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12 articlesContext 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.
Tool Use in Production: Function Calling Patterns That Actually Work
Most LLM agent failures trace back to under-specified tool schemas, not model capability. A practical guide to schema design, error handling, parallel calling, and security for production function calling.
Your AI Product Needs Evals
Most AI products fail not because of the model, but because of missing evaluation systems. A practical guide to building evals from unit tests to human review to A/B testing — and why starting early compounds.
How to Write Fundraising Appeals That Inspire Giving
Craft compelling fundraising appeals that capture attention and inspire action by applying proven psychological principles and practical strategies. Learn how to navigate the critical first moments of reader engagement to ensure your message resonates and prompts giving.
The Unglamorous Work Behind Rapidly Improving AI Products
Most AI teams plateau after launch — not from lack of capability, but from skipping the boring fundamentals: error analysis, custom tooling, domain expert involvement, and experiment-driven roadmaps.
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.
