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ai-agent-development

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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

Source

git clone https://github.com/bcastelino/agent-skills-kit/blob/main/skills/ai-agent-development/SKILL.mdView on GitHub

Overview

AI Agent Development Workflow is a specialized approach for building autonomous agents, multi-agent systems, and orchestrating agent activities. It covers tool integration, memory patterns, and human-in-the-loop considerations, guiding design, implementation, and evaluation using CrewAI, LangGraph, and custom agents.

How This Skill Works

Developers progress through seven phases: Design, Single Agent Implementation, Multi-Agent System, Agent Orchestration, Tool Integration, Memory Systems, and Evaluation. Each phase lists the skills to invoke, concrete actions, and copy-paste prompts to accelerate delivery, plus a simple Agent Architecture diagram and Quality Gates such as memory functionality and orchestration tests. Agent Architecture: User Input -> Planner -> Agent -> Tools -> Memory -> Response.

When to Use It

  • Building autonomous AI agents
  • Creating multi-agent systems
  • Implementing agent orchestration
  • Adding tool integration to agents
  • Setting up agent memory

Quick Start

  1. Step 1: Define the agent purpose and select Phase 1 (Agent Design) to establish goals and success metrics.
  2. Step 2: Implement a single autonomous agent using Phase 2 (Single Agent Implementation) with the recommended patterns and tool integration.
  3. Step 3: Scale to a multi-agent system with Phase 3 (Multi-Agent System) and Phase 4 (Agent Orchestration), then test end-to-end.

Best Practices

  • Define the agent purpose and success metrics during Phase 1 to align design with business goals.
  • Choose the appropriate framework per phase (e.g., CrewAI for multi-agent systems, LangGraph for orchestration) to ensure smooth integration.
  • Plan tool interfaces and error handling in Phase 5, with clear memory design in Phase 6.
  • Test each phase with defined scenarios and edge cases, and validate against the evaluation criteria in Phase 7.
  • Document the agent architecture and keep quality gates (logic, tools, memory, orchestration, evaluation) checked throughout.

Example Use Cases

  • A single autonomous agent that schedules meetings by integrating calendar tools and natural language prompts.
  • A customer-support system with multiple agents coordinating to resolve complex requests.
  • A data-processing workflow where CrewAI assigns roles and delegates tasks among agents.
  • An orchestration graph using LangGraph to manage conditional branches and persistence across steps.
  • A memory-enabled agent that recalls user preferences across sessions to personalize interactions.

Frequently Asked Questions

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