March 2026
43 posts: 43 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 — sidecars, health checks, microVM boot sequences — that you never see. 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.
[... 2,829 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.
[... 2,597 words]March 13, 2026
Mental Models in the AI Agent Age
Mental models are compressed knowledge of human experience — patterns discovered over centuries by many thinkers 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.
[... 1,757 words]Coding in the AI Agent Age — Why Typing Code Is Dying But Engineering Is Thriving
If you think coding is just putting human-defined processes into structures, loops, functions, rules, packages, and web pages — you’re not wrong about the past. But that definition is dying. AI is automating the typing. What remains is the thinking.
[... 1,387 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’s not. Skills are a lazy-loading pattern for instructions — delivered through the same tool-calling mechanism the LLM already uses. But the details of how they load, where they land in the message hierarchy, and why they break at scale reveal deep truths about how LLMs actually work.
[... 2,801 words]Autoresearch and Context Rot — How a Stateless Agent Loop Avoids Memory Problems (And Where It Breaks)
The autoresearch pattern — where a coding agent runs hundreds of autonomous experiments to optimize code — 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.
[... 2,392 words]March 15, 2026
The Agent Loop Iceberg — 10 Hard Problems Hiding Beneath the Simple Loop
The basic agent loop — LLM call, tool execution, observe result, repeat — is maybe 10% of a production agent’s code. The other 90% is making it reliable, resumable, extensible, and production-grade. After tracing through real agent source code, here are the ten hard problems hiding beneath the surface that nobody shows you in tutorials.
[... 1,695 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 — the persistent infrastructure that gives a foundation model hands, feet, and senses. The shift is fundamental: from prompt engineering (optimizing single interactions) to harness engineering (building the systems that control long-running, autonomous agents).
[... 1,174 words]7 Mental Models for Building Agent Skills (From Anthropic’s Internal Playbook)
Anthropic just published their internal playbook for Claude Code Skills — based on hundreds of skills in active use. Buried inside the practical advice are deep mental models for building better agents. Here’s what they’re really telling you.
[... 1,275 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’s a universal language for describing complex scenes, their relationships, and their properties. Think of it as JSON for 3D worlds, but infinitely more powerful.
[... 1,260 words]March 28, 2026
OpenUSD: Advanced Patterns and Common Gotchas.
Deeper OpenUSD concepts — schemas, rendering rules, performance patterns, and the gotchas that catch people off guard.
[... 1,122 words]