ai-agent-development — Библиотека навыков для разработки AI агентов

--- 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 ## 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