This architectural exploration looks at building software systems that combine predictable, deterministic scripts with probabilistic LLM tasks. The author structures complex workflows using specialized AI worker roles with distinct departmental responsibilities. The piece highlights how managing context boundaries becomes the primary scaling bottleneck in agentic systems, offering strategies like task-driven development and dedicated environment tools to help agents retrieve relevant historical context without polluting execution bounds.
Naive automated self-healing mechanisms can easily worsen system outages, as demonstrated when a downstream dependency failure caused a production service to restart 847 times in four hours. To address runaway recovery loops, this article presents a three-tier escalating self-healing architecture implemented in Jarvis. The design handles operational failures through progressive remediation steps rather than immediate, aggressive service restarts that exacerbate infrastructure strain. For systems architects and backend engineers operating microservices or Kubernetes clusters, understanding how to construct bounded, escalating self-healing patterns is essential for building resilient distributed infrastructure that recovers gracefully without compounding system load.