Agents

Agents are specialized AI personas, each with its own system prompt, capabilities, and focus area. They go beyond simple chat with tool access, sub-agent spawning, and budget management.

Built-in Agents

🔬 Analyst

Understanding problems, research, specs, and requirements analysis.

🏗️ Architect

Technical decisions, API design, system architecture.

💻 Developer

Writing code, debugging, refactoring, implementation.

🔍 Reviewer

Code review, security audit, best practices compliance.

🧪 Tester

Test generation, verification, coverage analysis.

📊 Manager

Task coordination, sprint planning, estimation.

📚 Researcher

Knowledge gathering, technology evaluation, deep research.

Execution Modes

Full execution with file and bash access (uses Claude Code CLI or AI SDK):

Spawn the architect agent to design a caching layer for our API.
Consider Redis vs in-memory caching.

LLM-only execution — fast but no file/bash access. Great for analysis:

Run a quick agent (analyst) to compare REST vs GraphQL for our use case.

Run multiple agents concurrently (budget split equally):

Run reviewer and developer agents in parallel:
- Reviewer: audit the auth module for security issues
- Developer: fix any issues found

Custom Agents

Agent definitions are Markdown files stored in the agents/ directory:

agents/database-expert.md

# Database Expert

You are a senior database engineer specializing in PostgreSQL and Drizzle ORM.

## Expertise

- Query optimization and indexing strategies
- Schema design and migrations
- Connection pooling and performance tuning

## Rules

- Always suggest adding indexes for frequently queried columns
- Prefer Drizzle ORM patterns over raw SQL
- Always include .limit() on SELECT queries

Use reload_agents to hot-reload agent definitions after editing files on disk.

Orchestration

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Budget Management

Aspect	Description
Per-run budget	Configurable limit per agent execution
Inherited budget	Sub-agents share the parent's remaining budget
Usage reporting	View token usage via `agent_budget` tool