ai-agent-development – skill Agent & Tự động hóa cho agent AI

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.