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12 articlesSeven Patterns for Building LLM Systems That Actually Work in Production
A practical breakdown of seven engineering patterns — evals, RAG, fine-tuning, caching, guardrails, UX design, and feedback loops — that separate working LLM prototypes from reliable production systems.
Common Pitfalls When Building Generative AI Applications
95% of generative AI pilots yield no measurable business impact. Here are eight engineering and product failures that kill AI projects — from problem selection through evaluation — with production examples.
The Agent Evaluation Readiness Checklist
A practical, sequenced checklist for building AI agent evaluations that actually catch failures — covering trace review, dataset design, grader patterns, and connecting evals to production.
Self-Healing Agents in Production: How to Build Systems That Fix Themselves
Production AI agents fail silently — wrong answers, stalled tasks, no stack traces. A layered approach to detection, triage, and automated recovery can catch most failures before users notice.
The Anatomy of an Agent Harness
The model is almost interchangeable — the harness is what determines whether your AI agent works in production. A breakdown of the six core components every production-grade agent harness needs.
How AI Agents Actually Learn Over Time
AI agents can improve at three distinct layers—model weights, harness code, and runtime context—and most teams are only using one of them. Here's how to build the feedback loops that let agents compound in quality over time.
Context Engineering for Personalization: How to Build Long-Term Memory Into AI Agents
Building truly personalized AI agents requires more than a large context window. A structured memory lifecycle — inject, distill, trim, consolidate — is what separates agents that remember from agents that reset.
Routines and Handoffs: The Two Primitives Behind Every Reliable Multi-Agent System
Most multi-agent failures trace back to leaky plumbing, not bad models. Learn how routines and handoffs — two simple primitives — give you the structure to build reliable, production-grade agent systems.
Measuring AI Agent Autonomy in Production: What the Data Actually Shows
Large-scale production data reveals counterintuitive patterns in AI agent autonomy: experienced users approve more AND interrupt more, agents initiate oversight at 2x the rate of humans, and only 0.8% of actions are truly irreversible.
Building Effective AI Agents: Patterns That Actually Work in Production
Most AI agent projects fail because engineers reach for complexity before they've earned it. A practical guide to the mental models, patterns, and operational principles that separate reliable agents from ones that hallucinate and loop.
Going Viral on Twitter by Reverse-Engineering The Algorithm
Gain insights into Twitter's recommendation algorithm through an analysis of its open-source code, revealing the mechanisms behind content visibility and virality. Align content strategies with the algorithm's core logic for maximum engagement.
Organizational Economics: How Do Structure and Management Style Affect Performance?
An organization's structure and management directly impact its performance. This analysis of real-world cases reveals how good organizational practices solve problems of coordination and incentives, enhance efficiency, and determine business success or failure.
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.
