AI agent workflow

Definition

An AI agent workflow is a repeatable way to move from intent to inspected output with memory, checkpoints, and human review.

Overview

The useful unit is not a single prompt. It is a loop: give the agent context, let it act, inspect the result, preserve what matters, and turn the learning into the next run.

Principles

• Start from the repository or workspace reality, not from an abstract prompt.
• Make the agent show its work through files, tests, diffs, and small status updates.
• Save reusable decisions in project docs, changelogs, or structured data.
• Keep the human in charge of taste, risk, and final judgment.

Checklist

1. Define the concrete output before starting.
2. Read the existing project instructions and current git state.
3. Work in small verified steps rather than one giant generation.
4. Run checks that match the risk: typecheck, build, tests, or visual review.
5. Record the useful learning where the next session can find it.

Examples

• Turning a website feature request into code, build verification, and a changelog entry.
• Converting a research question into a structured page with FAQ and internal links.
• Using an agent to inspect a local workflow and produce repeatable steps.

Next steps

Add concrete case studies from this site build: language routing, SEO structure, and content expansion.

FAQ