EvanFlow — A TDD-driven feedback loop for Claude Code runs TDD-driven, human-gated agent loops that enforce test-backed code, parallel coder/overseer teams, and strict anti-hallucination guardrails. This provides a concrete pattern for agent CI: human gates, enforced tests, and anti-hallucination checks you can fold into orchestration and governance — Principle 09 and 15.

YourMemory — AI memory with biological decay implements persistent agent memory with decay to improve multi-session recall and make MCP integration straightforward. Outcome engineers can use decaying memories to limit context bloat, prioritize relevance across sessions, and stabilize agent behavior graphs — Principle 11 and 06.

Google begins putting the guardrails on agentic AI shifts focus from demos to containment by launching governance, auditing, and grounding tools for enterprise agents. That means the agent control plane — audit trails, policy enforcement, and grounding — is becoming a first-class platform concern for production deployments — Principle 10 and 14.

Context decay, orchestration drift, and the rise of silent failures in AI systems warns that silent failures from decaying context and orchestration drift are becoming common and calls for behavioral telemetry and new observability. Outcome engineers must instrument behavioral telemetry and drift detectors and build refresh + validation loops into orchestration to catch silent failures early — Principle 14 and 06.

Inside Andon Market — the first retail boutique run by an AI agent reports a Claude Sonnet 4.6 agent now runs a San Francisco retail store, handling customer service and operations. This live deployment surfaces practical needs — grounding with real-world sensors, clear escalation paths, and orchestration patterns for noisy physical contexts — Principle 03 and 09.