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

• Define agent purpose

• Design agent capabilities

• Plan tool integration

• Design memory system

• 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

• Choose agent framework

• Implement agent logic

• Add tool integration

• Configure memory

• 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

• Define agent roles

• Set up agent communication

• Configure orchestration

• Implement task delegation

• 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

• Design workflow graph

• Implement state management

• Add conditional branches

• Configure persistence

• 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

• Identify tool needs

• Design tool interfaces

• Implement tools

• Add error handling

• 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

• Design memory structure

• Implement short-term memory

• Set up long-term memory

• Add entity memory

• 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

• Define evaluation criteria

• Create test scenarios

• Measure agent performance

• Test edge cases

• 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.