When All Your AI Agents Are Wrong Together

Medium Article: https://medium.com/@jsmith0475/when-all-your-ai-agents-are-wrong-together-c719ca9a7f74?postPublishedType=initial "When All Your AI Agents Are Wrong Together," by Dr. Jerry A. Smith, discusses advanced architectures for achieving million-step reliability in Large Language Model (LLM) agents, building upon the foundational success of the existing MAKER system. Although MAKER demonstrates long-horizon stability using probabilistic voting, which relies on logarithmic cost scaling against exponential reliability, the article identifies three major flaws: vulnerability to correlated errors, the requirement for a fully explicit state representation, and high per-step costs. To address these limitations, the author proposes a new structure called TAC-HAVA-K, which incorporates adversarial reasoning (Thesis, Antithesis, Consolidator), hierarchical verification (Belief States, World Model, Verifier), and K-fold parallelism to create a more robust, cost-efficient, and generalizable system capable of operating in ambiguous, partially observed environments. Ultimately, the new architecture aims to achieve reliability through structural diversity of verification rather than relying solely on statistical independence.

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