SkyPilot Agent Skill: Let Agents Manage Your GPUs releases an Agent Skill that lets AI coding agents launch, manage, and autostop GPU clusters across clouds using natural language. Outcome engineers get a programmatic, observable compute lifecycle for agents — reducing manual ops, improving reproducibility, and turning compute into a shippable artifact (Principles 07/06).

AI agents aren’t failing. The coordination layer is failing proposes an ‘Event Spine’ to centralize ordering, context propagation, and coordination primitives to prevent multi-agent conflicts and scale orchestration. If you build agentic systems, this reframes the work from model tweaks to robust coordination primitives — design an orchestration layer that enforces ordering, context, and safe handoffs (Principle 09).

Tiger Teams, Evals and Agents: The New AI Engineering Playbook argues cross-functional Tiger Teams plus rigorous, continuous evals are the core playbook for shipping agentic applications. Adopt that pattern: pair delivery teams with domain-relevant evals and remediation loops so agents graduate from experiments to reliable organizational capabilities (Principles 03/16).

Advisor Strategy in Agents advocates using lightweight advisor models to triage work and call powerful LLMs only for hard decisions, cutting API costs while preserving frontier reasoning. Architect outcome systems with advisor-controller separation to optimize latency, cost, and safety — a practical production pattern for agent stacks (Principles 09/12).

Instant 1.0 — a backend for AI-coded apps ships a multi-tenant, real-time backend that bundles sync, auth, and storage to power AI-coded full-stack apps. Treat a backend like this as a first-class artifact: it enforces data contracts, multi-tenant boundaries, and operational guardrails that let agents deliver reliable user outcomes (Principles 04/07).