# AI Agent Development

> AI agent development workflow for building autonomous agents, multi-agent systems, and agent orchestration with CrewAI, LangGraph, and custom agents. Use when this capability is needed.

- Skill: `tomevault-io/ai-agent-development-9` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add tomevault-io/ai-agent-development-9`
- Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/ai-agent-development-9/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: tomevault-io (https://skillmd.com/u/tomevault-io)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/tomevault-io/ai-agent-development-9

---

@ 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

@ Limitations
- Use this skill only when the task clearly matches the scope described above.
- never 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.

---
> Source: [Regtransfers/agency-agents-mcp](https://github.com/Regtransfers/agency-agents-mcp) — distributed by [TomeVault](https://tomevault.io).
<!-- tomevault:4.0:skill_md:2026-06-15 -->