ai-agent-development

name	ai-agent-development
description	AI agent development workflow for building autonomous agents, multi-agent systems, and agent orchestration with CrewAI, LangGraph, and custom agents. Use when designing or implementing AI agents.

AI Agent Development Workflow

Overview

Specialized workflow for building AI agents including single autonomous agents, multi-agent systems, agent orchestration, tool integration, and human-in-the-loop patterns.

When to Use This Workflow

Use this workflow when:

• Building autonomous AI agents
• Creating multi-agent systems
• Implementing agent orchestration
• Adding tool integration to agents
• Setting up agent memory

Workflow Phases

Phase 1: Agent Design

Skills to Invoke

• ai-agents-architect - Agent architecture
• autonomous-agents - Autonomous patterns

Actions

1. Define agent purpose
2. Design agent capabilities
3. Plan tool integration
4. Design memory system
5. Define success metrics

Copy-Paste Prompts

Use @ai-agents-architect to design AI agent architecture

Phase 2: Single Agent Implementation

Skills to Invoke

• autonomous-agent-patterns - Agent patterns
• autonomous-agents - Autonomous agents

Actions

1. Choose agent framework
2. Implement agent logic
3. Add tool integration
4. Configure memory
5. Test agent behavior

Copy-Paste Prompts

Use @autonomous-agent-patterns to implement single agent

Phase 3: Multi-Agent System

Skills to Invoke

• crewai - CrewAI framework
• multi-agent-patterns - Multi-agent patterns

Actions

1. Define agent roles
2. Set up agent communication
3. Configure orchestration
4. Implement task delegation
5. Test coordination

Copy-Paste Prompts

Use @crewai to build multi-agent system with roles

Phase 4: Agent Orchestration

Skills to Invoke

• langgraph - LangGraph orchestration
• workflow-orchestration-patterns - Orchestration

Actions

1. Design workflow graph
2. Implement state management
3. Add conditional branches
4. Configure persistence
5. Test workflows

Copy-Paste Prompts

Use @langgraph to create stateful agent workflows

Phase 5: Tool Integration

Skills to Invoke

• agent-tool-builder - Tool building
• tool-design - Tool design

Actions

1. Identify tool needs
2. Design tool interfaces
3. Implement tools
4. Add error handling
5. Test tool usage

Copy-Paste Prompts

Use @agent-tool-builder to create agent tools

Phase 6: Memory Systems

Skills to Invoke

• agent-memory-systems - Memory architecture
• conversation-memory - Conversation memory

Actions

1. Design memory structure
2. Implement short-term memory
3. Set up long-term memory
4. Add entity memory
5. Test memory retrieval

Copy-Paste Prompts

Use @agent-memory-systems to implement agent memory

Phase 7: Evaluation

Skills to Invoke

• agent-evaluation - Agent evaluation
• evaluation - AI evaluation

Actions

1. Define evaluation criteria
2. Create test scenarios
3. Measure agent performance
4. Test edge cases
5. Iterate improvements

Copy-Paste Prompts

Use @agent-evaluation to evaluate agent performance

Agent Architecture

User Input -> Planner -> Agent -> Tools -> Memory -> Response
              |          |        |        |
         Decompose   LLM Core  Actions  Short/Long-term

Quality Gates

• Agent logic working
• Tools integrated
• Memory functional
• Orchestration tested
• Evaluation passing

Related Workflow Bundles

• ai-ml - AI/ML development
• rag-implementation - RAG systems
• workflow-automation - Workflow patterns