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12 articlesReasoning Models in Production: When They Help and When They Hurt
Reasoning models can solve problems that instruct models can't touch — but using them wrong costs 10x more and adds 10 seconds of latency to every request. Here's how to think about the tradeoff.
LLM Latency in Production: What Actually Moves the Needle
A practical breakdown of LLM latency — prefill vs decode phases, streaming, KV cache strategies, speculative decoding, and what to measure to ship faster AI applications.
Why Long-Running AI Agents Break in Production (And the Infrastructure to Fix It)
Long-running AI agents fail in predictable ways: compound error rates, synchronous timeouts, non-idempotent retries, and no plan for human interrupts. Here is the infrastructure that actually makes them reliable.
LLM Guardrails in Production: Why One Layer Is Never Enough
Five guardrails at 90% accuracy gives you 59% system correctness. A practical guide to tiered guardrail architecture—covering input and output validation, tool selection, latency tradeoffs, and why compound error rates are the hidden failure mode.
Context Engineering: Why What You Feed the LLM Matters More Than How You Ask
Context engineering is the systems architecture problem that prompt engineering can't solve. Here's why the four failure modes — poisoning, distraction, confusion, and clash — explain most production LLM incidents, and how to engineer your way out of them.
AI Agent Architecture: What Actually Works in Production
88% of AI agent projects never reach production. The failure is almost never the model — it's the surrounding architecture. A practical breakdown of the five-layer agent stack, four-tier memory model, orchestration vs. routing tradeoffs, and the seven failure modes that account for 94% of production failures.
LLM Guardrails in Production: What Actually Works
A practical engineering guide to LLM guardrails: layered input/output validation, why false positives compound, serial vs. parallel execution tradeoffs, and how to monitor what matters in production.
Memory Architectures for Production AI Agents
A practical breakdown of memory architectures for production AI agents — covering episodic, semantic, and graph memory types, the accuracy/latency tradeoff in retrieval, and the staleness problem no framework has solved yet.
AI 2041: A Journey Through Ten Futures
AI 2041 presents ten realistic future scenarios shaped by artificial intelligence, combining compelling narratives with analytical insights from leading experts. This exploration reveals the profound societal impacts of near-term AI developments.
Fine-Tuning vs. Prompting: A Decision Framework for Production LLMs
A practical framework for deciding when to fine-tune vs. prompt-engineer your LLM—covering cost trade-offs, LoRA/QLoRA, model distillation, and six diagnostic questions every AI team should answer before committing to training.
Prompt Engineering in Production: What Actually Matters
The prompting techniques that make demos impressive often aren't the ones that keep production systems reliable. Here's what actually matters when shipping LLM features at scale.
Why Multi-Agent LLM Systems Fail (and How to Build Ones That Don't)
Multi-agent LLM systems fail 41–87% of the time in production — and 79% of those failures come from coordination and specification problems, not model quality. Here's the failure taxonomy and how to design around it.
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.
