Claude Code Agents: Engineering Autonomous AI Assistants

Problem: Claude Code conversations become unwieldy when juggling multiple roles - reviewer, analyst, writer, and coder all in one session. Context gets muddy and responses lose focus.

Quick Win: Create your first specialized agent in 2 minutes:

---
name: Code Reviewer (CR)
description: Security and performance code review specialist
tools: Read, Grep, Edit
---
 
You are a focused code reviewer. When invoked:
1. Scan for security vulnerabilities
2. Identify performance bottlenecks  
3. Suggest specific improvements
4. Flag potential bugs

Save as code-reviewer.md in your .claude/agents/ folder. Now run: ask cr to review the authentication logic

Understanding: Agents transform Claude Code from a single assistant into a specialized team. Each agent has isolated context, specific tools, and focused expertise.

The Agent Evolution: From Manual to Automatic

We've evolved through three eras: Prompt Engineering (crafting prompts), Context Engineering (optimizing CLAUDE.md files), and Agent Engineering (specialized assistants).

Custom agents enable automatic delegation. Instead of describing what you want Claude to become, you activate pre-configured specialists who know exactly what to do.

Agent Types: Understanding Your Options

Custom agents deliver the sweet spot: automatic activation, surgical tool selection, and zero context pollution. They work across projects and activate based on task description matching.

When Agents Excel: The Perfect Use Cases

Specialized Analysis Tasks:

• Security reviews and vulnerability scanning
• Performance optimization and bottleneck identification
• Code quality assessment and best practice enforcement
• UX evaluation and accessibility auditing

Cross-Project Workflows:

• Documentation standardization
• Team coding standards enforcement
• Research tasks requiring specific methodologies

Unlike sub-agents tied to specific projects, custom agents work everywhere.

Agent Performance: Token Efficiency Matters

Every agent invocation carries initialization costs. Smart design minimizes these costs while preserving capability:

Agent Weight Classifications:

• Lightweight (under 3k tokens): Maximum composability, frequent use
• Medium-weight (10-15k tokens): Balanced complexity and efficiency
• Heavy (25k+ tokens): Complex analysis, occasional use

Model Selection Strategy:

• Haiku + Lightweight: Simple tasks, minimal cost
• Sonnet + Medium: Balanced reasoning and speed
• Opus + Heavy: Complex analysis requiring deep reasoning

Test unconventional combinations - simple agents with Opus might unlock surprising depth.

Essential Agent Features

No Context Pollution: Agents don't inherit your project's CLAUDE.md configuration. They start clean every time, ensuring consistent behavior.

Visual Distinction: Agents display custom colors in the terminal, making it easy to track which specialist is responding.

Smart Activation: Configure descriptions that trigger automatic delegation. Include phrases like "use PROACTIVELY" to improve reliability.

Nickname Efficiency: Set short aliases for frequent agents:

ask A1, P2, C1 to review the changes
# Activates UX, Performance, and Code Review agents

Design Principles for Effective Agents

Start Lightweight: Begin with focused, single-purpose agents using minimal tools. Optimize for composability before adding complexity.

Token-First Design: Engineer initialization costs carefully. Lightweight agents get used more frequently due to subscription constraints.

Clear Purpose: Write specific descriptions that match the tasks you want automated. Test reliability by seeing how often Claude invokes the right agent automatically.

Tool Selection: Grant only necessary tools initially. Every tool increases initialization cost.

Building Your First Agent

Start with one specific problem you encounter regularly. Create an agent that handles that exact workflow.

Test its effectiveness by using it for a week. Refine the description based on how reliably Claude activates it automatically. Master single agents before exploring agent orchestration.

Next Action: Create your first agent solving a problem you face daily, then explore custom agent tactics to enhance its capabilities.

The future belongs to agent-first product design - software engineered for AI specialists to extend, customize, and scale. Master these fundamentals and you're building toward that paradigm shift.