High Frequency Trading and Lessons for Agentic AI lays out how HFT controls map to deterministic guardrails and automated risk management for agentic systems. Outcome engineers should borrow HFT patterns for real-time limits, kill-switches, and audit trails to harden agent behavior (Principles 10 & 14).

Inside OpenSearch’s bid to become the default AI data layer describes OpenSearch 3.5–3.6 adding compressed vector search, neural sparse retrieval, and native agent memory APIs. That matters because a unified AI data layer simplifies durable agent memory, hybrid search, and graph-like retrievals you’ll need to build reliable, context-rich agents (Principles 06 & 11).

Flue is a TypeScript framework for building the next generation of agents ships an agent harness and sandboxing primitives in TypeScript to build, run, and deploy autonomous agents. Use it when you want a pragmatic, language-native developer experience for composing, testing, and sandboxing agent behaviors across environments (Principles 06 & 07).

The Agent Harness Belongs Outside the Sandbox argues for moving the harness off disposable sandboxes to protect credentials, enable durable multi-user sessions, and treat sandboxes as ephemeral compute. That architectural shift affects orchestration and security designs—protecting secrets, enabling session continuity, and scaling multi-user agents in production (Principles 07 & 03).

Specsmaxxing — On overcoming AI psychosis, and why I write specs in YAML introduces a YAML-first, spec-driven toolkit to keep agents on-task and preserve structured context across runs. Formalizing intent and expectations as machine-readable specs improves reproducibility, validation, and documentation of agent behavior in production (Principles 06 & 13).