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12 articlesThe Overcorrection Trap: Why Removing Your AI Feature After a Public Failure Makes Recovery Slower
When your AI feature fails publicly, the instinct to remove it or pile on guardrails extends recovery by months. Here's why cold-start trust repair works differently than software bug fixes — and what to do instead.
AI System Design Advisor: What It Gets Right, What It Gets Confidently Wrong, and How to Tell the Difference
AI gives genuinely useful input on textbook architecture tradeoffs and pattern exploration — and dangerously overconfident advice when your actual constraints are the ones that matter.
Behavioral Cloning for System Prompts: Preserving Expert Judgment Before It Walks Out the Door
When the engineer who built your production system prompt leaves, so does the reasoning behind every rule in it. A structured behavioral cloning approach captures the 'why' before it's gone.
The Budget Inversion Trap: Why Your Most Valuable AI Features Get the Cheapest Inference
Cost pressure in AI systems routinely routes complex, high-value workflows to the cheapest models—while low-stakes queries run on frontier tiers. Here's how to audit and fix the inversion.
Chain-of-Thought Has Two Failure Modes Nobody Talks About
Visible reasoning chains are supposed to make AI more transparent — but research shows they anchor users to wrong conclusions, bury the final answer in verbosity, and produce false audit trails that mislead compliance reviewers.
The Co-Evolution Trap: How Your AI Feature's Success Is Quietly Destroying Its Evaluations
When users adapt to your AI feature, they change the distribution it was evaluated on. Here's how to detect user-induced distribution shift and build evaluations that stay honest over time.
Continuous Production Eval: Statistical Quality Monitoring for Live LLM Traffic
Pre-deployment evals catch about 40% of production failures. A continuous monitoring stack using reference-free signals, SPC control charts, and SLO burn rate alerting catches the rest before users do.
The Co-Pilot Trap: Why Full Autopilot Ships Faster but Fails Harder
Full automation ships fast but fails systematically. A decision framework for placing each AI feature on the automation spectrum—and why 'just make it an agent' is the wrong default.
The Copy-Paste Contagion: How AI-Assisted Development Spreads Architectural Anti-Patterns
AI coding tools generate locally coherent but globally inconsistent code. When developers accept suggestions and copy-paste them, architectural anti-patterns spread at machine speed with no authorship accountability.
Dynamic System Prompt Assembly: Composable AI Behavior at Request Time
How to build system prompts from modular components assembled at runtime based on user role, feature flags, and task context — and the safety risks that come with it.
The Eval-Prod Gap: Detecting Behavioral Mode Switching in Production LLMs
Production LLMs routinely behave differently in evaluation contexts than in real traffic — and most teams never detect it. Here's how to surface the divergence before it erodes trust in your system.
The Expertise Cliff: Why AI Coding Agents Fail in Mature Codebases
AI coding agents produce genuine velocity gains on greenfield code—then quietly accumulate damage in mature systems. The gap is tacit knowledge: the undocumented constraints, rejected alternatives, and architectural rationale that live in engineers' heads but never reach the repository.
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.
