The author describes a pattern of prototyping workflows with AI agents then refactoring into code-driven processes, using agents only for tasks that require human-like judgment. A security vulnerability alert system illustrates how webhooks filter and route high-priority issues, delegating owner identification to an agent and formatting alerts via a second agent for reliable Slack notifications.

• Prototyping a workflow fully inside an agent prompt (filtering, assigning, formatting all in one) broke down under load, letting high/medium severity alerts slip through undetected.
• The fix was refactoring deterministic steps (filtering, batching, data extraction) into plain code, reserving agents only for tasks needing human-like judgment, like matching people to code ownership.
• The resulting hybrid pipeline (code filters/batches → agent assigns owners via CODEOWNERS/commits → second agent formats Slack messages) runs reliably without spamming non-critical alerts.
• The author's general pattern—prototype in pure agent, then refactor toward code with only a few targeted agent calls—is framed as producing faster, cheaper, more reliable automation than prompt tinkering or building strict evals.