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Do not try to automate every task blindly. There have been cases where medical institutions attempted to automate scheduling and billing all at once, causing the entire system to crash. If you get greedy with automation, you will inevitably run into trouble. First, you must identify which repetitive tasks are unnecessary for you.
List the tasks you performed over the last two weeks that involved copying, pasting, and transferring data. Use the following formula to calculate the automation value:
V_{ ext{auto}} = rac{F imes T}{C}
By plugging in Task Frequency (F), Time Saved (T), and Complexity/Difficulty of Setup (C), it becomes clear which tasks you should tackle first. Even just selecting the top three tasks with the highest scores can help you reclaim over 5 hours of deep work time per week. As Andrej Karpathy emphasized, start with structured data tasks where the results can be immediately verified. It is better to leave areas that require human subjectivity as they are.
Automation systems are ultimately determined by how they handle exceptions. You must design how your agent reacts when data errors occur or authentication expires.
Practitioners should document the following three coping methods: First, if an API error occurs, do not retry immediately; apply exponential backoff by increasing wait times. The formula is . Second, isolate incorrectly formatted data into a manual exception log. Third, set the system to stop automatically and require administrator approval if the usage cost exceeds 1 million KRW. You can easily build these safety nets by utilizing the error handler features in platforms like Make or n8n.
If every department uses different data formats, your agent will not be able to function. Implement the 3-tier memory structure proposed by Andrej Karpathy in April 2026 within your company.
Standardize file names to 'YYYY-MM-DD_category_content' and use lowercase letters for all English text. Divide folders into Source Data, Editing Hub, and Deliverables. You do not need a complex database. Save all documents as Markdown (.md) files. According to research by Amazon Science, a well-organized, text-based search model can achieve 94.5% of the performance of an expensive vector DB at a low cost. Doing just this will double the speed of your decision-making.
An automation pipeline functions properly only when trust is built. The 'Function Growth' team increased productivity by 30% by running human workflows and agent workflows in parallel for two weeks.
Verification is simple. First, choose one small task and run it repeatedly dozens of times a day to catch errors. Second, compare the work performed by actual humans against the results generated by the agent over two weeks. Third, analyze the log.md file once a month to record what went wrong. Once the mapping accuracy of client slips exceeds 95%, you can safely deploy that automation into your daily operations.
