AI is widely used for document reading, data extraction and decision support. However, agents do not execute instructions like deterministic rule engines. They do not know the truth; they only generate predictions based on patterns. Expecting a single agent to always find the correct answer is unrealistic.
Document formats and quality vary greatly. OCR issues, misaligned tables and misinterpreted context lead to unreliable results.
Agents are not deterministic and may misinterpret even clear instructions. Assuming correctness without knowing the truth introduces risk.
A multi-agent setup does not ensure truth but reveals uncertainty and improves reliability assessment.
Instead of asking which value is correct, the question becomes which value provides stronger reliability signals such as position, format and context.
Two methods extract different total amounts from the same report: 345,820.10 and 343,820.10. Without knowing the truth, focusing on the more reliable value becomes the rational choice.
One agent cannot determine the truth. Multiple agents highlight uncertainty. This shifts the system from accuracy-driven to reliability-driven decision making.