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ai-agent-development

Plan and build AI agents through selected stages of design, implementation, tool integration, memory, and evaluation. The workflow points to specialized skills for single agents, coordinated agent teams, and stateful workflows using CrewAI or LangGraph.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

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AI agent development workflow for building autonomous agents, multi-agent systems, and agent orchestration with CrewAI, LangGraph, and custom agents.

설치 방법 보기

이럴 때 유용합니다

  • Designing an autonomous AI agent
  • Building a team of agents with distinct roles
  • Creating stateful workflows with LangGraph
  • Adding tools and memory to an agent
  • Planning agent evaluations and test scenarios

포함된 파일(1)

  • SKILL.md4.6 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

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

Scope and execution

Choose the phases that match the user’s task; this document is an index, not an instruction to run every listed skill. Read a selected skill before using it. Respect repository and user constraints. Cloud calls, host configuration, publishing, destructive operations and spending require the applicable authorization; a workflow checklist does not grant it.

Multi-agent phases describe the product being built. They do not authorize the assistant to spawn or delegate to agents during this task. Use an existing single-agent architecture when appropriate and record evaluation inputs, observed results and limitations.

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

Limitations

  • Use this skill only when the task clearly matches the scope described above.
  • Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
  • Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.

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