Google Cloud is rebuilding the enterprise stack for the age of agents. Google Cloud unveils an agentic enterprise stack aimed at turning autonomous agents into measurable business outcomes. Outcome engineers must treat the control plane as product infrastructure for orchestration, observability, and measurable ROI — Principle 09.

Red-teaming a network of agents: Understanding what breaks when AI agents interact at scale. Microsoft Research demonstrates network-level failure modes—propagation, amplification, trust capture, and invisibility—when agents interact at scale. If you build agent networks, you need adversarial tests and system-level defenses to prevent cascading failures and capture attack surfaces — Principle 14.

Agentic Memory: Walrus Takes On AI’s Next Big Bottleneck. Walrus launches MemWal, an SDK for verifiable, portable, encrypted long-term memory for agents to share state across vendors and sessions. Persistent, verifiable memory changes agent design: outcome engineers can build cross-agent workflows that are auditable and portable rather than siloed — Principle 06/09.

Alibaba’s Metis agent cuts redundant AI tool calls from 98% to 2% — and gets more accurate doing it. Alibaba uses HDPO to eliminate redundant tool calls and simultaneously improve reasoning accuracy. That’s a concrete pattern for reducing per-outcome compute and designing rewards/priority systems to make agents both cheaper and more reliable in production — Principle 12.

The playbook for your agentic SDLC. The piece prescribes measuring and governing agentic AI across an SDLC with defined business KPIs and human-in-the-loop checkpoints before scaling. Outcome engineers need this operational checklist to move agents from prototypes to auditable, accountable production — Principles 15 and 16.