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12 articlesWhy Multi-Agent AI Architectures Keep Failing (and What to Build Instead)
Multi-agent AI systems fail in production at rates of 41–87%. Here's why parallel agents compound errors, fragment context, and resist debugging — and what simpler architecture actually works.
Building a Generative AI Platform: Architecture, Trade-offs, and the Components That Actually Matter
A practitioner's guide to the infrastructure layer around LLMs—RAG pipelines, model gateways, caching strategies, guardrails, and observability—and when to actually add each component.
12-Factor Agents: A Framework for Building AI Systems That Actually Ship
Most AI agent projects stall at 80% quality and never ship. The 12-Factor Agents framework documents the principles that production teams converged on independently to build reliable, observable LLM-powered systems.
The Lethal Trifecta: Why Your AI Agent Is One Email Away from a Data Breach
When an AI agent can access private data, consume untrusted content, and communicate externally, a single poisoned email becomes a data breach. Here's the architectural pattern behind these attacks and how to stop them.
Valuable. Doable. Mine.
Reflecting on the historical lessons of Xerox PARC and Apple to explore how, in a rapidly changing technological environment, one can judge the true value of a technology and whether it aligns with personal business pursuits.
LLM Observability in Production: Tracing What You Can't Predict
Standard monitoring dashboards miss most of what goes wrong in LLM applications. A practical guide to distributed tracing, cost attribution, latency profiling, and debugging non-deterministic agent behavior at scale.
The Hidden Costs of Context: Managing Token Budgets in Production LLM Systems
Context windows aren't free storage — they're the biggest hidden cost in LLM systems. Learn how quadratic attention scaling, the lost-in-the-middle problem, and context length creep drive bills up, and the layered strategies that keep them under control.
Structured Output in Production: Getting LLMs to Return Reliable JSON
Getting LLMs to return valid, schema-compliant JSON in production is harder than it looks. Here's how constrained decoding, validation layers, and schema design decisions interact — and where each approach breaks down.
Prompt Engineering Deep Dive: From Basics to Advanced Techniques
A practical guide to prompt engineering for engineers building with LLMs in production — covering zero-shot vs few-shot tradeoffs, chain-of-thought benchmarks, structured output reliability patterns, and the five mistakes that break production prompts.
What AI Benchmarks Actually Measure (And Why You Shouldn't Trust the Leaderboard)
AI benchmark scores look objective, but data contamination, format sensitivity, and Goodhart's Law mean leaderboard rankings often tell you little about real-world performance. Here's what to watch for.
Tool Calling in Production: The Loop, the Pitfalls, and What Actually Works
A practical guide to tool calling in production LLM systems — covering the agentic loop, parallel execution formatting rules, writing effective tool descriptions, error recovery with is_error, and when tools add latency without value.
Why Multi-Agent Systems Break at the Seams: Designing Reliable Handoffs
Production multi-agent systems fail at the boundaries between agents, not inside them. A breakdown of the three dominant failure modes and the engineering patterns that prevent them.
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.
