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12 articlesVibe Coding Considered Harmful: When AI-Assisted Speed Kills Software Quality
AI coding tools promise speed but deliver comprehension debt — experienced developers are 19% slower with AI, generated code has 1.7x more issues, and 76% of developers ship code they don't fully understand.
A/B Testing Non-Deterministic AI Features: Why Your Experimentation Framework Assumes the Wrong Null Hypothesis
Standard A/B testing frameworks assume deterministic treatments, but LLM-powered features introduce within-treatment variance that breaks power calculations, inflates sample sizes, and produces unreliable results. A practical guide to randomization, metrics, and variance reduction for non-deterministic AI experiments.
The Abstraction Inversion Problem: When AI Frameworks Force You to Think at the Wrong Level
Most AI agent frameworks promise velocity but deliver lock-in. Here is how the abstraction inversion problem traps teams, why AI abstractions leak faster than traditional ones, and the architecture pattern production teams converge on instead.
Agent Credential Rotation: The DevOps Problem Nobody Mapped to AI
Autonomous AI agents accumulate long-lived secrets across tool integrations, and traditional rotation policies break them mid-task. Four architectural patterns — JIT provisioning, dual refresh, tool-runtime isolation, and connector abstraction — keep agents running safely through credential lifecycles.
The Agentic Deadlock: When AI Agents Wait for Each Other Forever
Multi-agent AI systems deadlock at rates between 25% and 95% when agents coordinate simultaneously — a direct echo of classical distributed systems failures. Practical detection and prevention patterns that keep production agent workflows from freezing.
AI-Assisted Incident Response: Giving Your On-Call Agent a Runbook
Operational toil rose despite record AI investment because teams deployed agents without runbooks or guardrails. A three-tier autonomy model — advisory, approval-gated, conditional — paired with structured runbooks and blast-radius checks turns AI agents into reliable on-call partners.
The AI Feature Adoption Curve Nobody Measures Correctly
DAU and session length hide whether users genuinely adopt AI features or just tolerate them. Learn the behavioral signals — edit-to-accept ratio, bypass rate, time-to-override — that reveal real adoption, plus the instrumentation architecture to capture them.
AI Feature Billing Is an Engineering Problem Nobody Planned For
Why per-seat and per-query pricing models break for agentic AI products, how to build the cost attribution stack from API call to customer invoice, and the margin math that tells you which AI features are underwater before finance figures it out.
AI Feature Cannibalization: When Your Smart Feature Quietly Kills Your Core Product
AI shortcuts that automate key workflow steps can silently erode engagement loops, reduce product stickiness, and turn your product into a commodity wrapper — here is how to detect and prevent it.
The Five Gates Your AI Demo Skipped: A Launch Readiness Checklist for LLM Features
Why 'the demo looked great' is the worst launch criterion for LLM features, and the five production-readiness gates every AI team needs to pass before shipping.
AI in the SRE Loop: What Works, What Breaks, and Where to Draw the Line
LLMs can cut MTTR by 40-70% and automate post-mortems in minutes — but a confident wrong diagnosis at 3 AM is a different problem than a chatbot error. A practical breakdown of where AI augments incident response, where autonomous action backfires, and the architectural decisions that determine which outcome you get.
AI Product Metrics Nobody Uses: Beyond Accuracy to User Value Signals
Engineering teams obsess over accuracy and latency while the metrics that predict AI product success — task completion rate, edit rate, session depth — go unmeasured. Here's how to instrument for user value.
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.
