included in Zenodo v1.0.0 archive.
ATF addresses requirements across the major regulatory frameworks governing AI systems in 2026.
Article 13 (Transparency) and Article 14 (Human Oversight) for high-risk AI systems. DR chains provide the signed, auditable human oversight record mandated by Art. 14.
Implements the GOVERN function — accountability, traceability, and organizational responsibility for AI decision-making. CES provides continuous measurable accountability.
Aligns with DFSA MKT and CIR rules for audit trails and technology risk management in financial services. Evidence Package (OEP) satisfies forensic audit requirements.
All delegation receipts and evidence artifacts are signed with ML-DSA-65 (Dilithium-3), the NIST-standardized post-quantum digital signature algorithm. PQC-hardened by design.
ATF is relevant to any organization deploying autonomous AI agents in contexts where authorization provenance, execution continuity, or forensic evidence retention are material requirements.
Multi-agent frameworks, LLM orchestrators, and autonomous workflow engines that need to prove every agent action was within explicitly delegated scope.
Get ATF-Compliant →Trading algorithms, credit decisioning, and risk management systems operating under DFSA, FCA, or MiFID II — where agent authority chains are regulatory evidence.
Get ATF-RGC-Compliant →Clinical decision support, autonomous diagnosis support, and safety-critical systems under EU AI Act Article 6 high-risk classification.
Get ATF-FEI-Compliant →Government, defense, and national AI initiatives requiring cryptographic proof of human control over autonomous decision chains, resistant to post-quantum adversaries.
Get ATF-FEI-Compliant →Reference implementation, conformance test vectors, JSON schemas, and verifier tools — all in one open repository.
Start with RFC-ATF-1 for the delegation protocol. Each RFC is self-contained and builds on the previous.
Browse RFC Index →Python reference implementation with full test suite. Install, verify examples, run conformance vectors.
View on GitHub →Use the public verifier to validate ATF receipts — checks content hash, MAR invariant, CES formula, and field structure.
Open Verifier →Run the conformance test suite against your implementation and declare your profile tier in your documentation.
Conformance Program →