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12 articlesBehavioral SLAs for AI-Powered APIs: Writing Contracts for Non-Deterministic Outputs
When your API wraps an LLM, traditional SLAs break down. Learn how to define behavioral contracts — format guarantees, refusal rates, latency p95, hallucination budgets — and how to version and communicate behavioral changes without breaking your consumers.
Browser-Native LLM Inference: The WebGPU Engineering You Didn't Know You Needed
Running LLMs directly in the browser via WebGPU changes your entire application architecture. Here's what the capability ceiling actually looks like, and when hybrid routing beats a pure cloud approach.
Coding Agents in the Monorepo: Why Context Windows and 50-Service Repos Don't Mix
Coding agents hit a hard wall in large monorepos: the relevant code for any cross-service change spans more packages than fit in any context window. Here's what actually works.
The Cold Start Trap in AI Products
AI features need user data to work, but need to work to attract users. Here's how to escape the cold start trap without burning months on ML before your product earns the right to it.
The Confidence-Accuracy Inversion: Why LLMs Are Most Wrong Where They Sound Most Sure
Frontier LLMs exhibit their worst calibration in the domains where users trust them most. Here's how to measure the problem and build systems that handle overconfident wrong answers before they cause real damage.
The Copyright Exposure in AI-Generated Content: A Risk Framework for Engineering Teams
LLM outputs can reproduce verbatim training data, and the output liability can land with you — not the model provider. A practical engineering framework for measuring copyright exposure, implementing controls that actually work, and understanding the limits of provider indemnification.
Cultural 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.
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.
