Agent Ops: Sandboxes, Shared Brains, and KV Cache Transfers

Running AI agents in GitHub Actions with Docker Sandboxes runs AI coding agents inside disposable Docker sandboxes in GitHub Actions, giving agents full CI access while containing blast radius. This provides a repeatable pattern for safe, auditable agentic workflows and enforces Principle 07 (Build the Island) and Principle 15 (The Gate) in CI/CD.

Slack Code: Salesforce moves AI coding into a shared Slack workspace embeds coding agents into shared Slack channels so stakeholders co-author, guide, and sign off on AI-generated code in-context. Outcome engineers should treat this as a collaboration pattern that converts prompt-based outputs into auditable artifacts and human checkpoints — a practical application of Principle 03 (No More Single Player Mode).

OzBrain: a shared brain for knowledge between agents and your team centralizes team and agent knowledge into one writable, connector-based brain so every AI reads and updates the same single source of truth. This reduces context drift across agents and teams and directly supports Principle 11 (The Graph) by making agent context queryable and authoritative.

NVIDIA finds simple linear math can replace costly AI model handoffs demonstrates linear mapping to transfer KV caches across compatible LLMs, cutting recompute and latency while preserving up to 98% accuracy. For outcome systems that stitch multiple models together, this technique materially lowers runtime cost and makes multi-model orchestration and routing far more practical (Principle 09 and Principle 06).

Building an (almost) fully self-hosted, sandboxed, agentic software factory documents a self-hosted pipeline that autonomously builds, tests, and deploys apps from one prompt on a home server with strict network guardrails. Use this as an operational blueprint for running agentic delivery lanes under your control — combining sandboxing, least privilege, and automated CI checks to implement Principles 07 and 15.