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12 articlesThe AI Feature Adoption Curve Nobody Measures Correctly
DAU and session length hide whether users genuinely adopt AI features or just tolerate them. Learn the behavioral signals — edit-to-accept ratio, bypass rate, time-to-override — that reveal real adoption, plus the instrumentation architecture to capture them.
AI Feature Billing Is an Engineering Problem Nobody Planned For
Why per-seat and per-query pricing models break for agentic AI products, how to build the cost attribution stack from API call to customer invoice, and the margin math that tells you which AI features are underwater before finance figures it out.
AI Feature Cannibalization: When Your Smart Feature Quietly Kills Your Core Product
AI shortcuts that automate key workflow steps can silently erode engagement loops, reduce product stickiness, and turn your product into a commodity wrapper — here is how to detect and prevent it.
The Five Gates Your AI Demo Skipped: A Launch Readiness Checklist for LLM Features
Why 'the demo looked great' is the worst launch criterion for LLM features, and the five production-readiness gates every AI team needs to pass before shipping.
AI in the SRE Loop: What Works, What Breaks, and Where to Draw the Line
LLMs can cut MTTR by 40-70% and automate post-mortems in minutes — but a confident wrong diagnosis at 3 AM is a different problem than a chatbot error. A practical breakdown of where AI augments incident response, where autonomous action backfires, and the architectural decisions that determine which outcome you get.
AI Product Metrics Nobody Uses: Beyond Accuracy to User Value Signals
Engineering teams obsess over accuracy and latency while the metrics that predict AI product success — task completion rate, edit rate, session depth — go unmeasured. Here's how to instrument for user value.
AI Technical Debt: Four Categories That Never Show Up in Your Sprint Retro
Prompt rot, eval drift, embedding lock-in, and shadow coupling — four compounding forms of AI technical debt that traditional engineering practices miss, with practical strategies to manage each.
Backpressure in Agent Pipelines: When AI Generates Work Faster Than It Can Execute
Agent pipelines that spawn sub-agents and fan out tool calls create unbounded work queues that exhaust token budgets and crash production systems. Applying backpressure patterns from reactive systems — bounded queues, hierarchical budgets, circuit breakers, and adaptive concurrency — prevents runaway expansion before the invoice arrives.
Brownfield AI: Integrating LLM Features into Legacy Codebases Without a Rewrite
Practical adapter patterns — sidecar inference, async enrichment queues, and LLM-as-middleware — for shipping AI features inside legacy monoliths without a risky full rewrite.
Building Multilingual AI Products: The Quality Cliff Nobody Measures
Most AI teams ship globally with English-only evals and aggregate satisfaction scores. Here's what they're missing — and how to find the quality cliff before your users do.
The Caching Hierarchy for Agentic Workloads: Five Layers Most Teams Stop at Two
Production AI agents need five caching layers — prompt, semantic, tool result, plan, and session state — each with distinct TTLs and invalidation strategies. Most teams stop at two and leave half their savings on the table.
Capability Elicitation: Getting Models to Use What They Already Know
Most prompt optimization focuses on instruction clarity, but the real bottleneck is often the model's failure to activate knowledge it already has. A practical guide to elicitation techniques — structured decomposition, analogical priming, expertise framing — that unlock latent LLM capability without fine-tuning.
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.
