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12 articlesBuilding Governed AI Agents: A Practical Guide to Agentic Scaffolding
Most AI agent deployments fail not because the LLM underperforms—but because the scaffolding around it lacks governance. Here's how to build agents that are secure, auditable, and trustworthy in production.
Context Engineering: The Discipline That Matters More Than Prompting
Why the real bottleneck in production LLM systems is context architecture, not prompt wording — and how to design context as a first-class system concern.
Your CLAUDE.md Is Probably Too Long (And That's Why It's Not Working)
Adding more rules to your CLAUDE.md often makes your AI coding agent follow fewer of them. Here's why instruction overflow happens and how to architect your agent files for reliable compliance.
Eval Engineering for Production LLM Systems
How to build LLM evaluation systems that actually catch failures—covering error analysis loops, eval cost hierarchies, LLM-as-judge methodology, CI/CD integration, and agent-specific pitfalls.
Multi-Agent Conversation Frameworks: The Paradigm Shift from Pipelines to Talking Agents
Most multi-agent failures aren't model failures — they're architecture failures. Here's how conversation-based agent frameworks work, where they win, and why unstructured agent networks can amplify errors 17x.
Agentic RAG: When Your Retrieval Pipeline Needs a Brain
Most RAG systems fail in production not because of bad models, but because engineers skip the control loop. A guide to agentic RAG architecture — routers, graders, hallucination checkers, and the failure modes that kill first deployments.
Why Your Existing Observability Stack Won't Save You When AI Agents Break
HTTP 200s and clean latency charts mean nothing when your AI agent is reasoning incorrectly. How execution-level tracing works, what to measure, and how the observability tooling landscape breaks down for production agent systems.
Token Economics for AI Agents: Cutting Costs Without Cutting Corners
AI agents consume 3–10x more tokens than chatbots — and the gap between an unoptimized and optimized deployment can reach 200x in cost. A practical guide to prompt caching, model routing, context compression, and hard limits that actually move the needle.
AlphaEvolve's Architecture: How Evolutionary Search + LLMs Discovered a Better Matrix Algorithm
A breakdown of AlphaEvolve's four-component loop — program database, prompt sampler, LLM ensemble, and evaluator — and what engineers can learn from the architecture that beat a 56-year-old algorithm.
Evaluating AI Agents: Why Grading Outcomes Alone Will Lie to You
A practical guide to evaluating AI agents by grading both outcomes and multi-step trajectories — covering grader types, pass@k vs pass^k, eval harness design, and the organizational pitfalls that sink evaluation programs.
Context Engineering: The Invisible Architecture of Production AI Agents
Context rot degrades every major LLM at scale. Learn how to manage context as first-class infrastructure—KV-cache optimization, reversible compression, error trace retention, and the metrics that reveal degradation before your first production incident.
Agentic Engineering Patterns: The While Loop Is the Easy Part
Every production agent runs the same trivial loop. The patterns that matter are the ones built around it — prompt chaining, routing, reflection, and the context discipline that prevents $47,000 weekly bills.
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.
