AI agents aren’t failing. The coordination layer is failing. An “Event Spine” proposal centralizes ordering, context propagation, and coordination primitives to prevent multi-agent conflicts and scale orchestration. Outcome engineers must design event-driven coordination and clear context propagation to keep agents consistent and composable — Principle 09 (Orchestration) and 06 (Legible Landscapes).

Tiger Teams, Evals and Agents: The New AI Engineering Playbook. Mastra argues cross-functional Tiger Teams plus rigorous evals are essential to shipping agentic systems. If you’re building outcome engineering pipelines, embed continuous evals and tight cross-discipline feedback loops to move from prototype to production safely — Principle 03 and 16.

Advisor Strategy in Agents. The piece recommends using lightweight advisor models to call powerful LLMs only when needed, cutting inference costs while reserving frontier models for hard decisions. Apply advisor layers in your agent stack to balance cost, latency, and auditability while keeping high-stakes reasoning gated and observable — Principle 09 and 12.

Launch HN: Twill.ai (YC S25) — Delegate to cloud agents, get back PRs. Twill runs sandboxed coding agents that build, test, and open PRs, pinging humans only for approvals. Use sandboxed agent runners and CI-style gating to automate developer workflows safely and preserve human oversight in delivery pipelines — Principle 07 and 08.

I Still Prefer MCP Over Skills. The author argues MCP connectors outperform Skills for giving LLMs real, remote, sandboxed service access and seamless integrations. Choose connector architectures that provide secure, observable service access and enforce boundaries when you instrument agents into production systems — Principle 06 and 07.