/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."
category: granular-workflow-bundle
risk: safe
source: personal
date_added: "2026-02-27"

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.