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Attest Dojo builds AI agent loops for verifiable correctness

Engineers at Attest Dojo have developed a system called Kaizen Harness that implements "loop engineering" for AI agents, a concept recently highlighted by Anthropic and OpenAI. This approach focuses on creating iterative systems where AI models prompt each other to achieve verifiable correctness, rather than relying solely on direct human prompting. Kaizen Harness utilizes three distinct loops: a council debate loop for architectural decisions, a PRD review loop for product development, and a code verification loop for automated patching, with swarming techniques employed to accelerate parallel tasks within these loops. AI

IMPACT Accelerates AI agent development by providing a framework for verifiable correctness and automated iteration.

RANK_REASON The article describes a system built by a third party that leverages concepts discussed by major AI labs, rather than a direct release from a frontier lab.

Read on dev.to — LLM tag →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

COVERAGE [1]

  1. dev.to — LLM tag TIER_1 English(EN) · AttestDojo ·

    We Built the Loops Both Anthropic and OpenAI Are Now Telling Engineers to Write. Here's the Architecture.

    <p><em>Previously: <a href="https://dev.to/attestdojo/kaizen-harness-patterns-for-making-ai-agents-reliable">Kaizen Harness: patterns for making AI agents reliable</a> (June 9) and <a href="https://dev.to/attestdojo/we-cut-our-ai-agent-costs-by-60-heres-what-worked">We Cut Our AI…