The exposure
Draft — to be completed
- Claim and document review is costly and inconsistent when manual, brittle when done by fixed rules; partial evidence is common.
- “Multi-agent” is often three parallel LLM calls with no role boundaries, no evidence contract and no way to explain a decision.
Where standard controls fall short
Draft — to be completed
| PRACTICE | WHAT IT HIDES | CONSEQUENCE |
|---|---|---|
| One LLM asked to “assess the claim” | No evidence trail | Decision cannot be defended |
| Parallel calls with no critic | Contradictions between findings go unnoticed | Wrong approvals |
| LLM as orchestrator | Retries and routing become unpredictable | Cost and audit problems |
| No quorum rule | A missing worker result becomes an implicit approval | Money out on no evidence |
| Document inconsistencies (header vs body claim ID, invoice amount vs claim amount) | Missed unless a worker is tasked with them | Fraud passes |
What we recommend
Draft — to be completed
- Specialist workers with explicit responsibilities and output contracts (“risk signal + evidence-backed notes”), each with stated limits (e.g. “never infer risk from frequency alone”).
- Tools accessed through a standard protocol boundary (MCP) so data access is governed and logged.
- A critic that compares findings, flags contradictions and evidence gaps, and may request exactly one retry.
- A deterministic orchestrator (code, not a model) that dispatches, enforces retry caps and applies the verdict.
- A quorum rule: insufficient evidence falls back to deny or to human review, never to approve.
- An append-only audit trail per claim, readable by compliance.
- Human review branch for high-value or contested decisions.
OUR RECOMMENDATION
If the thing deciding which agent runs next is itself a model, you cannot promise your auditor a repeatable process. Keep the orchestrator boring.
Findings are from EonAI’s reference systems, built and tested on synthetic data. They describe patterns we see across clients’ systems, not a client engagement.