agent-workflow

# Agent Workflow Designer

## Overview

This skill guides the design and architecture of AI agent workflows using proven methodologies. When a user presents a problem, this skill helps structure an agent-based solution following the 9-step building process and 8-layer architecture framework validated at Meta.

## Workflow Decision Tree

When a user shares a problem or requests agent design help:

1. **Assess the problem scope**
   - Is the problem clearly defined? → Proceed to Problem Analysis
   - Is the problem vague? → Ask clarifying questions about desired outcomes and constraints

2. **Determine architecture complexity**
   - Simple task (single action)? → Single agent with basic tools
   - Complex task (multiple sub-tasks)? → Consider multi-agent orchestration
   - Integration task (connecting systems)? → Focus on Layer 4 (Tooling) design

3. **Follow the appropriate workflow**
   - **New agent from scratch** → Apply 9-Step Building Process
   - **Existing agent improvement** → Focus on specific layers needing enhancement
   - **Tool integration problem** → Apply MCP and tooling patterns

## 9-Step Agent Building Process

Use this sequential workflow when designing a new agent from scratch:

### Step 1: Define Purpose and Scope

**Key principle:** Start with job-to-be-done, not technology.

Ask the user:
- What specific outcome does the end user need?
- What are the constraints (budget, time, resources)?
- What's the success metric?

**Bad scope example:**
"An AI assistant for customer service"

**Good scope example:**
"An agent that takes customer complaints, pulls order history from Shopify API, and drafts refund approvals for orders under $200"

**Decision point:** Narrow scope = better performance. Resist building Swiss Army knives.

### Step 2: Structure Inputs and Outputs

Treat the agent as a function with structured interfaces:

**Inputs:**
- Use JSON schemas or Pydantic models, not free text
- Define required vs. optional fields
- Specify data types a