The model wars are over. Now, Google is fighting for something bigger. Google unveils the Gemini Enterprise Agent Platform, shifting the competition from model performance to control of the agentic control plane; outcome engineers must design around orchestration APIs, telemetry, and portability rather than treating models as the only integration surface. — Principle 09

Agentic Memory: Walrus Takes On AI’s Next Big Bottleneck. Walrus launches MemWal SDK to provide verifiable, portable, encrypted long-term memory for agents; this lets outcome engineers treat memory as an auditable artifact for cross-agent collaboration, provenance, and vendor portability. — Principles 02 & 06

Red-teaming a network of agents: Understanding what breaks when AI agents interact at scale. Microsoft Research demonstrates network-level failures—propagation, amplification, trust capture, invisibility—when agents interact at scale; outcome engineers must prioritize system-level defenses, isolation boundaries, and monitoring as part of the agentic architecture. — Principles 09 & 14

DigiCert debuts AI Trust Framework to secure agents, models and content. DigiCert releases an AI Trust Framework for attestation, identity, and content provenance across agents and models; outcome engineers should bake identity, signing, and governance hooks into pipelines to meet enterprise safety and audit requirements. — Principles 10 & 14

The playbook for your agentic SDLC. Luis Blando lays out practical SDLC patterns—define business KPIs, human-on-the-loop checkpoints, and audit gates—to govern agentic AI at scale; outcome engineers can adopt these patterns immediately to move agents from prototypes to measurable business outcomes. — Principles 15 & 16