Your Financial Agent Passed Every Eval. Can You Defend Its Decision?

In the context of financial auditing, relying on AI agents requires more than just high performance on evaluation benchmarks; it demands defensibility. Anirudh, a security engineer at Fieldguide, argues that for agentic decisions to be trusted, they must be explainable and auditable. He proposes four core controls to ensure this: authorization, which prevents agents from modifying signed work; execution control, which requires breaking processes into traceable steps; evidence provenance, which binds claims to specific, stable document versions; and evaluation integrity, which ensures benchmarks are based on ground truth rather than outdated methodologies. Ultimately, the author emphasizes that when an agent's decision is challenged—whether in auditing, loan processing, or AML alerts—the ability to provide a transparent, verifiable audit trail is the true standard for success. Moving beyond mere correctness, developers must build systems where human oversight is integrated at critical decision points.