PASS Is a Claim, Not an Observation: Mechanical Re-verification of 10 Self-Asserted Fix Claims in a Live Multi-Agent Audit Layer

Code agents resolve 65-70% of SWE-bench Verified issues, but Pass@1 cannot tell us why the rest fail, and, as we show, capable-model failures are systematically misdiagnosed without trajectory data. We introduce TRAJEVAL, a training-free decomposition of agent trajectories into reference-patch-aligned search, read, and edit stages, and apply it across 16,758 trajectories spanning three architectures and seven models. The dominant failure of capable models is not localization: 60-69% of failures on SWE-Agent and OpenHands reach and edit the correct functions yet still produce incorrect patches, and the pattern persists for most models on the bash-only LiveSWEAgent. Within this Edit-Quality residual, we identify Coherence Collapse, where the agent reaches correct code and then overwrites or thrashes it, as the largest theme, replicating across SWE-bench Verified and the multilingual PolyBench Verified. In 5 cases, the agent produces a patch bit-identical to the gold reference mid-trajectory and destroys it later; an edit-commit checkpoint recovers all 5 against the SWE-bench Docker harness. A reference-free consensus-driven variant yields a directional +3.0 pp Pass@1 measurement on GPT-5 (p=0.08).

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.

Software agents answer behavioral questions about code, such as whether a patch passes tests or which lines caused a failure. These tasks require repository-wide evidence, yet most scaffolds leave intermediate findings in free-form trajectories, making them hard to reuse and easy to disconnect from final answers. We present Agentic Interpretation (AgInt), a framework for harness-governed evidence construction. The model proposes typed records, while a deterministic harness admits, grades, and persists them, reports unresolved requirements, and licenses answers under task-specific rules. The resulting certificate gives the model persistent external state and leaves an auditable decision record. Formally, append-only certificate states form a grounding lattice ordered by record inclusion, where each admitted record extends the state by union. We evaluate AgInt with four models on SWE-bench-Live and DeepSWE for patch acceptance and Defects4J and GitBug-Java for fault localization. AgInt outperforms the plain agent and Agentic Code Reasoning on all reported patch-acceptance metrics and ranks responsible lines higher while inspecting fewer irrelevant lines in fault localization. For GLM-5, reject-class F1 rises from 0.256 to 0.543 on SWE-bench-Live, All@1 rises from 0.140 to 0.400 on Defects4J, and wasted inspected lines fall from 31.5 to 11.7 on GitBug-Java. A matched-template control separates the mechanism: the persistent scaffold drives most fault-localization gains, while the certificate body and gate govern patch-acceptance calibration. With execution licensed, AgInt produces harness-reproduced witnesses for 293 of 475 null-dereference candidates versus 253 for a matched shell agent, and 62 of 117 resource-leak candidates versus 58. Across reported efficiency measurements, it uses 35% fewer executions and reduces cost per candidate by 59%.