AI Methodologies: Agents do the Work

A Practical Method: Asynchronous Agent Runs With Review Gates

In many cases, agents are most useful when they can produce a first draft without a human in the chat loop the whole time. Treat these runs like any other automated system: define the inputs, set constraints, execute in a controlled environment, and require verification before changes land. The goal isn’t autonomy for its own sake, it’s predictable progress with clear accountability.

Habits:

• Define prompt inputs as structured artifacts (issue template, ticket fields, PRD snippets) so runs start from stable, reviewable context.
• Use explicit triggers (webhook, schedule, system event) and log what triggered the run and why.
• Constrain the environment: least-privilege credentials, scoped repos/services, resource caps, and a clear “no prod writes by default” boundary.
• Require evidence: change artifacts, checks executed, commands run, and links to logs so reviewers can verify quickly.
• Route output through review (change requests or approvals) and keep humans accountable for the final decision.
• Iterate the checklist after each run to tighten constraints and reduce repeated failures.