StewAI Blog · AI Workflow Design · 13 min read
How AI Agents Work, What a SKILL.md Is, and Where StewAI Draws the Line
A plain, technical explanation of how AI agents actually work, what a SKILL.md file is and how progressive disclosure loads it, where agents and skills genuinely shine, and the one job they should not have: high-volume, known, repeated work. Agents discover the process. Recipes preserve it.
Frequently asked questions
How does an AI agent actually work?
An agent is a loop around a language model that is allowed to choose its own next move. You give it a goal and a set of tools described by name and argument schema; the model reasons, emits a structured tool call, your runtime executes it and feeds the result back, and the loop repeats until the model decides the task is done. The defining trait is that the model, not your code, chooses the path at run time, which is why an agent differs in kind from a single call or a fixed pipeline.
What is a SKILL.md file?
A Skill (Anthropic's Agent Skills) is a folder centered on a SKILL.md file: YAML frontmatter with a name and description, then a Markdown body of instructions, plus optional bundled scripts and reference files. It uses progressive disclosure: by default only the name and description are in context, the instruction body loads when the model judges the skill relevant, and bundled files load on demand. Skills are model-invoked, portable across Claude apps, Claude Code, and the API, and composable.
When should you use an agent versus a deterministic recipe?
Use an agent for work you cannot specify in advance: exploratory, one-off, or high-variety tasks where discovery is the point and errors are cheap to recover. Use a recipe for known, high-volume, repeated work where you need the same path and output every run, inspection before execution, and statistical control at scale. A skill gives an agent knowledge on demand; a recipe gives the organization the process itself, frozen. Agents discover the process; recipes preserve it.
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