Agent Ops: Governance, Orchestration, and Real-World Limits

Google tethers Antigravity to enterprise controls amid AI shakeup. Google folds Antigravity into Gemini Enterprise with centralized governance, audit logging, IDE extensions, and token-based billing controls. Outcome engineers must map these controls into runtime policies, audit trails, and IDE hooks so agents run with governed permissions and traceability (Principles 10 & 15).

Claude Opus 5 scored 30% on ARC-AGI-3; wrapped in Nvidia’s AVO, it hit 100%. Nvidia’s AVO orchestration layer converts a weak model baseline into full long-horizon autonomy, proving system architecture — not just model quality — determines agent success. Build orchestration, retrievers, and robust evaluation loops as first-class infrastructure to achieve reliable outcome delivery (Principles 09 & 16).

NVIDIA finds simple linear math can replace costly AI model handoffs. Nvidia demonstrates linear mapping of KV caches across compatible LLMs, cutting recompute and latency while retaining ~98% of context fidelity. Outcome engineers can use KV-transfer strategies to stitch heterogeneous models into multi-model pipelines with far lower cost and faster context handoffs (Principles 06 & 11).

Running AI agents in GitHub Actions with Docker Sandboxes. The pattern runs coding agents inside disposable Docker sandboxes in CI, granting full repo and test access while containing blast radius. Adopt disposable execution environments and CI-integrated sandboxes so agents can perform real tasks safely, with credential and network guardrails (Principles 07 & 15).

Most coding agent benchmarks skip large-scale refactoring. Not this one.. SWE-Bench ProMax exposes that leading coding agents fail large-scale refactors — top model only resolves ~41.2% of tasks. Outcome engineers must audit agent outputs against realistic refactor benchmarks and insert validation gates, rollback paths, and human-in-the-loop checkpoints before authorizing autonomous code changes (Principles 16 & 02).