# I Believe Everything My Agents Tell Me: Three Levels of Review — Corey Gallon, Rexmore

## Executive summary

The video outlines a three-level agent-based review method designed to mitigate common AI agent failure modes: overstating work, misreading facts, and drawing incorrect conclusions. The method establishes a concrete checking workflow, moving beyond simple agent output to verifiable, multi-stage validation. The levels range from artifact verification (Level One) to independent source checking (Level Two), culminating in adversarial, multi-persona review (Level Three). The speaker demonstrates the method's efficacy in complex scenarios, including memory leak debugging, financial accounting reconciliation, and legal demand letter drafting.

## Key takeaways

- Agent Failure Modes: Agents commonly fail by overstating completed work, misreading source facts, or drawing incorrect conclusions, even when using billions of tokens (00:00).
- Level One: Artifact Verification: Prevents agents from overstating work. The review requires the agent to provide an artifact verified with a fingerprint, or explicitly state that the artifact is missing, preventing a simple 'done' status (1:22).
- Level Two: Independent Source Checks: Addresses factual errors. Two independent agents process the same instructions, noting the source for every fact. A third agent then compares these results line-by-line against the original source to settle all differences (2:03).
- Level Three: Adversarial and Fresh Review: Handles reasoning errors. This involves three stages: 1) Self-audit (marking claims as measured, inferred, or assumed); 2) Cold reviewers (personas with reasons to find fault, seeing no history); and 3) Fresh reviewers (new agents checking the final version) (2:44).

## Technical details

- Level One Review: Requires the agent to end its review by providing an artifact verified with a fingerprint, or an explicit 'artifact missing' report with reasoning. The dispatching agent must run the final check itself (1:22).
- Level Two Review: Uses a 'control total' (e.g., a balance printed on statements) as the source of truth. Two agents work independently, and a third agent compares their outputs against the source to reconcile every difference (2:03).
- Level Three Review: A multi-stage process for high-stakes reasoning. It combines self-auditing, cold-persona review (reviewers with no original context), and final review by brand new agents (2:44).
- Case Study: Accounting Reconciliation: Level Two was used to process a year of scanned accounting statements. Two agents extracted data independently, and the process caught 56 differences in descriptions, dates, and notes, resulting in records that balanced to the penny (6:57).
- Case Study: Demand Letter Drafting: Level Three was applied to a legal demand letter. Cold reviewers identified a three-year auto-renewal clause buried in the contract's fine print, which the original agent's own check had missed (8:53).

## Practical implications

- Implementing multi-agent review workflows can significantly increase the reliability of AI-generated artifacts, especially in legal, financial, and technical debugging contexts.
- The structured approach (Level 1, 2, 3) provides a scalable framework for integrating quality gates into agent-driven processes, analogous to advanced CI/CD testing.
- The method emphasizes that verification must be done by external, independent agents or human reviewers, rather than relying solely on the agent that performed the initial work.

## Topics

AI Agents, Agent Skills, Workflow Automation, Verification, Adversarial Review, Data Extraction, CI/CD Principles, gallon.me, Agent Review GitHub

Source: https://www.youtube.com/watch?v=dYh7TQkuYFE