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12 articlesWhy Your AI Demo Always Outperforms Your Launch
AI demos score high on curated inputs. Production traffic is messier, broader, and full of edge cases your team never imagined. Here is why the gap exists and the methodology that closes it before you ship.
The AI Hiring Rubric Problem: Why Your Interview Loop Selects the Wrong Engineer
Traditional coding interviews are blind to the skills that actually predict AI engineering success. Here's what to assess instead.
The Metrics Translation Problem: Why Technically Successful AI Projects Lose Funding
80% of AI projects fail to deliver business value — not because the models don't work, but because engineering teams never translate technical metrics into language executives can evaluate. A practical framework for mapping F1 scores, latency, and eval results to outcomes that keep projects funded.
Ambient AI Design: When the Chat Interface Is the Wrong Abstraction
Most AI features get built as chat interfaces—but chat is the wrong abstraction for a large fraction of valuable AI work. Here's how to recognize when ambient agents are the right call.
The Annotation Pipeline Is Production Infrastructure
Running human labeling for evals and fine-tuning is a software engineering problem most teams manage in a spreadsheet. Here's what production annotation infrastructure actually looks like — and why inter-annotator agreement is a spec health signal, not a headcount problem.
Backpressure Patterns for LLM Pipelines: Why Exponential Backoff Isn't Enough
Four production patterns—token bucket queuing, priority lanes, token-aware circuit breakers, and load shedding—that keep LLM pipelines reliable when exponential backoff leaves systems in a sustained overload oscillation.
Behavioral Contracts: Writing AI Requirements That Engineers Can Actually Test
Traditional acceptance criteria break on stochastic AI systems. The four-field behavioral contract format — input class, expected behavior, failure budget, test oracle — gives engineers something they can actually measure.
The Build-vs-Buy LLM Infrastructure Decision Most Teams Get Wrong
Most teams undercount TCO on both sides of the build-vs-buy decision for LLM infrastructure. Here's the break-even math at every stage and the hidden costs nobody budgets for.
Closing the Feedback Loop: How Production AI Systems Actually Improve
Why most teams collect feedback signals that never reach the model — and the architectural decisions that convert production telemetry into genuine capability gains.
The Cold Start Problem in AI Features: Why Week One Always Fails
Why behavioral ML systems fail on day one — and the layered bootstrapping architecture that keeps them useful before real training data arrives.
Context Poisoning in Long-Running AI Agents
How accumulated context in long-running AI agents silently corrupts reasoning, the four failure modes that cause it, and the checkpointing, pruning, and invariant-checking patterns that prevent cascading failures.
Debugging LLM Failures Systematically: A Field Guide for Engineers Who Can't Read Logs
When a prompt fails in production, most engineers cycle through random edits until something works. Here's the structured methodology — input ablation, boundary testing, intermediate inspection — that finds root causes in minutes instead of hours.
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.
