We present Acessilia, an agentic document-parsing architecture developed for the Dr.DocBench track of the DocInsights 2026 shared task. Independent structural agents use Docling and MinerU to generate alternative canonical document trees. A Tree Differ aligns these representations using textual and spatial evidence, identifying discrepancies that are resolved by an adjudicator agent leveraging a vision-language model and visual evidence. The architecture supports hierarchical adjudication of specialised subtrees, including tables and mathematical expressions, while a deterministic serialiser converts the reconciled tree into the benchmark format. On the 509-page hidden test set, the final integrated pipeline achieves an official EvalAI overall score of 70.37, compared with 58.09 for the MinerU-only baseline, an absolute improvement of 12.28 points. Reading order shows the largest gain, increasing from 53.44 to 72.30, while text accuracy reaches 74.67 and table TEDS reaches 58.31. Formula CDM remains near zero at 0.22. These results demonstrate the effectiveness of combining complementary parser outputs through a shared canonical representation, structural alignment, and adjudication, while highlighting the need for stronger mathematical recognition and specialised processing.