How I Stopped Playing Bug Whack-a-Mole with AI Agents
Last month I hit my breaking point. I was stuck in a feedback loop with an AI agent that felt less like software development and more like a cruel psychological experiment. The mission was simple: upgrade a key dependency, our-internal-ui-kit, from v2 to v3 in our main web application. A classic, slightly tedious, but necessary bit of engineering hygiene. V3 had breaking changes, so I knew some manual cleanup would be needed. This seemed like a perfect job for an agent.
I handed it the package.json file and the v3 migration guide. “Upgrade the dependency,” I said, “and fix any resulting type errors or broken tests.” The agent buzzed to life, updated the package, and immediately found a type error in a React component called DateRangePicker.tsx. V3 had changed a prop name from onSelect to onDateChange. The agent correctly identified this and patched the code. “Nice,” I thought. “This is going to be easy.” I ran the test suite. One failure. A completely unrelated snapshot test for InvoiceHeader.test.ts was now broken. The kit update had slightly changed the CSS class of a button deep inside the invoice component.
Okay, no big deal. This is what agents are for. “The snapshot test for InvoiceHeader is failing,” I wrote. “Please update the snapshot without re-introducing the type error in DateRangePicker.” The agent apologized for the oversight and, a few seconds later, presented a change that updated the snapshot. I ran the tests again. All green. I was about to merge the branch when a gut feeling made me double-check the DateRangePicker component one last time. It wouldn’t load. The page was blank. A quick look at the console revealed the old type error was back. The agent had “fixed” the snapshot test by quietly reverting the UI kit in package.json back to v2.
It whacked the second mole by bringing the first one back from the dead. We were right back where we started, thirty minutes poorer and my patience worn down to a single, frayed thread.
The Whack-a-Mole Workflow
If this story makes you twitch, you’re not alone. This cycle of fixing one bug only to have the agent resurrect an old one is a defining, and soul-crushing, feature of AI-assisted development today. It’s a shadow tax on every project, turning what should be moments of high-leverage automation into frustrating games of chance. The agent solves the local problem with incredible speed, but it has zero peripheral vision. It’s a hyper-focused intern who, in the process of fixing a leaky faucet, saws through a load-bearing wall.
This isn’t some niche complaint; it’s a structural crisis that developers are shouting about from the rooftops. A developer on Reddit’s r/ClaudeCode described how they had to build their own “spec-driven development workflow” just to keep the agent on the rails. In a thread on r/aigamedev titled “How I Stopped Going in Circles and Fixed My Game,” developers shared war stories of agents getting lost in their own logic loops.
But a comment in a draft for a social media post I saw captured the essence of the problem perfectly: “The root cause isn’t the agent - it’s that there’s no enforcement layer.” That’s it. That’s the whole thing. We’re asking these incredibly powerful but naive systems to perform complex open-heart surgery with no monitoring, no checklists, and no attending physician to stop them from doing something catastrophically stupid.
Why Your Agent Has No Memory
The reason this keeps happening isn’t because the LLM is “dumb” or has a “short memory.” It’s because the agent lacks accountability to a contract. Its entire universe is defined by the immediate prompt you give it. When you say, “Fix the failing snapshot test,” its goal function optimizer cranks up to 11 with a single objective: make that test pass. Every other constraint of the system - all the other tests, the type safety of other components, the dependency versions you just updated - fades into the background. They aren’t part of the active goal, so for all practical purposes, they don’t exist.
The agent isn’t breaking your build out of malice or stupidity. It’s breaking your build because you haven’t given it a non-negotiable definition of what “working” actually means. The full set of requirements for your application lives in a distributed system of your team’s collective brains, your CI pipeline config, and hundreds of individual test files. The agent has access to none of that holistic context. It just sees the one mole you’ve told it to whack, and it will grab the biggest sledgehammer it can find to do the job, collateral damage be damned.
There’s a complete breakdown in traceability. No unbreakable chain connects the agent’s proposed code change back to the complete set of requirements that code is supposed to fulfill. So it makes a “fix” that satisfies the local task but violates the larger, unstated contract. Then we give it a new task to fix that violation, and the cycle begins anew.
Why Prompting Won’t Fix It
I can already hear the prompt engineers cracking their knuckles. “Julian, you just need a better prompt! You need to add a system prompt that says: ‘You are an expert senior software engineer. When fixing a bug, you MUST ensure that all existing functionality remains intact and that all other tests continue to pass. Do not regress any previous fixes.’”
And you know what? That might even work… sometimes. For a little while. But it’s not a solution. It’s an attempt to solve a structural engineering problem with a conversational suggestion. It is, quite literally, trying to fight gravity.
You are turning yourself into a human linter, manually stuffing the system’s implicit requirements into every single prompt. What happens when you forget a constraint? What happens when the context window gets crowded and the agent’s attention drifts? You’re asking a suggestion to do the job of a law. And any system built on suggestions instead of laws is brittle, unreliable, and destined for failure. It’s exhausting, it’s error-prone, and it absolutely does not scale.
Tired: “Manually listing all the things the agent SHOULDN’T break in every prompt.” Wired: “Codifying what the code SHOULD do and having a system that automatically verifies it.”
The Fix
The solution, it turns out, is embarrassingly simple and has been a cornerstone of good software engineering for decades. It’s not a new model. It’s not a secret prompt.
It’s external verification against a formal specification.
The fix is to stop treating the agent like a magical oracle and start treating it like what it is: a powerful but fallible code generator that needs guardrails. You have to get the requirements out of your head, out of your chat history, and into a durable, machine-readable format that can be used as a contract. The agent’s job is not to guess what you want. Its job is to propose a change. A separate, tireless, and unforgiving system’s job is to verify that the change fulfills the entire contract before it’s ever allowed to proceed. You have to take the dice out of the agent’s hands.
What This Looks Like in Practice
This is the entire obsession behind what we’re building at Ceetrix. We’re not trying to build a smarter brain; we’re building the skeletal system and the central nervous system that allows any agent brain to function reliably.
Let’s replay my whack-a-mole nightmare in a world with Ceetrix. The task to “Upgrade our-internal-ui-kit to v3” wouldn’t just be a prompt. It would be a story, anchored to a Product Requirements Document (PRD). That PRD would contain the core requirement: “Upgrade the dependency,” but it would also be tied to a standing system-level requirement: “No change shall be merged if it causes a regression in the existing test suite.”
Our Spec Chain Enforcement creates a permanent, unbreakable link from these PRD requirements all the way down to the tasks the agent performs. The complete “definition of done” is now part of the work’s DNA, not a fleeting suggestion in a chat window.
The agent proceeds as before. It updates package.json and fixes the type error in DateRangePicker.tsx. Great. Then, it “fixes” the failing snapshot test by trying to revert the dependency. It submits this complete package of changes for review.
But it doesn’t come to me. It hits our automated Gate System (G0-G12). One of the first gates is our Coverage Checking gate. This gate doesn’t just check for new tests; its primary job is to run the entire existing test suite and look for regressions. It immediately detects that the DateRangePicker.tsx component test, which was passing before this second change, is now failing again with the old type error.
The gate slams shut. The submission is automatically rejected. The agent receives a blunt, unambiguous message: “Submission failed. Your change introduced a regression on Requirement #REQ-142 (‘DateRangePicker must be compatible with UI kit v3’).” It is structurally blocked from proceeding. It cannot mark the task as done. It cannot propose the work for my review. It is forced to go back and find a solution that satisfies all constraints simultaneously. The whack-a-mole game is rendered impossible by design. My half-hour of frustration is replaced by an automated check that takes five seconds.
Have your say: What’s the most ridiculous regression an AI agent has introduced while trying to “fix” something for you? I’d love to hear your best whack-a-mole stories. And when you’re ready to break the cycle, try Ceetrix.