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12 articlesThe Eval Debt Ratchet: How Teams Get Buried Cleaning Up What They Shipped on Vibes
Skipping evaluations when shipping AI features creates compounding debt that locks teams into untestable behavior. Here's how the ratchet effect works and how to pay it down without halting feature work.
The Eval Fatigue Cycle: Why AI Quality Measurement Collapses After Launch
Most teams launch with comprehensive AI eval suites and abandon them within six weeks. Here's why the collapse is structurally inevitable — and how to prevent it.
The Eval Overcrowding Problem: Why Your Bigger Test Suite Is Catching Fewer Regressions
Growing your AI eval suite often makes it worse at catching real regressions. Here's why eval suites drift toward engineering-convenient edge cases — and the forced-ranking methodology that keeps them predictive.
Feature Interaction Failures in AI Systems: When Two Working Pieces Break Together
AI capabilities that pass every individual test can fail silently in combination. Here's how to audit the seams before users find them.
The Federated AI Team: Why Centralizing AI Expertise Creates the Problems It Was Supposed to Solve
Central AI platform teams promise standardization and governance but routinely become bottlenecks, knowledge silos, and sources of the fragmentation they were meant to prevent. Here's what the failure looks like and what federation actually requires.
Fine-Tuning Data Saturation: When Adding Examples Makes Your Model Worse
Adding more training examples is the default response to a fine-tuning plateau — and often the wrong one. How to detect data saturation early, and the four alternatives that actually break through it.
The First-Mover Disadvantage in AI: A Framework for Timing Your AI Feature Launch
Moving fast in AI can kill your product faster than any competitor. A practical decision framework for timing AI feature launches based on the gap vs. layer distinction, moat accumulation, and model improvement velocity.
The Frozen Feature Trap: When Your AI Differentiator Becomes a Maintenance Anchor
Early AI differentiators — custom fine-tunes, bespoke retrieval pipelines, hand-crafted prompt chains — calcify into technical debt as base models improve. Here's how to recognize the transition and build a framework for retiring them.
Function Calling vs Code Generation for Agent Actions: The Tradeoffs Nobody Benchmarks
Most agent benchmark papers measure function selection accuracy. The production tradeoffs that actually matter — safety surface, debugging cost, parsing failures, and irreversibility — are rarely compared. Here's the framework engineers need.
The Generalization Cliff: How Fine-Tuning Creates Silent Capability Regressions
Fine-tuning a model on a narrow task silently degrades capabilities on adjacent tasks your team never tested. Here's how to detect, measure, and prevent the generalization cliff.
Ghost Context: How Contradictory Beliefs Break Long-Running Agent Memory
Persistent agent memory stores accumulate contradictory facts over time — and most systems retrieve them together without warning. Here's what that failure looks like in production and the patterns that prevent it.
The Helpful-But-Wrong Problem: Operational Hallucination in Production AI Agents
Factual hallucination gets the headlines, but there's a more insidious failure mode: AI agents that are directionally plausible but operationally wrong. Wrong API flag, stale method signature, correct concept wrong instance — and your evals won't catch it.
About Tian Pan
I'm Tian Pan, an engineer-founder focused on agentic engineering — building autonomous AI systems and scaling engineering teams. I write practical guides on system design, technical leadership, and shipping with AI agents. Previously an early engineer at Uber, Brex, and IoTeX.
