Build specs through automated multi-agent verification and review loops
I simply don't want to review AI code in depth.I used to do it but now I only review code organization and structure.Mostly I define the product behavior I want and discuss the technical implementation beforehand.Once that's in a spec I let it build in my durable implementation engine. Task: The author shifted from deep code review to defining product behavior and technical specs, then delegating implementation to a custom 'durable implementation engine.' This engine executes automated verification and multi-agent review loops across different models, triaging issues and flagging false positives until the code meets the spec. The human role is limited to initial spec definition and reviewing code organization/structure, not line-by-line logic.; tools: custom-built or unnamed agent. Human role: The original post does not say how a person stayed involved.; limitations: The source does not specify the tools, models, or infrastructure used in the 'durable implementation engine,' making exact replication impossible. It assumes LLMs require multiple passes to avoid issues, implying high computational costs and latency. There is no guidance on handling complex architectural decisions or security vulnerabilities that automated review might miss..
Tools
custom-built or unnamed agent
Published on Agent Work Atlas:
Overview
I simply don't want to review AI code in depth.I used to do it but now I only review code organization and structure.Mostly I define the product behavior I want and discuss the technical implementation beforehand.Once that's in a spec I let it build in my durable implementation engine.
Workflow
The author shifted from deep code review to defining product behavior and technical specs, then delegating implementation to a custom 'durable implementation engine.' This engine executes automated verification and multi-agent review loops across different models, triaging issues and flagging false positives until the code meets the spec. The human role is limited to initial spec definition and reviewing code organization/structure, not line-by-line logic.
Reported result
No measured outcomes, success rates, or efficiency metrics are reported in the source. The author claims the loop continues until code is 'up to spec,' but provides no data on time saved, defect reduction, or cost. It is unknown if this approach consistently produces production-ready code or requires significant manual intervention for edge cases.
Human role
The original post does not say how a person stayed involved.
Limitations
The source does not specify the tools, models, or infrastructure used in the 'durable implementation engine,' making exact replication impossible. It assumes LLMs require multiple passes to avoid issues, implying high computational costs and latency. There is no guidance on handling complex architectural decisions or security vulnerabilities that automated review might miss.
Try it
Act as a senior software engineer implementing a spec-driven AI coding workflow. 1. Define a clear product behavior spec for [replaceable feature, e.g., a user authentication module] including edge cases. 2. Use an AI coding agent to generate initial code. 3. Run automated verification scripts and a secondary AI review agent to check for logic errors and style compliance. 4. Triage issues into clusters and flag potential false positives. 5. Iterate until the code passes all checks. 6. Present the final code structure and organization for my manual review. Do not deploy or commit changes without my explicit approval. If the spec is ambiguous, ask clarifying questions before generating code. Provide a summary of issues found and fixed in each iteration.
Source reports are not independently verified. Publication date is the catalog publication date, not the original article date.