The verifiability litmus test for agent design

Agent design should be guided first by whether a task’s outcomes can be reliably verified, rather than by automatically increasing model size, context, or sampling budgets. For verifiable tasks such as single-answer agentic search, systems can improve through candidate selection, calibrated confidence, and independent verification, with experiments showing that cheaper specialized verifiers can substantially outperform majority voting. For tasks where completeness cannot be checked, including deep research reports and RAG indexing before queries are known, diversity and aggregation are more effective because useful facts or retrieval needs are distributed across multiple attempts and granularities. Agentic coding combines both conditions: repository exploration is a coverage problem requiring parallel, diverse search, while final patches can be evaluated through tests, making stronger models most valuable at the final generation stage. Across these examples, oracle experiments, pass@k comparisons, verifier cost analyses, and coverage measurements are presented as inexpensive ways to identify architectural inefficiencies and allocate compute where it produces the greatest benefit.

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