Cross-Checking Outputs

Cross-checking is the pattern where independent agents verify each other's work. Agent A produces analysis. Agent B produces the same analysis independently — from the same data but with no visibility into Agent A's output. Then a comparison agent evaluates both outputs side by side, flags discrepancies, and surfaces the truth.

This sounds expensive. It is. You are paying for two agents to do the same work. But the discrepancies are where the gold is. When two independent agents agree, you have high confidence in the result. When they disagree, you have found something worth investigating — either one agent made an error, or the data is ambiguous enough that two reasonable interpretations exist. Both findings are valuable.

The comparison agent is the critical component. It does not pick a winner. It analyzes the disagreement. "Agent A calculated market share at 23%. Agent B calculated it at 19%. The discrepancy appears to stem from different definitions of the addressable market — Agent A used the global TAM, Agent B used the North American SAM. Both calculations are correct given their assumptions." That level of analysis from the comparison agent tells you more than either individual analysis alone.

Cross-checking is not for every deliverable. Reserve it for high-stakes outputs where accuracy is critical and the cost of being wrong exceeds the cost of redundant computation. Financial models before a board presentation. Competitive analysis before a pricing decision. Technical assessments before a major architecture commitment.