Late last year, my OneUp auto-posting pipeline published the same post eight times at two in the morning. Eight platforms, each with eight duplicate posts. When I woke up and saw the phone notifications, I spent forty minutes manually deleting posts.
The lesson I learned that day was simple: Agents aren’t hard to build — the hard part is keeping them from running out of control.
Over the past year I built three Agent systems. The debate engine had GPT-4o, Gemini, and Grok debate one another, with Perplexity doing the fact-checking. The OneUp pipeline read schedules from a Google Sheet, generated images with DALL-E, and auto-posted to eight platforms. Our AI platform’s production-line monitoring auto-tuned parameters at three in the morning. Each one taught me something different.
This isn’t theory. It’s the five principles I’ve distilled from the pitfalls I’ve fallen into.
Answer Three Questions Before You Start
Before writing a single line of code, I now force myself to answer three questions: What problem does this Agent solve? What is the scope of its permissions? How will I know it’s doing the right thing?
It sounds basic, but the first version of my debate engine simply didn’t think through the second question. I let the models freely decide the number of debate rounds, and one time it ran twenty-seven rounds and burned through the entire API quota. Later I added a hard cap: five rounds maximum, with forced convergence beyond that.
An Agent without clear positioning is just a runaway black box. Draw the boundaries first, then write the code.
API First, Browser as a Last Resort
The value of an Agent lies in calling tools to complete multi-step tasks. But the complexity of tool integration grows exponentially.
The most painful lesson I learned on the OneUp pipeline: at first I tried using browser automation to schedule posts, and everything broke every time the UI was redesigned. Later I switched to APIs, and stability jumped straight from 60% to 99%. Now my principle is simple — API stability always comes first, and browser automation is only considered when there’s no API available.
The same goes for data processing. If the input data isn’t clean, the Agent will make decisions on top of garbage. As I discussed in “Breaking Through the AI Storm,” structural design is the new core competitiveness — and an Agent’s structural design begins right at the data-cleaning step.
Break It Into Modules So You Can Locate the Errors
A large monolithic Agent is hard to maintain. I now break all my systems into four modules: Input processing → decision logic → tool invocation → result post-processing. Each module is independently verifiable and roll-back-able.
The debate engine was designed exactly this way. The Input module parses the topic and mode (dialogue/duo/adversarial). The decision module handles round control and convergence judgment. The tool-invocation module handles the API calls to the four models. The post-processing module saves the results as markdown. If any one module has a problem, it won’t drag down the others with it.
This is also the collaboration principle I’ve noted in my memory: complex engineering must first be broken into Phases, each independently verifiable. Trying to handle everything at once usually results in getting stuck partway and wrecking the parts you’d already gotten right.
Every Step Needs a Log
An Agent’s operation must be transparent, otherwise when something goes wrong you have no idea where it broke.
The eight-times duplicate-posting incident on my OneUp pipeline happened precisely because logging was missing. The API returned a timeout, the script retried eight times, and each time it successfully scheduled a new post. If I had been recording the return status of each API call back then, I would have noticed on the second retry that the first one had actually already succeeded.
Now all my Agents have three layers of monitoring: operation logs (what was done at each step), decision paths (why this choice was made), and cost tracking (how much API quota was spent). Transparency isn’t a luxury — it’s a survival condition.
Start From the Smallest Scenario
The last principle is the simplest and the most often ignored: start small.
Our AI platform’s monitoring system didn’t cover the entire production line from the start. I first had it monitor just one parameter on one production line, ran it for two weeks to confirm the logic was correct, and only then gradually expanded. As I said in “You’re Not Losing Because of Cognition” — “just make something crappy first.” Agents are the same: running a rough POC first and optimizing while it operates is a hundred times better than spending three months planning a perfect system that explodes the moment it goes live.
The value of an Agent isn’t in replacing people. It’s in extending the boundaries of human capability. But that extension requires you to tame it first — clear positioning, modularity, traceability, starting small. Every one of these principles was earned through failure.
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