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All digests tagged CI/CD Automation
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Loop engineering designs systems where AI agents iteratively prompt, observe, and adjust toward a measurable goal. This process requires managing context, tools (MCP/APIs), and persistent state (spine).
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Robert Brennan details four strategies for sandboxing AI coding agents: Docker, GitHub Actions, VMs, and Kubernetes. He argues that Kubernetes offers the best isolation and scale for autonomous agents.
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Suraj Gupta details three ways software factories self-improve: using outer-loop agents for skill refinement, implementing persistent memory for traceable facts, and optimizing costs via model routing. Learn how to build self-improving agentic workflows.
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Learn how Context Engines solve AI agent limitations by ingesting data from Slack, Notion, and GitHub history. Discover how to build truly intelligent, context-aware development tools.
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Instead of massive 40,000 line PRs, adopting control theory loops (Sensor $\rightarrow$ Controller $\rightarrow$ Actuator) is key for building robust, verifiable code in complex systems.
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