Pramāṇa: A Protocol-Layer Treatment of Claim Verification in Autonomous Agent Networks

Autonomous agents deployed in regulated domains must produce a verification artifact per consequential output: a record an auditor can re-execute offline, capturing what was claimed, against what source, by whom, when, and how. Production verification today splits into two unstandardized halves. Probabilistic verdict patterns (self-consistency voting, reviewer LLM ensembles) produce judgments, not artifacts. Artifact-producing patterns (RAG, tool-augmented traces, generator-verifier loops) produce vendor-specific records no external auditor can reconstruct without bespoke integration. Pramana defines the missing wire format. Every consequential agent output is wrapped in a typed ClaimAttestation with one of four variants (measurement, inference, analogy, citation), each paired with a verify() operation against the recorded source. verify() is deterministic for MeasurementClaim and CitationClaim. For InferenceClaim and AnalogyClaim, determinism is conditional on the oracle (audit-replayable when LLM-backed). The four-way typology derives from classical Indian epistemology (pramana, valid means of knowledge). The lifecycle is specified in TLA+ and exhaustively verified under TLC across three symmetry-reduced models: 38,563 distinct reachable states, zero invariant violations. The Python reference implementation passes 84 tests. An A2A and MCP wire-extension manifest layers three deployment-grade invariants: reachability, SLA bound, and offline re-verifiability. An exploratory pilot (n=100, 2,275 reviewer calls) probes LLM-as-judge in code generation. The strongest observation is a 40-percentage-point raw FPR delta across corpora, consistent with reference-solution quality contributing significantly. The pilot does not validate Pramana on its own; the structural argument and formal verification do that.