Late last year, my OneUp auto-posting pipeline sent the same post eight times at two in the morning. Eight platforms, eight duplicate posts each. When I picked up my phone and saw the notifications, I spent the next forty minutes manually deleting them.
The lesson I took away that morning was simple: building an Agent isn’t hard. Keeping it from going off the rails is.
Over the past year I built three Agent systems. A debate engine that pits GPT-4o, Gemini, and Grok against each other, then hands the results to Perplexity for fact-checking. A OneUp pipeline that reads a schedule from Google Sheets, generates images with DALL-E, and auto-posts to eight platforms. A production-line monitor for our AI platform that adjusts parameters automatically at 3 a.m. Each system taught me something different.
This isn’t theory. These are five principles pulled from my mistakes.
Answer Three Questions Before You Write Anything
Before I touch a line of code now, I force myself to answer three questions: What problem is this Agent solving? What is it allowed to do? How will I know when it’s done something right?
They sound obvious. But the first version of my debate engine failed because I hadn’t thought through the second one. I let the model freely decide how many rounds of debate to run. One session went twenty-seven rounds and burned through my entire API quota. I added a hard cap afterward: five rounds maximum, then force convergence.
An Agent with no defined scope is a black box waiting to blow up. Draw the boundary first, then write the code.
APIs First, Browser Automation Last
An Agent’s value comes from calling tools to complete multi-step tasks. But the complexity of tool integration grows fast.
The most painful lesson I learned from the OneUp pipeline: I started with browser automation for scheduling posts. Every time the platform’s UI changed, everything broke. Switching to proper APIs pushed stability from 60% to 99%. My rule now is straightforward: API stability always wins. Browser automation is a last resort, only when no API exists.
The same logic applies to data. If your input data is dirty, your Agent makes decisions on garbage. I wrote in Breaking Through the AI Storm that structural design is the new core competency — and for Agents, that structure starts at the data-cleaning step.
Break It Into Modules So You Can Find What Broke
A large monolithic Agent is almost impossible to maintain. Every system I build now follows four modules: Input processing, Decision logic, Tool calls, Result post-processing. Each module is independently verifiable and independently rollback-able.
That’s exactly how the debate engine is structured. The Input module parses the topic and mode (dialogue, duo, or adversarial). The Decision module controls round count and convergence. The Tool Calls module handles API requests to four different models. The Post-processing module saves results as Markdown. When something breaks in one module, it doesn’t corrupt the rest.
This mirrors a principle I keep in my working notes: complex engineering must be broken into phases, each phase independently verifiable. Trying to handle everything at once usually means getting stuck midway and dragging down the parts that were already working.
Log Every Step
An Agent’s behavior has to be visible. Without logs, when something breaks, you have no idea where.
The eight-duplicate-post incident happened because I had no logging. The API returned a timeout; the script retried eight times; each retry successfully scheduled a new post. If I’d been recording each API call’s return status, I would have seen on the second retry that the first had already succeeded.
Every Agent I run now has three layers of monitoring: an operation log (what happened at each step), a decision trail (why that choice was made), and cost tracking (how much API quota was spent). Transparency isn’t a luxury. It’s a survival condition.
Start With the Smallest Scenario
The last principle is the simplest and the most ignored: start small.
The monitoring system for our AI platform didn’t begin by watching the entire production line. I had it track a single parameter on a single line for two weeks, confirmed the logic was correct, then expanded gradually. I said in You’re Not Behind Because of Mindset that you should “ship something rough first” — the same applies to Agents. A crude POC that actually runs will teach you a hundred times more than a perfect system that crashes on launch day.
An Agent’s value isn’t in replacing people. It’s in extending what people can do. But that only works after you’ve tamed it: clear scope, modular, traceable, starting small. Every one of those principles was paid for with a failure.
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