Articles
Notes, tests, and arguments.
A tutorial should do more than run, and an explanation should do more than agree with the first page of search results. These are my arguments about developer content, documentation, and the AI systems I build around them.
Documentation Review Should Start With the Product, Not the Prose
Review documentation in four passes: product truth, reader route, rendered behavior, and release state, before polishing prose.
Technical SEO Checklist for Documentation Sites
Technical SEO for developer documentation: crawling, rendering, indexing, canonicals, internal links, Core Web Vitals, structured data, and search visibility.
Agentic Workflows: A Five-Stage Playbook From Prompt to PR
An agentic workflow puts an AI agent inside a fixed process. I frame and scope the task before the agent runs, and I review its diff before the change lands.
Fine-Tuning vs RAG for Agent Memory: When Each Approach Makes Sense
Fine-tuning and RAG solve different parts of the agent memory problem. Here is how to decide which one you actually need.
Shared Memory vs Isolated Memory in Multi-Agent Workflows
How to choose between shared and isolated memory architectures for multi-agent systems, including their coordination and debugging trade-offs.
LLM Inference Optimization: What Actually Works in Production
A practical breakdown of the inference optimization techniques that move the needle — batching, quantization, caching, and attention kernels — with concrete numbers and the tradeoffs between them.
Why Your Coding Agent Keeps Forgetting Everything: Memory Persistence in AI Coding Assistants
The memory persistence patterns that actually work for AI coding assistants, and why most agents lose context between sessions.
Memory Versioning and Audit Trails for Regulated AI Agents
If your agent overwrites its memory, you cannot pass a compliance audit. How to build append-only memory versioning and trace agent reasoning.
Contextual Compression for Agent Memory: What Stays and What Goes
How agents decide what to keep in memory when context space is finite, and the three compression strategies that actually work.
Episodic, Semantic, and Working Memory in AI Agents: A Practical Map
AI agents juggle three distinct memory types. Getting them wrong is the source of most agent memory failures I see in production.
Memory Serialization: How Agents Persist State Across Sessions
Why agents forget everything on restart, and the serialization patterns that actually solve it
Multi-Agent vs Single-Agent Systems: The Real Trade-offs
The decision between one agent and many is not about capability. It is about failure modes, latency, and operational complexity.
AI Agent vs AI Assistant: When to Build Each
An AI agent chooses its next steps toward a goal, and an AI assistant leaves the next step to you. I compare the cost and control you trade when you build each.
The Agent Design Space: A Map of What Engineers Are Actually Building
After surveying production agents across industries, the design space clusters into patterns. Here is what I found.
Lambda Calculus as an AI Reasoning Exercise
Lambda calculus exposes substitution, scope, and composition errors in AI outputs through problems with mechanically checkable answers.
The Anatomy of an Agent Loop: Perceive, Think, Act, Remember
The agent loop is not one thing. It is four distinct phases that run in sequence, and understanding each one is how you debug what breaks.
Why AI Agents Fail in Production
I have spent two years watching agents fail in production. Here is what I keep seeing and what the field is starting to do about it.
Types of AI Agents: Five Types by Autonomy and Scope
I group AI agents into five types, from reactive information agents to persistent-goal agents, to decide how much autonomy a task needs and which tools and memory it gets.
Memory for Voice AI Agents: What Text Chatbots Cannot Do
Voice AI agents live or die by how they manage memory across a real-time streaming pipeline. Text chatbots solve memory with RAG. Voice agents need something different.
Short-Term Memory for AI Agents: A Practical Guide
Context windows are not memory. Here is what every production AI agent engineer needs to understand about token budgets, overflow handling, and how short-term and long-term memory actually work together.