Outcome Engineering — The o16g Manifesto publishes a concise manifesto that centers human intent as the organizing principle for agent design and operation. It reframes agents as explorers acting under human-set missions, which directly informs how you define goals, guardrails, and verification in production — Principle 01.

CAISI Issues Request for Information About Securing AI Agent Systems issues an RFI asking for public input to define security standards and practices for AI agent systems. This will shape national expectations for threat modelling, runtime controls, and compliance obligations that outcome engineers must bake into agent architectures — Principles 10 & 14.

Prompt Injection Via Road Signs shows CHAI using deceptive text in visual inputs to hijack embodied AI controls, exposing a practical multimodal attack vector. Outcome engineers need to treat perceptual inputs as attack surfaces, adding adversarial detection, input provenance, and robust failure modes to agent pipelines — Principle 14.

Zvec: A lightweight, fast, in-process vector database releases an in-process vector DB optimized for low-latency hybrid semantic and filtered similarity search at scale. Embedding a tiny, fast vector store directly in your app reduces retrieval latency and operational complexity for agents, letting you build tighter RAG loops and ship repeatable artifacts faster — Principles 06 & 07.

Picogrid wins $9M Air Force contract for counter-drone software written by AI reports Picogrid using AI to generate translator modules that cut system-integration time from weeks to hours and secure a $9.3M contract. It demonstrates agent-assisted code and integration moving from experiment to production, underscoring the need for orchestration, verifiable artifacts, and integration-safe workflows in outcome engineering practice — Principles 06 & 09.