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12 articlesThe Long-Tail Coverage Problem: Why Your AI System Fails Where It Matters Most
Aggregate metrics like accuracy and F1 can look great while your AI system silently fails on the minority inputs that matter most. How to detect, measure, and fix long-tail coverage gaps before users find them.
LoRA Adapter Composition in Production: Running Multiple Fine-Tuned Skills Without Model Wars
Teams build separate LoRA adapters for tone, format, domain knowledge, and safety — then hit conflicts when composing them. Here's how to detect interference, choose the right merge strategy, and serve mixed adapters per-request without reloading weights.
The Mental Model Shift That Separates Good AI Engineers from the Rest
The transition from deterministic to stochastic systems trips up strong engineers. Here are the mental models, debugging intuitions, and practices that actually separate experienced AI engineers from everyone else.
Model Deprecation Is a Production Incident Waiting to Happen
LLM providers deprecate models on 6–12 month windows, but most teams treat migration as a backlog item—until it becomes a 3 AM outage. Here's the operational playbook to make model upgrades boring.
Multi-Tenant AI Systems: Isolation, Customization, and Cost Attribution at Scale
How to serve multiple customers from shared AI infrastructure without leaking data, creating noisy neighbors, or losing track of who's spending what.
Multi-Modal Agents in Production: What Text-Only Evals Never Catch
Adding vision and document inputs to agent pipelines introduces failure modes that text-only evals never surface. Here's what practitioners are running into and how to build evals that catch it.
Multimodal AI in Production: The Gap Between Benchmarks and Reality
Vision and audio models look impressive in demos. In production, they face latency penalties, grounding failures, and extraction inconsistencies that most benchmark scores hide entirely.
The 90% Reliability Wall: Why AI Features Plateau and What to Do About It
Why AI features stall around 90% reliability, how to diagnose reducible vs. irreducible error, and the product-architecture decisions that let you ship honest value.
On-Call for Stochastic Systems: Why Your AI Runbook Needs a Rewrite
Traditional incident response assumes reproducible failures. LLM-powered systems don't. Here's how to rewrite your alerting schema, triage decision tree, and post-mortem template for non-deterministic AI.
The On-Device LLM Problem Nobody Talks About: Model Update Propagation
Shipping LLMs to edge devices creates a distributed system with no central rollback — version fragmentation, silent capability drift, and artifact ensemble mismatches that don't show up in benchmarks.
On-Device LLM Inference in Production: When Edge Models Are Right and What They Actually Cost
The privacy, latency, and offline case for running LLM inference on iOS, Android, and browser—plus the quality-size tradeoffs, cost math, and the update problem that bites teams six months after ship.
The Orchestration Framework Trap: When LangChain Makes You Slower to Ship
AI orchestration frameworks like LangChain accelerate prototyping but create debugging opacity, versioning brittleness, and leaky abstractions at scale. Here's the decision framework for knowing when to use them and when to drop down a layer.
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.
