Pramana defines a typed ClaimAttestation protocol with four variants and verify operations, specifies its lifecycle in TLA+, model-checks it with TLC, and provides a tested Python implementation for auditable agent claims.
Anumati: Proof of Adherence as a Formal Consent Model for Autonomous Agent Protocols
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abstract
As autonomous AI agents increasingly call other agents to complete tasks on behalf of a human principal, a structural accountability gap has emerged: the calling agent accepts the terms of service of the callee without any protocol-level mechanism to prove that it understood those terms or that it subsequently honoured them. Authentication protocols such as OAuth and mutual TLS establish who may call which capability. They do not address under what conditions a permitted call may be made, and those conditions change as the callee's policies evolve. In this paper we formalise the distinction between proof of acceptance (a timestamped acknowledgement) and proof of adherence (a per-action reasoning record citing the specific clause evaluated). We propose three primitives (PolicyDocument, ConsentRecord, and AdherenceEvent) that together constitute a versioned, append-only consent model for agent-to-agent communication. The model is instantiated as a non-breaking extension to two widely used agent protocols: the Agent2Agent (A2A) protocol and the Model Context Protocol (MCP). A TLA+ specification of the consent lifecycle, together with a reference Python implementation of the chain integrity and adherence trail validators, is available in the accompanying repository.
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cs.CR 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
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Pramana: A Protocol-Layer Treatment of Claim Verification in Autonomous Agent Networks
Pramana defines a typed ClaimAttestation protocol with four variants and verify operations, specifies its lifecycle in TLA+, model-checks it with TLC, and provides a tested Python implementation for auditable agent claims.