Late last year, my OneUp auto-posting pipeline posted the same article eight times at 2 AM. Eight platforms, eight duplicate posts on each platform. When I woke up to the phone notifications, it took me forty minutes to manually delete everything.

The lesson I learned that day was simple: building Agents isn’t hard; keeping them from going rogue is.

Over the past year, I’ve built three Agent systems. A debate engine where GPT-4o, Gemini, and Grok debate each other while Perplexity fact-checks. OneUp pipeline reads schedules from Google Sheets, generates images with DALL-E, and auto-posts to eight platforms. Our AI platform’s production monitoring automatically adjusts parameters at 3 AM. Each system taught me different lessons.

This isn’t theory. These are five principles I’ve distilled from falling into these pitfalls.

Answer Three Questions Before Coding

Before writing any line of code, I now force myself to answer three questions: What problem does this Agent solve? What are its permission boundaries? How do I know it’s working correctly?

Sounds basic, but my debate engine’s first version failed on the second question. I let the models freely decide how many debate rounds to run, and once it ran twenty-seven rounds, burning through my entire API quota. Later I added hard limits: maximum five rounds, forced convergence beyond that.

An Agent with unclear positioning is an uncontrollable black box. Draw boundaries first, then write code.

API First, Browser as Last Resort

The value of Agents lies in calling tools to complete multi-step tasks. But tool integration complexity grows exponentially.

I learned the most painful lesson on the OneUp pipeline: initially I tried browser automation for scheduled posting, and every UI update broke everything. After switching to APIs, stability jumped directly from 60% to 99%. Now my principle is simple—API stability always takes priority, browser automation only when no API is available.

Same goes for data processing. Dirty input data means Agents make decisions based on garbage. As I discussed in “Breaking Through the AI Storm,” structural design is the new core competitive advantage—and Agent structural design starts with data cleaning.

Break Into Modules, Enable Error Localization

Large Agents are hard to maintain. I now break all systems into four modules: Input processing → Decision logic → Tool invocation → Result post-processing. Each module is independently verifiable and rollback-capable.

The debate engine is designed this way. Input module parses topics and modes (dialogue/duo/adversarial). Decision module handles round control and convergence determination. Tool invocation module processes API calls to four models. Post-processing module saves results as markdown. If any module fails, it won’t cascade to break other parts.

This aligns with the collaboration principle I keep in mind: complex engineering must be broken into phases first, each phase independently verifiable. One-shot processing usually results in getting stuck midway and breaking even the parts that were working correctly.

Every Step Needs Logs

Agent operations must be transparent, otherwise you have no idea where things broke when they fail.

My OneUp pipeline’s eight-duplicate-post incident was due to lack of logging. The API returned a timeout, the script retried eight times, each time successfully scheduling a new post. If I had logged the return status of each API call, I would have discovered on the second retry that the first had actually 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 consumed). Transparency isn’t a luxury; it’s a survival condition.

Start With Minimal Scenarios

The final principle is the simplest and most often ignored: start small.

Our AI platform’s monitoring system didn’t begin by monitoring the entire production line. I first had it monitor just one parameter of one production line, ran it for two weeks to confirm the logic was correct, then gradually expanded. As I mentioned in “You’re Not Losing on Cognition,” “make something crappy first”—same applies to Agents. Run a rough POC first and optimize while operating, rather than spending three months planning a perfect system that explodes on launch.

The value of Agents isn’t in replacing humans. It’s in extending the boundaries of human capability. But extension requires first taming them—clear positioning, modularity, traceability, starting small. Each principle was earned through failure.