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Apply the Four Condition Test
Before building a loop, verify the task is repeatable and has a clear definition of done. The process must have access to the necessary tools, such as web browsers or code editors, to execute the work. Because loops consume more tokens than single prompts, only automate workflows where the time savings outweigh the compute costs.
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Adopt Skill Driven Orchestration
Do not attempt to build a complex loop in a single prompt. Create modular, battle-tested execution skills for every sub-task first. Once these individual components are reliable, use a separate orchestration prompt to call these skills in a specific sequence.
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Bridge Abstract Goals to Binary Outcomes
Loops require a clear signal to stop. For subjective tasks like drafting emails or writing code, create a reviewer skill that outputs a pass or fail score. This forces the AI to act as its own quality control, iterating until the output meets your pre-defined threshold.
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Implement Persistent Markdown Memory
AI agents lose context between iterations unless you provide a place to store notes. Require your loop to write its progress and failures to a simple markdown file. The agent should read this file at the start of every cycle to avoid repeating the same mistakes and wasting tokens.
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Start with Loop Training Mode
Never let a new loop run fully autonomous on day one. Build a manual approval step into the workflow where the agent must pause and wait for your feedback. Once the logic proves stable over several runs, you can remove the human-in-the-loop checkpoint.
Why it matters
Small business owners often become the bottleneck when they have to manually prompt and check every AI interaction. Shifting to loop engineering allows you to build reliable systems that run in the background. This moves your role from a task operator to a systems architect, enabling you to scale output without increasing your personal workload.