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12 articlesCultural Calibration for Global AI Products: Why Translation Is 10% of the Problem
LLMs are fluent in dozens of languages but calibrated to one culture. Here's what translation misses and how to engineer around it.
Database Connection Pools Are the Hidden Bottleneck in Your AI Pipeline
AI workloads break the assumptions behind standard connection pool sizing. Here's the math, the failure modes, and the patterns that actually work.
Deadline Propagation in Agent Chains: What Happens to Your p95 SLO at Hop Three
User-facing latency constraints silently disappear as requests traverse multi-step agent pipelines. Here's the structural problem behind that behavior, how major frameworks handle it (poorly), and the deadline-propagation patterns that fix it.
The Demo-to-Production Failure Pattern: Why AI Prototypes Collapse When Real Users Arrive
Demos run on cherry-picked inputs, warm caches, and patient evaluators. Production gets adversarial queries, distribution-shifted requests, and users who abandon in 8 seconds. Here's the pre-launch methodology that closes the gap.
The Deprecated API Trap: Why AI Coding Agents Break on Library Updates
LLMs produce fluent, confident code referencing APIs that no longer exist. Here's what causes it, how to measure it, and the layered defenses that actually work.
Distributed Tracing for Agent Pipelines: Why Your APM Tool Is Flying Blind
Standard APM tools break on multi-step agent pipelines. Here's what purpose-built observability for AI agents actually requires — and the three metrics that tell you an agent is degrading before users notice.
Document AI in Production: Why PDF Demos Lie and Production Pipelines Don't
The gap between a working PDF demo and a reliable production pipeline is vast. Here's what breaks, how to detect it, and how to architect for 10,000+ documents a day.
Document Extraction Is Your RAG System's Hidden Ceiling
PDF-to-text pipelines silently discard tables, scramble reading order, and destroy section hierarchy before your embedding model ever sees the data. Here's how to find and fix the real failure layer in your RAG system.
Earned Autonomy: How to Graduate AI Agents from Supervised to Independent Operation
A framework for gradually expanding AI agent operational scope based on measured performance history, with rollback triggers and oversight mechanisms that prevent premature autonomy.
The Edge Inference Decision Framework: When to Run AI Models Locally Instead of in the Cloud
A practical decision framework for AI engineers: when on-device and on-premise LLM inference outperforms cloud APIs, and how to design the hybrid architecture that connects them.
The Enterprise AI Capability Discovery Problem
Enterprise users systematically underutilize AI features because they can't imagine the full capability surface from a chat box. Here are the design patterns that actually fix this.
Why Your Document Extractor Breaks on the Contracts That Matter Most
Fixed-layout extractors fail on the adversarial diversity of real enterprise documents. Here's the preprocessing pipeline that actually works in production, and the eval methodology that measures quality on the long tail.
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.
