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12 articlesBehavioral 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.
Document Injection: The Prompt Injection Vector Inside Every RAG Pipeline
Every PDF, Word doc, and spreadsheet your RAG pipeline ingests is a potential attack surface. Here's how document injection works, what it's already broken in production, and the sanitization architecture that actually defends against it.
Why Gradual Rollouts Don't Work for AI Features (And What to Do Instead)
Feature flags and canary deploys assume deterministic code. AI features are stochastic, quality degrades silently, and there's no real-time ground truth. Here's the mental model shift required to deploy AI safely.
The HITL Rubber Stamp Problem: Why Human-in-the-Loop Often Means Neither
Most human-in-the-loop implementations don't produce oversight — they produce paperwork. Here's why reviewers stop scrutinizing, and the design patterns that keep HITL meaningful at scale.
The Hybrid Automation Stack: A Decision Framework for Mixing Rules and LLMs
Rule-based automation is brittle but auditable. LLM automation is flexible but opaque. A practical decision framework for which tasks belong in which paradigm—and how to architect the seam between them.
Latency Budgets for AI Features: How to Set and Hit p95 SLOs When Your Core Component Is Stochastic
LLM latency doesn't behave like database latency. Here's how to define realistic p95 SLOs for AI-powered features, decompose the latency budget, and use hedging, streaming, and speculative execution to actually hit them.
The Provider Reliability Trap: Your LLM Vendor's SLA Is Now Your Users' SLA
You're advertising 99.9% uptime while your critical path runs through an API with a 99.5% SLA — and provider incidents cluster during peak traffic. Here's how to close the gap before an outage closes it for you.
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.
