AI Agent Development

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

AGI Framework Integration

Adapted for @techwavedev/agi-agent-kit Original source: antigravity-awesome-skills

Memory-First Protocol

Cache workflow configurations and automation patterns. Retrieve prior pipeline designs to avoid re-building similar flows from scratch.

# Check for prior workflow/automation context before starting
python3 execution/memory_manager.py auto --query "automation patterns and workflow configurations for Ai Agent Development"

Storing Results

After completing work, store workflow/automation decisions for future sessions:

python3 execution/memory_manager.py store \
  --content "Workflow: automated data pipeline with retry logic, dead-letter queue, and Slack alerts on failure" \
  --type technical --project <project> \
  --tags ai-agent-development workflow

Multi-Agent Collaboration

Share workflow state with other agents so they can trigger, monitor, or extend the automation.

python3 execution/cross_agent_context.py store \
  --agent "<your-agent>" \
  --action "Workflow automation deployed — pipeline processing 1000+ events/day with 99.9% success rate" \
  --project <project>

Playbook Engine

Combine this skill with others using the Playbook Engine (execution/workflow_engine.py) for guided multi-step automation with progress tracking.