Agent Development

Autonomous agents for Claude Code are built using YAML frontmatter and Markdown to define roles, triggering logic, and system prompts. Structure your agent with a description field containing `<example>` blocks, configure model and tool access, then output a CLAUDE.md-compliant markdown file. This enables the agent to handle multi-step tasks independently based on user requests or context.

Intelligent triggering relies on crafting a clear `description` field paired with concrete `<example>` blocks that show when the agent activates. These examples teach the agent to recognize user intents and context patterns. Combined with explicit decision logic in your system prompt, this ensures reliable activation and appropriate behavior for your agent's specific responsibilities.

Yes. Agent configuration includes specifying the model version, visual color identifier, and tool permissions within the YAML frontmatter. These customizable settings let you control agent capabilities, performance, and appearance, enabling precise behavior tuning for different tasks and use cases.

System prompts are central to agent development. They define the agent's role, decision logic, and behavioral constraints. Combined with YAML frontmatter structure, triggering examples, and tool configurations, system prompts ensure outputs satisfy CLAUDE.md compliance and produce predictable, autonomous behavior.

Agent development focuses on creating autonomous configurations that handle complex tasks independently using structured design, triggering logic, and system prompts. Plugin development is broader infrastructure work. Agents are specialized plugins designed for autonomous operation; use agent development when you need multi-step task handling with minimal human intervention.

Use agent development when you need proactive problem-solving, multi-step task automation, or consistent behavior across repeated scenarios. Agents excel at code review, testing, and analysis tasks that benefit from structured decision logic and triggering conditions. Direct interaction suits one-off requests; agents suit ongoing automation.