Agent Design

Skill for designing high-performance AI agents following 2025 patterns.

Documentation

• patterns.md - Multi-agent architecture patterns
• workflows.md - Recommended workflows

Fundamental Distinction

Workflows vs Agents

| Type | Control | When to use | |------|---------|-------------| | Workflow | Code orchestrates LLM | Predictable tasks, need for control | | Agent | LLM directs its actions | Flexibility, adaptive decisions |

Golden rule: Start simple, add complexity if necessary.

Agent Architecture

Minimal Structure

yaml
Agent:
  identity: Who am I?
  capabilities: What can I do?
  tools: What tools do I have?
  constraints: What are my limits?
  workflow: How should I proceed?

Complete Structure (Production)

markdown
---
name: my-agent
description: Short description
model: sonnet|opus
tools: [list of tools]
skills: [associated skills]
---

# Identity
[Who the agent is]

# Capabilities
[What it can do]

# Workflow
[Steps to follow]

# Tools
[How to use each tool]

# Constraints
[Limits and rules]

# Examples
[Use cases]

# Forbidden
[What it must NEVER do]

Agent Patterns

1. Single Agent (Simple)

User → Agent → Response

Usage: Simple tasks, rapid prototyping.

2. Agent + Tools

User → Agent ↔ Tools → Response
                ↑
            Tool Results

Usage: Tasks requiring external access (API, files, DB).

3. Orchestrator + Subagents

User → Orchestrator → Subagent 1 (specialized)
                   → Subagent 2 (specialized)
                   → Subagent 3 (specialized)
                   ↓
              Synthesis → Response

Usage: Complex tasks, separation of responsibilities.

4. Sequential Pipeline

User → Agent 1 → Agent 2 → Agent 3 → Response
       (Analyze)  (Plan)    (Execute)

Usage: Linear processes (e.g., Analyst → Architect → Developer).

Fresh Eyes Principle

Key 2025 concept: Each sub-agent must have a "fresh" context.

❌ Bad: Pass entire history to each sub-agent
✅ Good: Give only necessary information

Orchestrator:
  - Keeps complete history
  - Extracts relevant context for each sub-agent
  - Synthesizes results

Design Checklist

Before creating an agent

• ] Is the objective clear?
• ] Would a simple workflow suffice?
• ] What tools are needed?
• ] What guardrails are required?

During design

• ] Is identity well defined?
• ] Is workflow explicit?
• ] Are error cases handled?
• ] Are examples relevant?

After creation

• ] Standard case tests?
• ] Edge case tests?
• ] Security tests (jailbreak)?
• ] Acceptable performance?

Claude Code Agent Template

markdown
---
name: [kebab-case-name]
description: [1-2 lines max]
model: sonnet
color: blue
tools: Read, Edit, Write, Bash, Grep, Glob
skills: [associated-skills]
---

# [Agent Name]

[Purpose description]

## Core Principles

1. **[Principle 1]**: [Short explanation]
2. **[Principle 2]**: [Short explanation]

## Workflow (MANDATORY)

### Phase 1: [Name]

Numbered actions]

### Phase 2: [Name]

Numbered actions]

## Output Format

[Response structure]

## Forbidden

- [Prohibition 1]
- [Prohibition 2]

Anti-Patterns to Avoid

❌ Omniscient agent

You know everything and can do anything.

✅ Specialized agent

You are an expert in [specific domain].
For topics outside your domain, redirect to the appropriate agent.

❌ Implicit instructions

Do what's logical.

✅ Explicit instructions

Step 1: Analyze the problem
Step 2: Propose 3 solutions
Step 3: Recommend the best with justification

❌ No error handling

Execute the task.

✅ Explicit error handling

IF the task fails:
  1. Identify the cause
  2. Propose an alternative
  3. Ask for confirmation before retrying

Forbidden

• Never create an agent without explicit workflow
• Never give access to all tools without necessity
• Never ignore the principle of least privilege
• Never forget security guardrails