AI Automation

Workflows that ship. Pipelines, scrapers, agents glued to APIs, and the operational discipline that keeps them running past the demo.

This pillar is for builders putting AI into production loops, not just chat windows. Expect coverage of orchestration patterns, queueing, retries, idempotency, observability, and the unglamorous plumbing that separates a working agent from one that fails silently overnight. The summaries below are filed when they explain a real production decision: which framework lasted, which one rotted, which prompt-routing trick survived contact with edge cases, and which tool calls turned into incidents.

Themes that recur across the channel: structured output and JSON mode reliability; multi-step tool-use traces and the cost of a long context window; integration with email, calendar, CRM, and database systems; long-running jobs and the trade-offs between background workers and serverless functions; eval-driven iteration on agent behavior. The work that holds up tends to look more like systems engineering than prompt engineering.

If you operate AI workflows at any scale, this is the channel to start with. Pair it with AI and LLMs for the model-layer context behind the production tactics.