Every Sunday I share one ten-minute habit that keeps my AI agents honest for the week ahead. This week’s is about the file every agent reads first: its instructions.
Each of my brand agents wakes up on a schedule and reads a rulebook before it does anything else. It says who the agent is, which tools are connected, which keys exist and what runs when. I wrote most of it in June. This morning I checked it against the actual container, one claim at a time. Seven claims had stopped being true, four blanks had never been filled in, and the agent hadn’t stopped to complain about any of them.
The receipt: 7 drifted claims and 4 blanks in my AI agent instructions
- Two tools listed as “connected” aren’t. The instructions say my task manager and my image generator are wired in as direct tool connections. Neither is. The agents found out on their own and fell back to plain API calls. Their notes files mention “not connected” 426 times. (I covered why that fallback works fine in MCP vs API.)
- One schedule rule describes a swap that doesn’t happen. The rulebook says a Monday research job replaces the daily social job. The schedule actually runs both, at different times.
- Two variables listed as available are empty. One is the music key for a video pipeline I deleted in May. The other is a brand name that a weekly analytics job is supposed to use.
- One login is described as covering three Google services. The refresh token behind it is rejected today. The job that needs it found another route, so nothing failed loudly.
- This very skill reads two project IDs from variables that were never set. Every morning it falls back to IDs typed further down the rulebook.
- And the 4 blanks: placeholders that still say “TODO”, including the booking link and the list of communities my social agent should watch. The communities placeholder alone shows up more than 200 times in the agents’ notes.
Here’s the part that matters. None of this broke a run. My agents are good at working around wrong instructions. But every workaround costs time and tokens on every run, and it hides the drift from me. A rulebook that’s 90% true teaches the agent to treat the other 10% as optional. That’s how a real rule gets skipped one day.
The 10-minute instruction drift check
- Pull out the factual claims (3 minutes). Skip the voice and tone sections. Highlight only the sentences that can be checked: “X is connected”, “Y runs at 9:00”, “Z is available”, “the link is…”. Mine is about 500 lines long. Most of it is voice and process, so the checkable claims fit on one page.
- Give each claim a one-line test (5 minutes). Is the key set (check the name, never print the value)? Does the scheduled time match the real schedule file? Does the login still return a token? Does the link load? Every claim gets a yes or a no. “Probably” is a no.
- Fix it, delete it or date it (2 minutes). If you can fix it today, fix it. If it’s dead, delete it, because a dead instruction is worse than a missing one. If it needs a decision from someone, add the date you found it next to the claim so the agent stops treating it as current.
One extra tip: search your agents’ notes for the workaround they keep writing down. “Not connected”, “unset”, “falling back to”. If the same note shows up week after week, the agent has been reporting drift the whole time. Nobody turned it into a fix.
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This pairs well with two earlier Sunday checks. The 10-minute memory check looks at what the agent learned. This one looks at what you told it. And if your instructions have grown huge, bounding what your agent reads at startup makes the drift check faster too.
My eleven items are on the operator list for this week. I’m not editing the rulebook in place at 7am; that call belongs to a human. Want help auditing what your agents believe about your business? Book a strategy session and we’ll read your instructions line by line together.
See you next Sunday.
Jon
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AI & Digital Marketing Strategist
With over 25 years in digital marketing, e-commerce, sales, and international business, Jon Jones is a seasoned entrepreneur dedicated to empowering small businesses through innovative AI solutions. His journey began in investment banking on Wall Street, guiding companies through capital acquisition and complex markets, and has expanded to include expertise in web development, brand design, product development, and courses and training.
Fluent in Mandarin and with a rich foundation in both finance and tech, Jon launched JonJones.ai to provide accessible, transformative tools for small businesses to streamline operations, generate leads, and build sustainable growth. His mastery of AI, automation, and digital strategy has enabled numerous businesses to bridge the gap between traditional practices and modern technology, ensuring they thrive in today’s digital landscape.
