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12 articlesThe Cognitive Offloading Trap: When Your Team Can't Work Without the AI
Engineering teams that route all knowledge work through AI agents stop practicing the underlying skills. Here's how to recognize unhealthy AI dependency and design deliberate practices that preserve human capability.
Compound Failure Modes in AI Pipelines: When Partial Success Isn't Enough
If each stage of your AI pipeline succeeds 95% of the time, a three-step chain succeeds only 86% of the time. The probability math practitioners undercount, correlation effects that make it dramatically worse, and the architectural patterns that prevent multiplicative collapse in production.
Context Compression Changes What Your Model Actually Sees
Token pruning and prompt compression can cut LLM inference costs by 3–10x, but they silently change what your model sees. A practical breakdown of the failure modes — lost coreference chains, dropped constraints, tool output hallucination — and how to validate and budget compression safely.
Continuous Fine-Tuning Without Data Contamination: The Production Pipeline
A production engineering guide to ongoing LLM fine-tuning from user feedback — covering data routing architecture, contamination detection, catastrophic forgetting prevention, and automated safety preservation.
Contract Tests for Prompts: Stop One Team's Edit From Breaking Another Team's Agent
Prompts are shared APIs without contracts — a consumer-driven testing discipline catches cross-team breaking changes before they hit production agents.
Data Quality Gates for Agentic Write Paths: Garbage In, Irreversible Actions Out
Agents with write-access tools translate upstream data quality failures directly into real-world side effects. Here's the validation architecture that prevents them.
Debugging AI at 3am: Incident Response for LLM-Powered Systems
A 500 error has a stack trace. A bad generation has a probability distribution. Here's how to triage, debug, and post-mortem AI incidents before they wreck your week.
The Dependency Injection Pattern for AI Applications: Writing Code That Survives Model Swaps
Coupling business logic directly to OpenAI or Anthropic SDKs turns every model deprecation into a month-long refactor. Here's how to apply dependency injection to AI components so model swaps become configuration changes.
Dependency Injection for AI: Mocking Model Calls Without Losing Test Fidelity
Mocking LLM calls in tests looks like a clean abstraction, but naïve stubs silently rot into lies about production behavior. A layered fixture architecture — stub fakes, recorded cassettes, live calls — plus deliberate seam design restores test fidelity without burning money on every commit.
Documenting Probabilistic Features: The Missing Layer Between Model Behavior and Developer Onboarding
AI-powered features have no stable input-output contract to document. Here's how to write API docs, changelogs, and runbooks for features that behave differently every time — using behavioral envelopes, versioning discipline, and observability as living documentation.
The Embedding Drift Problem: How Your Semantic Search Silently Degrades
Embedding models freeze language at training time. As new terminology emerges, your semantic search quietly loses accuracy — no error fires, no alert triggers. Here's how to detect it and what to do.
The Eval Smell Catalog: Anti-Patterns That Make Your LLM Eval Suite Worse Than No Evals At All
A field guide to the anti-patterns that poison LLM eval suites — contamination, brittle assertions, eval rot, judge collusion, vanity aggregates — and the refactoring patterns that restore signal without rewriting the whole harness.
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.
