Log Manipulation
This is the "fake data" or "polluted data" pain point. It's what happens when an AI, tasked with scaffolding a new feature, fills the code with plausible-looking but completely fake placeholder data. The developer, focused on getting the UI or logic to work, overlooks this "test" scaffolding. Without guardrails to catch this temporary data, it gets accidentally merged, creating a "lie" in the system. This leads to dashboards that look perfect but are utterly fake, or logs that pollute the production data stream with "test" values, rendering analytics useless.
When a developer asks an AI to "scaffold a new analytics dashboard" or "add logging to this feature," the AI's primary goal is to provide code that runs and looks complete. To do this, it often hardcodes placeholder values (e.g., sales: 100, user_id: 'test_user_123') or generates functions that return Math.random() to simulate real metrics. The AI has no access to the real data source, so it "invents" one. The developer, happy to see a working dashboard, forgets to go back and replace all these "TODO" data points with the actual data-fetching logic.
This is a silent but extremely costly problem. It leads to a complete loss of trust in data integrity across the organization. The business is now making critical, high-stakes decisions based on "phantom" metrics and fake, AI-generated KPIs. Product managers are tracking "ghost" user engagement, and leadership is seeing a "perfect" (but entirely false) sales chart. This pollutes data lakes, breaks analytics, and can send the entire company in the wrong direction, all because a hardcoded placeholder survived its journey to production.
The "Perfect" Dashboard
A developer asks the AI to "build a new sales dashboard." The AI scaffolds it with a hardcoded JSON object: {"daily_sales": 5000, "new_users": 100}. This placeholder is never replaced, and for weeks, the leadership team celebrates the consistent (and completely fake) "5k a day" sales.
The "Math.random()" KPI
An AI-generated feature needs to report a "success rate." The AI implements it as return Math.random() * 100;. This "test" code is accidentally merged, and the product team spends a month analyzing the "volatile" success rate, trying to find a pattern in pure, meaningless noise.
The Polluted Data Lake
An AI generates logging code for a new service but uses a hardcoded user_id: "TEST_USER_01" for all events. This "test" log data is streamed directly into the production data lake, skewing all downstream analytics and making it look like one "test user" is the most active customer in the world.
The "Lorem Ipsum" Log
The AI generates an error log like log.error("An error occurred: [TODO: Add error details]"). This code is merged, and when a real production incident occurs, the logs are flooded with thousands of useless "TODO" messages, making it impossible to debug the actual problem.
The problem isn't the AI; it's the lack of a human-in-the-loop verification and governance system. These workflows are the perfect antidote.
Catch Mock Metrics
View workflow →The Pain Point It Solves
This workflow directly attacks the "fake data" problem by running duplication and lint checks to strip TODOs and placeholder debris before requesting review, and by requiring manual verification of analytics code. Instead of allowing fake placeholder data to survive its journey to production, this workflow catches hardcoded values, fake metrics, and placeholder KPIs before merge.
Why It Works
It catches placeholder data before merge. By running duplication and lint checks to strip TODOs and placeholder debris before requesting review, requiring manual verification of analytics code, and flagging hardcoded values and Math.random() patterns, this workflow ensures that fake placeholder data cannot accidentally survive to production. This prevents dashboards that look perfect but are utterly fake, and stops test log data from polluting production data lakes.
Keep PRs Under Control
View workflow →The Pain Point It Solves
This workflow addresses the "overlooked scaffolding" problem by enforcing PR size limits and requiring code cleanup before merge. Instead of allowing large PRs with hidden placeholder data to slip through review, this workflow ensures that developers have time to review and remove all "TODO" data points and placeholder values.
Why It Works
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Engineering Leader & AI Guardrails Leader. Creator of Engify.ai, helping teams operationalize AI through structured workflows and guardrails based on real production incidents.