March 2026
46 posts: 46 entries
March 12, 2026
What Actually Happens When You Call invoke_agent_runtime()
You call invoke_agent_runtime(). Your agent responds 3 seconds later. But what actually happened in those 3 seconds? There’s an entire orchestration layer that you never see: sidecars, health checks, microVM boot sequences. Here’s the full picture.
The 95% Rule: Why Your Agent Is Slow and How to Prove It
Your agent takes 5 seconds to respond. Where did those 5 seconds go? AgentCore gives you 6 observability layers, 30 hidden metrics, and a debugging decision tree. But you have to know where to look. Here’s everything you can’t see by just reading the code.
[... 2,831 words]I Ran 100 Parallel Tool Calls on AgentCore — The microVM Didn’t Break, But the LLM Did
What happens when you fire 100 tool calls in parallel inside a single AgentCore microVM? Does the microVM crash? Does it run out of memory? Does the thread pool explode? I deployed an agent with 100 tools to Amazon Bedrock AgentCore Runtime and ran a scaling test from 5 to 100 parallel tool calls. Here’s exactly what happened.
[... 2,716 words]March 13, 2026
Mental Models in the AI Agent Age
Mental models are compressed knowledge of human experience. Many thinkers discovered these patterns over centuries across physics, biology, economics, mathematics, and systems theory. In the age of AI agents, these same patterns don’t just help you think better. They help you build better systems, debug reality faster, and make decisions that compound over decades.
[... 1,760 words]Coding in the AI Agent Age — Why Typing Code Is Dying But Engineering Is Thriving
Maybe you think coding is just putting human-defined processes into structures, loops, functions, rules, packages, and web pages. If so, you’re not wrong about the past. But that definition is dying. AI automates the typing. What remains is the thinking.
[... 1,396 words]How Skills Work in AI Agents — From Lazy-Loading Instructions to LLM Attention Weights
When you hear “skills” in AI agents, it sounds like a new concept. It is not. Skills are a lazy-loading pattern for instructions. The same tool-calling mechanism the LLM already uses delivers them. But the details reveal deep truths about how LLMs actually work:
[... 2,808 words]Autoresearch and Context Rot — How a Stateless Agent Loop Avoids Memory Problems (And Where It Breaks)
In the autoresearch pattern, a coding agent runs hundreds of autonomous experiments to optimize code. The pattern produced a 53% speedup on Shopify’s 20-year-old Liquid codebase and a 69x speedup on a demo text processor. But there’s a fundamental flaw nobody talks about: the agent has no memory of failed experiments. Here’s exactly how the pattern works, where it breaks, and how Tobi Lütke’s team quietly fixed it.
[... 2,409 words]March 15, 2026
The Agent Loop Iceberg — 10 Hard Problems Hiding Beneath the Simple Loop
The basic agent loop is: LLM call, tool execution, observe result, repeat. That loop is maybe 10% of a production agent’s code. The other 90% makes it reliable, resumable, extensible, and production-grade. I traced real agent source code. Here are the ten hard problems hiding beneath the surface that nobody shows you in tutorials.
[... 1,708 words]March 18, 2026
From Prompt Engineering to Harness Engineering: Building Infrastructure for Autonomous Agents
2025 was the year of agents. 2026 is the year of harnesses. A harness is the persistent infrastructure that gives a foundation model hands, feet, and senses. The shift is fundamental: from prompt engineering to harness engineering. Prompt engineering optimizes single interactions. Harness engineering builds the systems that control long-running, autonomous agents.
[... 1,179 words]7 Mental Models for Building Agent Skills (From Anthropic’s Internal Playbook)
Anthropic just published their internal playbook for Claude Code Skills. It draws on hundreds of skills in active use. Deep mental models for building better agents sit inside the practical advice. Here’s what they’re really telling you.
[... 1,293 words]March 19, 2026
Learning OpenUSD — From Curious Questions to Real Understanding
Written as I explored OpenUSD before my exam. These are real questions I asked, and the answers that actually made things click for me.
[... 1,135 words]March 25, 2026
OpenUSD Mastery: From Composition to Pipeline — A SO-101 Arm Journey
OpenUSD (Universal Scene Description) is not just a 3D modeling format. It is a universal language that describes complex scenes, their relationships, and their properties. Think of it as JSON for 3D worlds, but one that can do infinitely more.
[... 1,305 words]March 28, 2026
OpenUSD: Advanced Patterns and Common Gotchas.
This post covers deeper OpenUSD concepts: schemas, rendering rules, performance patterns, and the gotchas that surprise people.
[... 1,141 words]March 31, 2026
Multiple MCP Servers Through Amazon Bedrock AgentCore Gateway
As AI agents scale in enterprises, teams build dozens of specialized MCP (Model Context Protocol) servers. One is for order management, another is for the product catalog, and yet another is for promotions. Each server has its own endpoint, its own auth, its own tool definitions. The agent that consumes these tools suddenly becomes an integration nightmare.
[... 1,728 words]Claude Code’s Design Philosophy: 10 Patterns to use for Your Agent Systems
This post examines Claude Code’s engineering decisions: the prompt architecture, tool philosophy, concurrency model, permission system, and memory design that make it work. Each section includes what you can apply to your own agent systems.
[... 2,426 words]Does Claude Code Test Itself? Yes — Here’s What’s Actually in the Source
Anthropic published a blog post on demystifying evals for AI agents. It recommends three grader types, eight setup steps, and a feedback loop from production back into improvement decisions. The interesting part is what the Claude Code source code reveals. The product doesn’t just follow the philosophy. It IS the eval system.
[... 1,467 words]