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12 articlesThe Debugging Regression: How AI-Generated Code Shifts the Incident-Response Cost Curve
AI code generation delivers real upfront velocity, but the cost appears downstream — at 3am, when the engineer on-call lacks the mental model to debug code they didn't write and barely reviewed.
AI Code Review at Scale: When Your Bot Creates More Work Than It Saves
The false-positive math that determines whether an AI PR reviewer accelerates or exhausts your team, what issue categories AI reviewers catch reliably vs. miss, and how to measure whether your code review agent is net positive.
AI-Assisted Codebase Migration at Scale: Automating the Upgrades Nobody Wants to Touch
How AI agents handle bulk code migrations—deprecated APIs, framework upgrades, language version evolution—where the wins are massive, where they create more work than they save, and the verification strategy that makes either approach safe.
The AI Engineering Career Ladder: Why Your SWE Leveling Framework Is Lying to You
Standard SWE leveling frameworks systematically misread AI engineer performance. Here's what actually distinguishes junior from senior when models do most of the coding.
The AI-Everywhere Antipattern: When Adding LLMs Makes Your Pipeline Worse
Adding an LLM to every step of your pipeline is the fastest way to make it slower, more expensive, and harder to debug. Here's the decision framework for knowing when AI genuinely helps versus when a lookup table is the right answer.
1% Error Rate, 10 Million Users: The Math of AI Failures at Scale
Why accuracy metrics that look fine in offline evals become catastrophic at production volume, how to set SLOs for AI features that account for tail behavior, and the product decision of what to do when a model is good enough but still wrong millions of times per month.
The AI Feature Deprecation Playbook: Shutting Down LLM Features Without Destroying User Trust
A practical guide for engineers and PMs on how to deprecate LLM-powered features cleanly — covering data lifecycle teardown, behavioral migration testing, user trust dynamics, and communication strategy.
What 'Done' Means for AI-Powered Features: Engineering the Perpetual Beta
AI-powered features never reach a stable 'done' state — model drift, world drift, and expectation drift create continuous iteration pressure. Here's the engineering and governance infrastructure that makes 'stable but evolving' feel like quality rather than incompleteness.
The AI-Generated Code Maintenance Trap: What Teams Discover Six Months Too Late
Teams adopting coding agents see dramatic velocity gains in months one through three. By month twelve, many find themselves unable to ship features without understanding their own systems. Here's the failure pattern — and how to avoid it.
AI Infrastructure Carbon Accounting: The Sustainability Cost Your Team Hasn't Measured Yet
AI inference now produces 2.5–3.7% of global emissions and is growing 15% annually. Here's how to measure your team's contribution and why it will become a compliance concern whether you plan for it or not.
Choosing a Vector Database for Production: What Benchmarks Won't Tell You
Benchmark leaderboards measure the wrong things. Here's the evaluation framework that actually predicts whether your vector database will hold up in production.
AI Oncall: What to Page On When Your System Thinks
How to design alerting for non-deterministic AI systems, what an AI incident looks like vs. a traditional failure, and runbook structures that actually help an on-call engineer at 2am.
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.
