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12 articlesWhy LLMs Make Confident Mistakes When Analyzing Your Product Data
LLMs confidently hallucinate metrics, miss denominators, and confuse correlation with causation when analyzing behavioral data. Here's where they fail and how to use them safely.
The LLM Provider Incident Runbook: Staying Up When Your AI Stack Goes Down
When your LLM provider goes down, you have minutes to decide. An operational playbook for multi-provider failover, graceful degradation, and user communication that keeps your product standing.
LLM Rate Limits Are a Distributed Systems Problem
LLM API rate limits behave like distributed locks — batch jobs silently starve user-facing flows through starvation, head-of-line blocking, and priority inversion, all while your error dashboards stay green.
The Hidden Switching Costs of LLM Vendor Lock-In
Beyond API compatibility, the real switching costs of changing LLM providers live in prompt rewrites, eval rebuilds, and embedding re-indexing — a map of what survives a model swap and what doesn't.
The Magic Moment Problem: Why AI Feature Onboarding Fails and How to Fix It
The first five minutes determine whether users keep using your AI feature. Here's the engineering behind onboarding flows that actually convert skeptics.
The Minimal Footprint Principle: Least Privilege for Autonomous AI Agents
Designing autonomous AI agents that request only the permissions the current task requires—applying Unix least-privilege to agentic systems through ephemeral credentials, intent-aware access provisioning, and isolated execution.
The Compression Decision: Quantization, Distillation, and On-Device Inference for Latency-Critical AI Features
When model routing isn't enough and you need sub-100ms response times, you face a hard compression decision. Here's how to navigate quantization, distillation, and hybrid edge-cloud deployment without destroying quality on the tasks that matter.
Multi-Region LLM Serving: The Cache Locality Problem Nobody Warns You About
Deploying LLM inference across regions creates consistency and latency problems that stateless HTTP services don't have. Here's the routing architecture that handles it without tripling your ops burden.
The Multi-Tenant LLM Problem: Noisy Neighbors, Isolation, and Fairness at Scale
When thousands of users share the same model and vector index, one expensive session degrades everyone else. Here's why multi-tenant LLM infrastructure is harder than databases — and how to build fairness in.
The Multi-Turn Session State Collapse Problem
Why single-turn LLM failures are easy to catch while multi-turn session state silently corrupts across 10+ turns — and the checkpoint, compression, and monitoring patterns that prevent the 'AI forgot who I am' failure mode.
Multi-User Shared AI Sessions: The Concurrency Problem Nobody Has Solved
When multiple users share a single AI context simultaneously, standard distributed systems assumptions break down. Here's why multi-user AI sessions are architecturally hard and what production teams have built to address it.
On-Call for AI Systems: Incident Response When the Bug Is the Model
Standard on-call runbooks break when the failure is non-deterministic model behavior. A practical framework for detecting, triaging, and containing AI incidents — from guardrail bypass to cost explosions — with playbooks built for engineers, not ML researchers.
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.
