Hands-on with the Google Agent Development Kit shows Google shipping an ADK that simplifies building, deploying, and orchestrating modular AI agents across languages and runtimes with human-in-the-loop controls and agent-to-agent support. Outcome engineers need standardized agent frameworks to move agentic workflows from prototypes into repeatable, observable production systems (Principle 09).

Agents have their own computers with Sandboxes GA announces Cloudflare Sandboxes GA with secure credential injection, snapshots, PTY, and active-CPU pricing to run untrusted agent workloads at scale. Sandboxed, identity-aware execution is a must-have for safe multi-tenant agent infrastructure and for isolating risk during continuous deployment (Principles 07 & 14).

Google Cloud introduces QueryData to help AI agents create reliable database queries launches a schema-aware tool that produces near-deterministic, validated SQL for agents. Deterministic query generation removes a major source of agent flakiness and liability, making agents reliable data actors and simplifying observability and verification (Principles 06 & 14).

GAIA SDK — Build AI Agents That Run Locally debuts AMD’s GAIA SDK to run agents fully on-device, offering RAG, speech, image generation, and agent routing without cloud dependencies. Local-first runtimes shift tradeoffs for privacy, latency, and orchestration — outcome engineers must plan hybrid deployment patterns and versioned on-device artifacts (Principles 07 & 13).

Durable Objects in Dynamic Workers: Give each AI-generated app its own database shows Cloudflare combining Dynamic Workers with Durable Objects to provide per-AI-app persistent, low-latency storage backed by local SQLite instances. Per-agent persistent state is a foundational pattern for reproducible outcomes, auditability, and artifact shipping — use it to make agent behaviors inspectable and auditable (Principles 08 & 16).