The 100 Ways to Extend AI Agents (Well, More Than Zero)

2026-09-26T00:00:00+12:00

I lied. This article doesn’t show you 100 ways to extend AI agents. But there are definitely more ways than most people realize. Here are the ones I’ve discovered.

1. AGENTS.md

Harnesses auto-load this file when a session starts. It’s where you can change agent behavior by writing markdown documentation. Claude Code previously only supported CLAUDE.md, but they’ve recently added AGENTS.md support. (Thanks Shopify CEO Tobi.)

2. Skills

The LLM receives the name and description from skill frontmatter in your installed SKILL.md files—either globally or in the project directory (.agents/skills, ./skills). The LLM progressively requests full skill content as needed, revealing more of the skill in the prompt dynamically.

3. Prompt Templates

Save commonly used prompts as templates and invoke them like /review #8 to review PR #8. This lets you template complex workflows without rebuilding them each time.

4. Tools and MCP

MCP is a protocol supported by most AI agents. It extends agent capabilities to do more—reading and writing files locally, interacting with external systems, and automating tasks that would otherwise be manual.

5. Extensions and Plugins

Some agents support extensions or plugins at runtime (like Pi). These are agent-specific and only work with that particular agent.

6. Hooks

Define hooks before running tool calls or commands to intercept and change behavior. RTK is a great example—it intercepts tool calls for token efficiency.

7. Subagents

Write your own agents or invoke another AI agent as a subagent within the current session. The current agent typically treats it as a tool call or subprocess.

8. Model Routing

Route certain tasks to different models based on complexity. Decisions go to Jev, advanced tasks to Claude Fable, simple edits to Haiku. This optimizes cost and performance across different problem classes.

Ju Lin's AI Weblog: Tooling