Introducing the Stateful Runtime Environment for Agents in Amazon Bedrock. OpenAI and AWS launch a stateful runtime in Amazon Bedrock to run reliable, long‑horizon agent workflows with built‑in state, governance, and AWS‑native controls. Outcome engineers should treat this as a new control plane for agent orchestration — it changes where you store durable context, how you enforce policies, and who owns the runtime (Principles 06 & 09).

Building Secure, Scalable Agent Sandbox Infrastructure. Browser Use demonstrates isolating agents in Unikraft micro‑VMs behind a control plane to enable secretless, fast, scalable sandboxed code execution. This gives a concrete architecture for Principle 07 — plan for kernel‑level isolation and a control plane that mediates I/O and secrets when you run untrusted agent code.

FinOps for agents: Loop limits, tool-call caps and the new unit economics of agentic SaaS. The piece outlines loop limits, tool‑call caps and other financial guardrails for controlling agent compute spend. Outcome engineers must bake cost‑guardrails into agent orchestration and product SLAs — unit economics now shape architecture and feature design (Principle 12).

Chat, Code, Claw: What Happens When AI Agents Work in Teams. The article profiles multi‑agent frameworks that orchestrate fleets of specialized bots into persistent virtual firms, while flagging new security and reliability risks. If you’re building agent workflows, design for team coordination, failure modes, and identity boundaries — this is Principles 09 & 03 in practice.

Your Test Suite Should Hit the LLM, Stop Mocking It. George Guimarães argues that integration tests should exercise real models, assert on structured outputs and tool calls, and use semantic checks rather than mocking LLMs. For outcome engineering, replace brittle mocks with end‑to‑end model checks and evaluators to catch drift and validate outcomes (Principles 14 & 16).